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Merge pull request #95 from modelstudioai/feat/bailian-docs-update

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chore: update bailian-docs-llm-wiki (2026-08-28)
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gujieye
2026-08-28 04:10:20 +08:00
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parent 7666511f24 28b02cbe0f
commit 93ef162700
62 changed files with 3584 additions and 3309 deletions
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skills/bailian-docs-llm-wiki/llms.txt
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skills/bailian-docs-llm-wiki/models/families.jsonl
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@@ -36,7 +36,7 @@
{"slug":"image-instance-segmentation","name":"人物实例分割","description":"人物实例分割运用了检测和分割技术，不仅能够在图像中识别出不同的对象，而且还能准确地画出每一个对象边界的像素级掩码（mask）。","primaryCapability":"IG","capabilities":["IG"],"providers":["qwen"],"itemCount":1,"items":[{"model":"image-instance-segmentation","name":"人物实例分割","capabilities":["IG"]}],"detailPath":"groups/image-instance-segmentation.json"}
{"slug":"image-out-painting","name":"图像画面扩展","description":"图像画面大模型，对输入图像进行画面自由扩展，支持旋转画面，支持按照扩展系数和扩展像素数两种方式进行扩图。用户可以通过指定宽度、高度画面扩展比例或者左、右、上、下的扩展的像素值来控制画面扩展，可用于创意娱乐、辅助作图、画面设计、影视后期制作等场景。","primaryCapability":"IG","capabilities":["IG"],"providers":["qwen"],"itemCount":1,"items":[{"model":"image-out-painting","name":"图像画面扩展","capabilities":["IG"]}],"detailPath":"groups/image-out-painting.json"}
{"slug":"kimi-models-market-place","name":"Kimi","description":"由月之暗面提供的Kimi系列模型的API服务。","primaryCapability":"TG","capabilities":["TG","VU","Reasoning"],"providers":["moonshot-ai"],"itemCount":5,"items":[{"model":"kimi/kimi-k2.5","name":"Kimi/Kimi K2.5","contextWindow":262144,"capabilities":["TG","Reasoning","VU"]},{"model":"kimi/kimi-k2.6","name":"Kimi/Kimi K2.6","contextWindow":262144,"capabilities":["TG","Reasoning","VU"]},{"model":"kimi/kimi-k2.7-code","name":"kimi/kimi-k2.7-code","contextWindow":262144,"capabilities":["TG","VU","Reasoning"]},{"model":"kimi/kimi-k2.7-code-highspeed","name":"kimi/kimi-k2.7-code-highspeed","contextWindow":262144,"capabilities":["TG","Reasoning","VU"]},{"model":"kimi/kimi-k3","name":"kimi/kimi-k3","contextWindow":1048576,"capabilities":["TG","VU","Reasoning"]}],"detailPath":"groups/kimi-models-market-place.json","maxContextWindow":1048576}
{"slug":"kling-models-market-place","name":"可灵AI","description":"由可灵AI提供的高质量视频与图像生成及编辑模型。","primaryCapability":"VG","capabilities":["VG","IG"],"providers":["kling"],"itemCount":4,"items":[{"model":"kling/kling-v3-image-generation","name":"Kling Image 3.0","capabilities":["IG"]},{"model":"kling/kling-v3-omni-image-generation","name":"Kling Image 3.0 Omni","capabilities":["IG"]},{"model":"kling/kling-v3-omni-video-generation","name":"Kling Video 3.0 Omni","capabilities":["VG"]},{"model":"kling/kling-v3-video-generation","name":"Kling Video 3.0","capabilities":["VG"]}],"detailPath":"groups/kling-models-market-place.json"}
{"slug":"kling-models-market-place","name":"可灵AI","description":"由可灵AI提供的高质量视频与图像生成及编辑模型。","primaryCapability":"VG","capabilities":["VG","IG"],"providers":["kling"],"itemCount":5,"items":[{"model":"kling/kling-v3-image-generation","name":"Kling Image 3.0","capabilities":["IG"]},{"model":"kling/kling-v3-omni-image-generation","name":"Kling Image 3.0 Omni","capabilities":["IG"]},{"model":"kling/kling-v3-omni-video-generation","name":"Kling Video 3.0 Omni","capabilities":["VG"]},{"model":"kling/kling-v3-turbo-video-generation","name":"Kling Video 3.0 Turbo","capabilities":["VG"]},{"model":"kling/kling-v3-video-generation","name":"Kling Video 3.0","capabilities":["VG"]}],"detailPath":"groups/kling-models-market-place.json"}
{"slug":"liveportrait-detect","name":"灵动人像LivePortrait-detect","description":"LivePortrait-detect是辅助LivePortrait的图像检测模型，用于检测图片中的人物形象是否符合视频生成要求。","primaryCapability":"VG","capabilities":["VG"],"providers":["qwen"],"itemCount":1,"items":[{"model":"liveportrait-detect","name":"灵动人像LivePortrait-detect","capabilities":["VG"]}],"detailPath":"groups/liveportrait-detect.json"}
{"slug":"liveportrait","name":"灵动人像LivePortrait","description":"LivePortrait是一款视频生成模型，可基于人物图片生成轻量化的人物肖像动态视频。","primaryCapability":"VG","capabilities":["VG"],"providers":["qwen"],"itemCount":1,"items":[{"model":"liveportrait","name":"灵动人像LivePortrait","capabilities":["VG"]}],"detailPath":"groups/liveportrait.json"}
{"slug":"minimax-models-market-place","name":"MiniMax文本模型","description":"由MiniMax提供的MiniMax-M系列文本模型API服务。","primaryCapability":"TG","capabilities":["TG","Reasoning","VU"],"providers":["mini-max"],"itemCount":4,"items":[{"model":"MiniMax/MiniMax-M2.1","name":"MiniMax/MiniMax-M2.1","contextWindow":204800,"capabilities":["TG","Reasoning"]},{"model":"MiniMax/MiniMax-M2.5","name":"MiniMax/MiniMax-M2.5","contextWindow":204800,"capabilities":["TG","Reasoning"]},{"model":"MiniMax/MiniMax-M2.7","name":"MiniMax/MiniMax-M2.7","contextWindow":204800,"capabilities":["TG","Reasoning"]},{"model":"MiniMax/MiniMax-M3","name":"MiniMax/MiniMax-M3","contextWindow":1048576,"capabilities":["TG","Reasoning","VU"]}],"detailPath":"groups/minimax-models-market-place.json","maxContextWindow":1048576}
@@ -61,7 +61,6 @@
{"slug":"qwen-audio-realtime-flash","name":"Qwen-Audio-Realtime-Flash","description":"Qwen-Audio-Realtime 是一款登顶全球权威评测的下一代实时双工语音大模型，模型兼顾了模型智商与双工对话节奏,在保持流畅、自然的实时交互体验的同时,语音推理能力不打折扣;并通过并行推理和全向流式等工程优化,将端到端响应时延控制在低水平,实现\"又快又聪明\"的对话体验。Flash版更注重极致的响应速度","primaryCapability":"Realtime-Chatting","capabilities":["Realtime-Chatting"],"providers":["qwen"],"itemCount":1,"items":[{"model":"qwen-audio-3.0-realtime-flash","name":"千问实时语音对话大模型3.0（极速版）","contextWindow":40960,"capabilities":["Realtime-Chatting"]}],"detailPath":"groups/qwen-audio-realtime-flash.json","maxContextWindow":40960}
{"slug":"qwen-audio-realtime-plus","name":"Qwen-Audio-Realtime-Plus","description":"Qwen-Audio-Realtime 是一款登顶全球权威评测的下一代实时双工语音大模型，模型兼顾了模型智商与双工对话节奏,在保持流畅、自然的实时交互体验的同时,语音推理能力不打折扣;并通过并行推理和全向流式等工程优化,将端到端响应时延控制在低水平,实现\"又快又聪明\"的对话体验。Plus版本更注重高质量的回复结果。","primaryCapability":"Realtime-Chatting","capabilities":["Realtime-Chatting"],"providers":["qwen"],"itemCount":1,"items":[{"model":"qwen-audio-3.0-realtime-plus","name":"千问实时语音大模型 （标准版）","contextWindow":40960,"capabilities":["Realtime-Chatting"]}],"detailPath":"groups/qwen-audio-realtime-plus.json","maxContextWindow":40960}
{"slug":"qwen-audio-tts","name":"Qwen-Audio-TTS","description":"Qwen-Audio-TTS是一款面向实时交互场景和高质量语音生成场景的语音合成大模型。模型支持多种小语种和中文方言，并具有Free-style 指令遵循能力、Context能力和细粒度标签控制能力，可更灵活地控制情绪、语气、角色、语速、音量等表达方式。","primaryCapability":"Realtime-Text-to-Speech","capabilities":["Realtime-Text-to-Speech","TTS"],"providers":["qwen"],"itemCount":2,"items":[{"model":"qwen-audio-3.0-tts-flash","name":"Qwen-Audio-3.0-TTS-Flash","capabilities":["Realtime-Text-to-Speech"]},{"model":"qwen-audio-3.0-tts-plus","name":"Qwen-Audio-3.0-TTS-Plus","capabilities":["Realtime-Text-to-Speech","TTS"]}],"detailPath":"groups/qwen-audio-tts.json"}
{"slug":"qwen-coder-plus","name":"Qwen-Coder-Plus","description":"千问系列代码及编程模型是专门用于编程和代码生成的语言模型，性能出色，效果突出。","primaryCapability":"TG","capabilities":["TG"],"providers":["qwen-domain-model"],"itemCount":1,"items":[{"model":"qwen-coder-plus","name":"Qwen-Coder-Plus","contextWindow":131072,"capabilities":["TG"]}],"detailPath":"groups/qwen-coder-plus.json","maxContextWindow":131072}
{"slug":"qwen-coder-turbo","name":"Qwen-Coder-Turbo","description":"Qwen-Coder-Turbo模型是专门用于编程和代码生成的语言模型，推理速度快，成本低。","primaryCapability":"TG","capabilities":["TG"],"providers":["qwen-domain-model"],"itemCount":1,"items":[{"model":"qwen-coder-turbo","name":"Qwen-Coder-Turbo","contextWindow":131072,"capabilities":["TG"]}],"detailPath":"groups/qwen-coder-turbo.json","maxContextWindow":131072}
{"slug":"qwen-deep-research","name":"qwen-deep-research","description":"千问深入研究是一款面向复杂研究任务的高级智能体系统，具备多轮推理与全局规划能力，能够运用互联网搜索等多种工具，对任务进行精细化拆解，开展推理与分析，最终为用户生成可溯源、逻辑严谨的研究型报告。","primaryCapability":"TG","capabilities":["TG"],"providers":["qwen-domain-model"],"itemCount":1,"items":[{"model":"qwen-deep-research","name":"qwen-deep-research","contextWindow":1000000,"capabilities":["TG"]}],"detailPath":"groups/qwen-deep-research.json","maxContextWindow":1000000}
{"slug":"qwen-doc-turbo","name":"Qwen-Doc-Turbo","description":"快速对文档进行精准信息抽取，打标分类，内容审核及摘要总结。","primaryCapability":"TG","capabilities":["TG"],"providers":["qwen-domain-model"],"itemCount":1,"items":[{"model":"qwen-doc-turbo","name":"Qwen-Doc-Turbo","contextWindow":262144,"capabilities":["TG"]}],"detailPath":"groups/qwen-doc-turbo.json","maxContextWindow":262144}
@@ -170,7 +169,6 @@
{"slug":"wanx-background-generation-v2","name":"图像背景生成","description":"图像背景生成可以基于输入的前景图像素材拓展生成背景信息，实现自然的光影融合效果，与细腻的写实画面生成。支持文本描述、图像引导等多种方式，同时支持对生成的图像智能添加文字内容。","primaryCapability":"IG","capabilities":["IG"],"providers":["wan"],"itemCount":1,"items":[{"model":"wanx-background-generation-v2","name":"图像背景生成","capabilities":["IG"]}],"detailPath":"groups/wanx-background-generation-v2.json"}
{"slug":"wanx-poster-generation-v1","name":"创意海报生成","description":"创意海报生成，您的创意海报魔法工厂！它能够根据你的要求自动生成海报的背景和文字排版，支持多种海报风格，从宣传到祝福，让每一张海报都成为你的个性宣言。无需设计基础，轻松制作出彩作品，让创意触手可及。","primaryCapability":"IG","capabilities":["IG"],"providers":["wan"],"itemCount":1,"items":[{"model":"wanx-poster-generation-v1","name":"创意海报生成","capabilities":["IG"]}],"detailPath":"groups/wanx-poster-generation-v1.json"}
{"slug":"wanx-sketch-to-image-lite","name":"万相-涂鸦作画","description":"万相-涂鸦作画通过手绘任意内容加文字描述，即可生成精美的涂鸦绘画作品，作品中的内容在参考手绘线条的同时，兼顾创意性和趣味性。涂鸦作画支持扁平插画、油画、二次元、3D卡通和水彩5种风格，可用于创意娱乐、辅助设计、儿童教学等场景。","primaryCapability":"IG","capabilities":["IG"],"providers":["wan"],"itemCount":1,"items":[{"model":"wanx-sketch-to-image-lite","name":"万相-涂鸦作画","capabilities":["IG"]}],"detailPath":"groups/wanx-sketch-to-image-lite.json"}
{"slug":"wanx-style-repaint-v1","name":"人像风格重绘","description":"人像风格重绘可以将输入的人物图像进行多种风格化的重绘生成，使新生成的图像在兼顾原始人物相貌的同时，带来不同风格的绘画效果。","primaryCapability":"IG","capabilities":["IG"],"providers":["wan"],"itemCount":1,"items":[{"model":"wanx-style-repaint-v1","name":"人像风格重绘","capabilities":["IG"]}],"detailPath":"groups/wanx-style-repaint-v1.json"}
{"slug":"wanx-virtualmodel","name":"虚拟模特","description":"虚拟模特可以对上传的真人或者人台实拍商品展示图进行智能生成，将其中的模特和背景替换为心仪的内容，在保持人物姿态不变的情况下，使用虚拟模特对商品进行更加精美、多样的展示。支持各种与模特产生互动的商品，如手持小商品、服装、鞋靴、配饰等。","primaryCapability":"IG","capabilities":["IG"],"providers":["wan"],"itemCount":1,"items":[{"model":"wanx-virtualmodel","name":"虚拟模特","capabilities":["IG"]}],"detailPath":"groups/wanx-virtualmodel.json"}
{"slug":"wanx-x-painting","name":"万相-图像局部重绘","description":"万相-图像局部重绘是基于自研的Composer组合生成框架的AI绘画创作大模型后置处理链路，能够根据用户输入的原始图片和意涂抹图中局部区域和prompt提示词文字内容，生成符合语义描述的多样化风格的局部重绘图像。通过知识重组与可变维度扩散模型，加速收敛并提升最终生成图片的效果, 布局自然、细节丰富、画面细腻、结果逼真。","primaryCapability":"IG","capabilities":["IG"],"providers":["wan"],"itemCount":1,"items":[{"model":"wanx-x-painting","name":"万相-图像局部重绘","capabilities":["IG"]}],"detailPath":"groups/wanx-x-painting.json"}
{"slug":"wanx2.1-vace-plus","name":"Wan2.1-VACE-Plus","description":"万相2.1-VACE-Plus，视频编辑统一模型。支持局部编辑、视频重绘、背景扩展、时长延展、图片参考等多种视频编辑与生成任务，支持文本、图像、视频等多模态条件控制。","primaryCapability":"VG","capabilities":["VG"],"providers":["wan"],"itemCount":1,"items":[{"model":"wanx2.1-vace-plus","name":"Wan2.1-VACE-Plus","capabilities":["VG"]}],"detailPath":"groups/wanx2.1-vace-plus.json"}
skills/bailian-docs-llm-wiki/models/groups/kling-models-market-place.json
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@@ -343,6 +343,69 @@
}
}
}
},
{
"inferenceMetadata": {
"response_modality": [
"Video"
],
"request_modality": [
"Text",
"Image"
]
},
"description": "生成更快，成本更低，音画同步精准，批量输出质量稳定可靠。",
"features": [],
"provider": "kling",
"model": "kling/kling-v3-turbo-video-generation",
"prices": [
{
"priceUnit": "每秒",
"price": "0.8",
"type": "video_ratio_720p",
"priceName": "视频生成（720P）"
},
{
"priceUnit": "每秒",
"price": "1",
"type": "video_ratio_1080p",
"priceName": "视频生成（1080P）"
}
],
"qpmInfo": {
"model-default-actual": {
"count_limit_period": 60,
"async_user_queue_limit": 500,
"count_limit": 300,
"type": "model-default",
"async_user_concurrency_limit": 10
},
"model-default": {
"count_limit_period": 60,
"async_user_queue_limit": 500,
"count_limit": 300,
"type": "model-default",
"async_user_concurrency_limit": 10
}
},
"priceTimeBands": [],
"capabilities": [
"VG"
],
"modelAlias": "",
"versionTag": "MAJOR",
"latestOnlineAt": "2026-08-17T03:01:59.000+00:00",
"inferenceProvider": "kling",
"name": "Kling Video 3.0 Turbo",
"category": "Third-party",
"samples": {
"dashscope": {
"default": {
"curl": "curl --location 'https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/video-generation/video-synthesis' \\\n -H 'X-DashScope-Async: enable' \\\n -H \"Authorization: Bearer $DASHSCOPE_API_KEY\" \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"model\": \"kling/kling-v3-turbo-video-generation\",\n \"input\": {\n \"prompt\": \"一只小猫在月光下奔跑\"\n },\n \"parameters\": {\n \"mode\": \"std\",\n \"aspect_ratio\": \"16:9\",\n \"duration\": 5,\n \"audio\": false,\n \"watermark\": true\n }\n}'",
"docUrl": "https://help.aliyun.com/document_detail/3026701.html"
}
}
}
}
]
}
skills/bailian-docs-llm-wiki/models/groups/qwen-coder-plus.json
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@@ -1,96 +0,0 @@
{
"name": "Qwen-Coder-Plus",
"description": "千问系列代码及编程模型是专门用于编程和代码生成的语言模型，性能出色，效果突出。",
"items": [
{
"inferenceMetadata": {
"response_modality": [
"Text"
],
"request_modality": [
"Text"
]
},
"description": "千问系列代码及编程模型是专门用于编程和代码生成的语言模型，性能出色，效果突出。",
"offlineAt": "2026-07-13T15:59:59.000+00:00",
"features": [
"model-experience"
],
"provider": "qwen-domain-model",
"model": "qwen-coder-plus",
"prices": [
{
"priceUnit": "每百万tokens",
"price": "3.5",
"timeBand": "standard",
"type": "input_token",
"priceName": "输入"
},
{
"priceUnit": "每百万tokens",
"price": "7",
"timeBand": "standard",
"type": "output_token",
"priceName": "输出"
}
],
"qpmInfo": {
"model-default-actual": {
"count_limit_period": 1,
"usage_limit": 100000,
"usage_limit_field": "total_tokens",
"count_limit": 20,
"usage_limit_period": 6,
"type": "model-default"
},
"model-default": {
"count_limit_period": 1,
"usage_limit": 100000,
"usage_limit_field": "total_tokens",
"count_limit": 20,
"usage_limit_period": 6,
"type": "model-default"
}
},
"priceTimeBands": [
"standard"
],
"capabilities": [
"TG"
],
"versionTag": "MAJOR",
"maxOutputTokens": 8192,
"latestOnlineAt": "2024-11-11T16:00:00.000+00:00",
"contextWindow": 131072,
"maxInputTokens": 129024,
"offlineInfo": {
"inference": {
"announceUrl": "https://www.aliyun.com/notice/118177",
"offlineTime": "2026-10-10 00:00:00"
}
},
"inferenceProvider": "aliyun-bailian",
"name": "Qwen-Coder-Plus",
"docUrl": "https://help.aliyun.com/document_detail/2850166.html",
"category": "Older",
"samples": {
"openai": {
"completionsAPI": {
"curl": "curl -X POST https://[workspace-id].cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions \\\n-H \"Authorization: Bearer $DASHSCOPE_API_KEY\" \\\n-H \"Content-Type: application/json\" \\\n-d '{\n \"model\": \"qwen-coder-plus\",\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a helpful assistant.\"\n },\n {\n \"role\": \"user\", \n \"content\": \"你是谁？\"\n }\n ],\n \"stream\":true\n}'",
"python": "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key=\"sk-xxx\",\n api_key=os.getenv(\"DASHSCOPE_API_KEY\"),\n base_url=\"https://[workspace-id].cn-beijing.maas.aliyuncs.com/compatible-mode/v1\",\n)\ncompletion = client.chat.completions.create(\n model=\"qwen-coder-plus\",\n messages=[\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"你是谁？\"},\n ],\n stream=True\n)\nfor chunk in completion:\n print(chunk.choices[0].delta.content, end=\"\", flush=True)",
"nodejs": "import OpenAI from \"openai\";\nconst openai = new OpenAI(\n {\n // 若没有配置环境变量，请用百炼API Key将下行替换为：apiKey: \"sk-xxx\",\n apiKey: process.env.DASHSCOPE_API_KEY,\n baseURL: \"https://[workspace-id].cn-beijing.maas.aliyuncs.com/compatible-mode/v1\"\n }\n);\nconst completion = await openai.chat.completions.create({\n model: \"qwen-coder-plus\",\n messages: [\n { role: \"system\", content: \"You are a helpful assistant.\" },\n { role: \"user\", content: \"你是谁？\" }\n ],\n stream: true\n});\nfor await (const chunk of completion) {\n process.stdout.write(chunk.choices[0].delta.content);\n}",
"docUrl": "https://help.aliyun.com/document_detail/3016807.html"
}
},
"dashscope": {
"completionsAPI": {
"curl": "curl --location \"https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation\" \\\n--header \"Authorization: Bearer $DASHSCOPE_API_KEY\" \\\n--header \"Content-Type: application/json\" \\\n--data '{\n \"model\": \"qwen-coder-plus\",\n \"input\":{\n \"messages\":[ \n {\n \"role\": \"system\",\n \"content\": \"You are a helpful assistant.\"\n },\n {\n \"role\": \"user\",\n \"content\": \"你是谁？\"\n }\n ]\n },\n \"parameters\": {\n \"result_format\": \"message\"\n }\n}'",
"python": "import os\nfrom dashscope import Generation\nimport dashscope\n\ndashscope.base_http_api_url = 'https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1'\n\nmessages = [\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"你是谁？\"},\n]\nresponse = Generation.call(\n # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = \"sk-xxx\",\n api_key=os.getenv(\"DASHSCOPE_API_KEY\"),\n model=\"qwen-coder-plus\",\n messages=messages,\n result_format=\"message\",\n)\n\nprint(response.output.choices[0].message.content)",
"java": "import java.util.Arrays;\nimport java.lang.System;\nimport com.alibaba.dashscope.aigc.generation.Generation;\nimport com.alibaba.dashscope.aigc.generation.GenerationParam;\nimport com.alibaba.dashscope.aigc.generation.GenerationResult;\nimport com.alibaba.dashscope.common.Message;\nimport com.alibaba.dashscope.common.Role;\nimport com.alibaba.dashscope.exception.ApiException;\nimport com.alibaba.dashscope.exception.InputRequiredException;\nimport com.alibaba.dashscope.exception.NoApiKeyException;\nimport com.alibaba.dashscope.protocol.Protocol;\n\npublic class Main {\n public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException {\n Generation gen = new Generation(Protocol.HTTP.getValue(), \"https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1\");\n Message systemMsg = Message.builder()\n .role(Role.SYSTEM.getValue())\n .content(\"You are a helpful assistant.\")\n .build();\n Message userMsg = Message.builder()\n .role(Role.USER.getValue())\n .content(\"你是谁？\")\n .build();\n GenerationParam param = GenerationParam.builder()\n // 若没有配置环境变量，请用百炼API Key将下行替换为：.apiKey(\"sk-xxx\")\n .apiKey(System.getenv(\"DASHSCOPE_API_KEY\"))\n .model(\"qwen-coder-plus\")\n .messages(Arrays.asList(systemMsg, userMsg))\n .resultFormat(GenerationParam.ResultFormat.MESSAGE)\n .build();\n return gen.call(param);\n }\n public static void main(String[] args) {\n try {\n GenerationResult result = callWithMessage();\n System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent());\n } catch (ApiException | NoApiKeyException | InputRequiredException e) {\n System.err.println(\"错误信息：\"+e.getMessage());\n }\n }\n}",
"docUrl": "https://help.aliyun.com/document_detail/3016809.html"
}
}
}
}
]
}
skills/bailian-docs-llm-wiki/models/groups/qwen-embedding.json
+13 -2
View File
@@ -14,14 +14,23 @@
"description": "是通义实验室基于Qwen3.7训练的多语言文本统一向量模型，相较text-embedding-v4版本在文本检索、聚类、分类性能大幅提升；在MTEB多语言、中英、Code检索等评测任务上效果提升20%；支持256~2560维用户自定义向量维度。",
"collectionTag": "qwen3.7",
"features": [
"model-experience"
"model-experience",
"batch"
],
"provider": "qwen",
"model": "qwen3.7-text-embedding",
"prices": [
{
"priceUnit": "每百万tokens",
"price": "0.25",
"timeBand": "standard",
"type": "embedding_token_batch",
"priceName": "向量输入（Batch File）"
},
{
"priceUnit": "每百万tokens",
"price": "0.5",
"timeBand": "standard",
"type": "embedding_token",
"priceName": "文本输入"
}
@@ -44,7 +53,9 @@
"type": "model-default"
}
},
"priceTimeBands": [],
"priceTimeBands": [
"standard"
],
"capabilities": [
"TR"
],
skills/bailian-docs-llm-wiki/models/groups/qwen3.8-flash.json
+9 -6
View File
@@ -96,14 +96,14 @@
"prices": [
{
"priceUnit": "每百万tokens",
"price": "1",
"price": "0.8",
"timeBand": "standard",
"type": "input_token",
"priceName": "输入"
},
{
"priceUnit": "每百万tokens",
"price": "3",
"price": "2.7",
"timeBand": "standard",
"type": "output_token",
"priceName": "输出"
@@ -117,14 +117,14 @@
},
{
"priceUnit": "每百万tokens",
"price": "0.5",
"price": "0.4",
"timeBand": "standard",
"type": "input_token_batch",
"priceName": "输入（Batch File）"
},
{
"priceUnit": "每百万tokens",
"price": "1.5",
"price": "1.35",
"timeBand": "standard",
"type": "output_token_batch",
"priceName": "输出（Batch File）"
@@ -145,15 +145,17 @@
},
{
"priceUnit": "每百万tokens",
"price": "1",
"price": "0.8",
"timeBand": "standard",
"discount": 0.5,
"type": "input_token_batch_chat",
"priceName": "输入（Batch Chat)"
},
{
"priceUnit": "每百万tokens",
"price": "3",
"price": "2.7",
"timeBand": "standard",
"discount": 0.5,
"type": "output_token_batch_chat",
"priceName": "输出（Batch Chat)"
}
@@ -187,6 +189,7 @@
"maxInputTokens": 991808,
"inferenceProvider": "aliyun-bailian",
"name": "Qwen3.8-Flash",
"docUrl": "https://help.aliyun.com/document_detail/3016807.html",
"category": "Cost-optimized",
"samples": {
"openai": {
skills/bailian-docs-llm-wiki/models/groups/qwen3.8-opensource.json
-8
View File
@@ -98,18 +98,14 @@
],
"qpmInfo": {
"model-default-actual": {
"count_limit_period": 6,
"usage_limit": 500000,
"usage_limit_field": "total_tokens",
"count_limit": 500,
"usage_limit_period": 6,
"type": "model-default"
},
"model-default": {
"count_limit_period": 6,
"usage_limit": 500000,
"usage_limit_field": "total_tokens",
"count_limit": 500,
"usage_limit_period": 6,
"type": "model-default"
}
@@ -289,18 +285,14 @@
],
"qpmInfo": {
"model-default-actual": {
"count_limit_period": 6,
"usage_limit": 500000,
"usage_limit_field": "total_tokens",
"count_limit": 500,
"usage_limit_period": 6,
"type": "model-default"
},
"model-default": {
"count_limit_period": 6,
"usage_limit": 500000,
"usage_limit_field": "total_tokens",
"count_limit": 500,
"usage_limit_period": 6,
"type": "model-default"
}
skills/bailian-docs-llm-wiki/models/groups/tongyi-xiaomi-analysis-flash.json
+7
View File
@@ -29,6 +29,13 @@
"timeBand": "standard",
"type": "output_token",
"priceName": "输出"
},
{
"priceUnit": "每百万tokens",
"price": "0.04",
"timeBand": "standard",
"type": "input_token_cache",
"priceName": "输入（缓存命中）"
}
],
"qpmInfo": {
skills/bailian-docs-llm-wiki/models/groups/tongyi-xiaomi-analysis-pro.json
+7 -5
View File
@@ -19,16 +19,20 @@
{
"priceUnit": "每百万tokens",
"price": "1",
"timeBand": "standard",
"type": "input_token",
"priceName": "输入"
},
{
"priceUnit": "每百万tokens",
"price": "2.7",
"timeBand": "standard",
"type": "output_token",
"priceName": "输出"
},
{
"priceUnit": "每百万tokens",
"price": "0.2",
"type": "input_token_cache",
"priceName": "输入（缓存命中）"
}
],
"qpmInfo": {
@@ -49,9 +53,7 @@
"type": "model-default"
}
},
"priceTimeBands": [
"standard"
],
"priceTimeBands": [],
"capabilities": [
"TG"
],
skills/bailian-docs-llm-wiki/models/groups/wanx-style-repaint-v1.json
-47
View File
@@ -1,47 +0,0 @@
{
"name": "人像风格重绘",
"description": "人像风格重绘可以将输入的人物图像进行多种风格化的重绘生成，使新生成的图像在兼顾原始人物相貌的同时，带来不同风格的绘画效果。",
"items": [
{
"inferenceMetadata": {
"response_modality": [
"Image"
],
"request_modality": [
"Image"
]
},
"description": "人像风格重绘可以将输入的人物图像进行多种风格化的重绘生成，使新生成的图像在兼顾原始人物相貌的同时，带来不同风格的绘画效果。",
"features": [],
"provider": "wan",
"model": "wanx-style-repaint-v1",
"prices": [
{
"priceUnit": "每张",
"price": "0.12",
"timeBand": "standard",
"type": "image_number",
"priceName": "图片生成"
}
],
"priceTimeBands": [
"standard"
],
"capabilities": [
"IG"
],
"versionTag": "MAJOR",
"latestOnlineAt": "2024-03-22T03:32:59.000+00:00",
"inferenceProvider": "aliyun-bailian",
"name": "人像风格重绘",
"docUrl": "https://help.aliyun.com/document_detail/2712493.html",
"samples": {
"dashscope": {
"default": {
"curl": "curl --location 'https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/image-generation/generation' \\\n--header 'X-DashScope-Async: enable' \\\n--header \"Authorization: Bearer $DASHSCOPE_API_KEY\" \\\n--header 'Content-Type: application/json' \\\n--data '{\n \"model\": \"wanx-style-repaint-v1\",\n \"input\": {\n \"image_url\": \"https://vigen-video.oss-cn-shanghai.aliyuncs.com/demo_image/image_demo_input.png\",\n \"style_index\": 3\n }\n}'\n\ncurl -X GET https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1/tasks/86ecf553-d340-4e21-xxxxxxxxx \\\n--header \"Authorization: Bearer $DASHSCOPE_API_KEY\""
}
}
}
}
]
}
skills/bailian-docs-llm-wiki/models/index.json
+10 -40
View File
@@ -1,16 +1,16 @@
{
"updatedAt": "2026-08-27",
"totalFamilies": 181,
"totalModels": 408,
"updatedAt": "2026-08-28",
"totalFamilies": 179,
"totalModels": 407,
"profileCoverage": {
"full": 0,
"partial": 0,
"structured-only": 408,
"structured-only": 407,
"none": 0
},
"capabilityDistribution": {
"TG": 38,
"IG": 32,
"TG": 37,
"IG": 31,
"VG": 28,
"TTS": 16,
"Reasoning": 15,
@@ -28,8 +28,8 @@
},
"providerDistribution": {
"qwen": 110,
"qwen-domain-model": 34,
"wan": 14,
"qwen-domain-model": 33,
"wan": 13,
"mini-max": 4,
"happyhorse": 4,
"pixverse": 4,
@@ -686,11 +686,12 @@
"providers": [
"kling"
],
"itemCount": 4,
"itemCount": 5,
"items": [
"kling/kling-v3-image-generation",
"kling/kling-v3-omni-image-generation",
"kling/kling-v3-omni-video-generation",
"kling/kling-v3-turbo-video-generation",
"kling/kling-v3-video-generation"
]
},
@@ -1080,22 +1081,6 @@
"qwen-audio-3.0-tts-plus"
]
},
{
"slug": "qwen-coder-plus",
"name": "Qwen-Coder-Plus",
"primaryCapability": "TG",
"capabilities": [
"TG"
],
"providers": [
"qwen-domain-model"
],
"itemCount": 1,
"items": [
"qwen-coder-plus"
],
"maxContextWindow": 131072
},
{
"slug": "qwen-coder-turbo",
"name": "Qwen-Coder-Turbo",
@@ -2978,21 +2963,6 @@
"wanx-sketch-to-image-lite"
]
},
{
"slug": "wanx-style-repaint-v1",
"name": "人像风格重绘",
"primaryCapability": "IG",
"capabilities": [
"IG"
],
"providers": [
"wan"
],
"itemCount": 1,
"items": [
"wanx-style-repaint-v1"
]
},
{
"slug": "wanx-virtualmodel",
"name": "虚拟模特",
skills/bailian-docs-llm-wiki/models/index.md
+4 -8
View File
@@ -1,6 +1,6 @@
# 百炼模型市场索引
> 自动生成 · 共 181 个模型家族 · 408 个主干模型 · 更新于 2026-08-27
> 自动生成 · 共 179 个模型家族 · 407 个主干模型 · 更新于 2026-08-28
**机器查询走结构化文件**：
@@ -17,7 +17,7 @@ join：`models.jsonl[].family == families.jsonl[].slug == index.json.families[].
`notSuitableFor: []` 表示无已知限制。`profileCompleteness` 为 `structured-only` 时该画像未读过文档，仅机械字段可信。
硬规格（价格/QPM/contextWindow/openSource）一律看行内顶层字段，不要从 profile 里找。
## 文本生成 `TG` — 38 个家族
## 文本生成 `TG` — 37 个家族
- [DeepSeek](groups/deepseek.json) — DeepSeek是由深度求索提供的开源模型，包含 V3.1、V3、R1以及基于Qwen2.5系列蒸馏的大语言模型。
- 模型：`deepseek-r1`, `deepseek-r1-0528`, `deepseek-r1-distill-qwen-1.5b`, `deepseek-r1-distill-qwen-14b`, `deepseek-r1-distill-qwen-32b`, `deepseek-r1-distill-qwen-7b`, `deepseek-v3`, `deepseek-v3.1`, `deepseek-v3.2`, `deepseek-v3.2-exp`, `deepseek-v4-flash`, `deepseek-v4-flash-0731`, `deepseek-v4-pro`, `deepseek-v4-pro-0813`
@@ -33,8 +33,6 @@ join：`models.jsonl[].family == families.jsonl[].slug == index.json.families[].
- 模型：`xiaomi/mimo-v2.5-pro`
- [MiniMax文本模型](groups/minimax-models-market-place.json) — 由MiniMax提供的MiniMax-M系列文本模型API服务。
- 模型：`MiniMax/MiniMax-M2.1`, `MiniMax/MiniMax-M2.5`, `MiniMax/MiniMax-M2.7`, `MiniMax/MiniMax-M3`
- [Qwen-Coder-Plus](groups/qwen-coder-plus.json) — 千问系列代码及编程模型是专门用于编程和代码生成的语言模型，性能出色，效果突出。
- 模型：`qwen-coder-plus`
- [Qwen-Coder-Turbo](groups/qwen-coder-turbo.json) — Qwen-Coder-Turbo模型是专门用于编程和代码生成的语言模型，推理速度快，成本低。
- 模型：`qwen-coder-turbo`
- [qwen-deep-research](groups/qwen-deep-research.json) — 千问深入研究是一款面向复杂研究任务的高级智能体系统，具备多轮推理与全局规划能力，能够运用互联网搜索等多种工具，对任务进行精细化拆解，开展推理与分析，最终为用户生成可溯源、逻辑严谨的研究型报告。
@@ -96,7 +94,7 @@ join：`models.jsonl[].family == families.jsonl[].slug == index.json.families[].
- [通义法睿-Plus-32K](groups/farui-plus.json) — 通义法睿是以通义千问为基座经法律行业数据和知识专门训练的法律行业大模型产品，综合运用了模型精调、强化学习、 RAG检索增强、法律Agent技术，具有回答法律问题、推理法律适用、推荐裁判类案、辅助案情分…
- 模型：`farui-plus`
## 图像生成 `IG` — 32 个家族
## 图像生成 `IG` — 31 个家族
- [AI试衣-Plus版](groups/aitryon-plus.json) — aitryon-plus是一款效果出众的虚拟试衣图片生成模型，可基于服饰平拍图片以及人物正面全身照，输出服饰的人物试衣效果图片。 相较于aitryon模型，aitryon-plus模型在图片清晰度、服…
- 模型：`aitryon-plus`
@@ -144,8 +142,6 @@ join：`models.jsonl[].family == families.jsonl[].slug == index.json.families[].
- 模型：`wanx-x-painting`
- [万相-涂鸦作画](groups/wanx-sketch-to-image-lite.json) — 万相-涂鸦作画通过手绘任意内容加文字描述，即可生成精美的涂鸦绘画作品，作品中的内容在参考手绘线条的同时，兼顾创意性和趣味性。涂鸦作画支持扁平插画、油画、二次元、3D卡通和水彩5种风格，可用于创意娱乐、…
- 模型：`wanx-sketch-to-image-lite`
- [人像风格重绘](groups/wanx-style-repaint-v1.json) — 人像风格重绘可以将输入的人物图像进行多种风格化的重绘生成，使新生成的图像在兼顾原始人物相貌的同时，带来不同风格的绘画效果。
- 模型：`wanx-style-repaint-v1`
- [人物实例分割](groups/image-instance-segmentation.json) — 人物实例分割运用了检测和分割技术，不仅能够在图像中识别出不同的对象，而且还能准确地画出每一个对象边界的像素级掩码（mask）。
- 模型：`image-instance-segmentation`
- [创意海报生成](groups/wanx-poster-generation-v1.json) — 创意海报生成，您的创意海报魔法工厂！它能够根据你的要求自动生成海报的背景和文字排版，支持多种海报风格，从宣传到祝福，让每一张海报都成为你的个性宣言。无需设计基础，轻松制作出彩作品，让创意触手可及。
@@ -198,7 +194,7 @@ join：`models.jsonl[].family == families.jsonl[].slug == index.json.families[].
- [Wan2.1-VACE-Plus](groups/wanx2.1-vace-plus.json) — 万相2.1-VACE-Plus，视频编辑统一模型。支持局部编辑、视频重绘、背景扩展、时长延展、图片参考等多种视频编辑与生成任务，支持文本、图像、视频等多模态条件控制。
- 模型：`wanx2.1-vace-plus`
- [可灵AI](groups/kling-models-market-place.json) — 由可灵AI提供的高质量视频与图像生成及编辑模型。
- 模型：`kling/kling-v3-image-generation`, `kling/kling-v3-omni-image-generation`, `kling/kling-v3-omni-video-generation`, `kling/kling-v3-video-generation`
- 模型：`kling/kling-v3-image-generation`, `kling/kling-v3-omni-image-generation`, `kling/kling-v3-omni-video-generation`, `kling/kling-v3-turbo-video-generation`, `kling/kling-v3-video-generation`
- [声动人像VideoRetalk](groups/videoretalk.json) — VideoRetalk是一个人物视频生成模型，可基于人物视频和人声音频，生成人物讲话口型与输入音频相匹配的新视频。
- 模型：`videoretalk`
- [悦动人像EMO](groups/emo-v1.json) — EMO是一款视频生成模型，可基于人物图片生成高质量的人物肖像动态视频。
skills/bailian-docs-llm-wiki/models/models.jsonl
+407 -408
View File
@@ -1,408 +1,407 @@
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{"model":"z-image-turbo","name":"Z-Image-Turbo","family":"z-image-turbo","familyName":"Z-Image-Turbo","provider":"qwen-domain-model","capabilities":["IG"],"features":["model-experience"],"inferenceMetadata":{"response_modality":["Text","Image"],"request_modality":["Text"]},"prices":[{"type":"image_standard","unit":"每张","price":"0.1"},{"type":"image_thinking","unit":"每张","price":"0.2"}],"qpmInfo":{"model-default-actual":{"count_limit":2,"count_limit_period":1},"model-default":{"count_limit":2,"count_limit_period":1}},"versionTag":"SNAPSHOT","docUrl":"https://help.aliyun.com/document_detail/3002354.html","profile":{"profileCompleteness":"structured-only","familyTier":null,"familyComparison":null,"callModes":["sync"],"_meta":{"generatedAt":"2026-08-27T20:09:57.440Z","sourceHash":"8197f692943966c2","generatorVersion":"1.0.0"}},"detailPath":"groups/z-image-turbo.json"}
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skills/bailian-docs-llm-wiki/raw/application-user-guide/application-gallery/quanmiao-light-application-series/light-application-guidelines-for-use/pan-enterprise-voc-mining.md
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# 泛企业VOC挖掘
泛企业VOC挖掘工具通过大模型对评论、论坛、客服聊天及通话等非结构化VOC数据实现智能标签化处理，支持标签选择或自定义，相比人工或规则打标，具备更高的准确率及响应速度，可快速适配业务标签的动态变化需求。
## 功能入口
登录**阿里云百炼大模型服务平台**，在**应用广场**页面，点击[**泛企业VOC挖掘**](https://bailian.console.aliyun.com/?spm=a2c4g.11186623.0.0.134172147obHXO#/app/app-market/quanmiao/voc)即可进入该轻应用控制台。
## 功能介绍
### 应用详情
在**泛企业VOC挖掘**应用的**应用详情**页签，您可以查看功能描述、目标客群、最佳实践、计费规则、全妙相关应用推荐五部分内容。
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/2718982471/p932062.png)
### 效果调试
在**效果调试**页签，如果您是首次使用，可以参考官方示例完成相关配置填写（本文以**电商平台用户反馈分析**为例说明）。
点击**电商平台用户反馈分析**，在弹出的文本框中点击**使用本示例**一键填充单个示例。您也可以点击**批量挖掘**，系统将自动填充批量挖掘示例。
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944696.png) ![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944697.png)
#### 第一步：上传素材
1. 输入**待挖掘素材**内容，您可以选择以下两种素材输入方式：
- **单个素材**：输入单条VOC，快速体验。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944713.png)
- **批量素材**：支持批量数据同时分析。先点击**批量挖掘**，再点击**下载模板**后上传，提高效率。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944712.png)
支持.xlsx、.xls、.txt、.csv、.pdf、.docx、.md格式的文件，文件最大20MB。
2. 选择模型，默认为**千问-Max**。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944434.png)
#### 第二步：设置内容标签
1. 输入**任务描述**，帮助模型理解内容。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944715.png)
2. 设置**内容标签**：
## 按照格式输入**标签名称**、**标签含义**，支持单个/批量输入。可参考官方提供的示例填写。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944765.png)
添加自定义内容标签：点击**添加自定义标签**，可支持自定义标签，精准挖掘信息。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944752.png)
> 支持多级标签，目前支持两级，层级间用"-"区分，例如：品牌名称-产品功能。
批量输入多个自定义内容标签：点击**批量输入**，可一键粘贴所有的标签名称、标签含义，自动识别。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944753.png)![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944755.png)
- 每行为一组，用换行区分；
- 每组内，用分隔符隔开标签名称、标签含义，例如：
性别 男、女、无法判断
最高学历 小学、初中、大学、研究生、博士
3. 可选：补充业务知识。
您可以在此文本框中输入业务背景、此任务的注意事项或者任务示例等补充内容。
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944770.png)
#### （可选）第三步：设置输出格式
输出格式推荐使用`json`格式，且请注意KEY和您所需要挖掘的标签要能一一对应，您可以参考示例来修改。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944775.png)
- 在完整填写第一步与第二步的前提下，点击**智能生成JSON格式**，模型会根据当前数据生成输出格式作为参考，请自行检查并确认最终输出格式。
- 您也可以点击**重置**清空输出格式。
#### （可选）第四步：设置高级功能
1. 开启**设置筛选维度**开关后，模型会先对VOC进行筛选，仅符合设定条件的VOC才参与挖掘，进一步精准范围。可输入单条或多条筛选维度。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944808.png)
您也可以点击**添加自定义标签**或**批量输入**，添加筛选维度，具体操作请参见[设置内容标签](raw/application-user-guide/application-gallery/quanmiao-light-application-series/light-application-guidelines-for-use/pan-enterprise-voc-mining.md)。
1. 选中**保存输入项**。
> 选中后系统会默认保存本次设置的筛选条件、标签，以及标签内容。
> 如果切换账号或清除浏览器本地存储，保存的配置数据将丢失。
#### 第五步：开始挖掘
点击**开始挖掘**，挖掘结果将在挖掘结束后显示在右侧。![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944863.png)
### 查看API示例
效果调试完成后，点击**API**页签，您可以查看对应生成的API示例。
**说明**支持单条实时流式API和批处理API两种调用方式：
- 单条实时流式API：请求一次接口（输入一段文本、发起一次调用），就实时处理并流式返回结果。流式响应协议为Server-Sent Events（SSE）。
- 批处理API：输入待挖掘文件或多段文本，后台会创建一个任务，用户需通过任务ID轮询任务的状态、直到任务结束后，返回结果。
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/3991694471/p944504.png)
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- 优化示例填充
具体请参见[泛企业VOC挖掘](https://help.aliyun.com/zh/model-studio/pan-enterprise-voc-mining)。
具体请参见[泛企业VOC挖掘](raw/application-user-guide/application-gallery/quanmiao-light-application-series/light-application-guidelines-for-use/pan-enterprise-voc-mining.md)。
4月11日
skills/bailian-docs-llm-wiki/raw/application-user-guide/application-gallery/tongyi-dianjin/api-reference-3/api-dianjin-2024-06-28-dir/api-dianjin-2024-06-28-dir-platform-capabilities-application/api-dianjin-2024-06-28-gettaskstatus.md
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@@ -1,194 +0,0 @@
# GetTaskStatus - 获取任务状态
获取任务状态。
## 调试
您可以在OpenAPI Explorer中直接运行该接口，免去您计算签名的困扰。运行成功后，OpenAPI Explorer可以自动生成SDK代码示例。
[调试](https://api.alibabacloud.com/api/DianJin/2024-06-28/GetTaskStatus)
## 授权信息
下表是API对应的授权信息，可以在RAM权限策略语句的`Action`元素中使用，用来给RAM用户或RAM角色授予调用此API的权限。具体说明如下：
- 操作：是指具体的权限点。
- 访问级别：是指每个操作的访问级别，取值为写入（Write）、读取（Read）或列出（List）。
- 资源类型：是指操作中支持授权的资源类型。具体说明如下：
- 对于必选的资源类型，用前面加 \* 表示。
- 对于不支持资源级授权的操作，用`全部资源`表示。
- 条件关键字：是指云产品自身定义的条件关键字。
- 关联操作：是指成功执行操作所需要的其他权限。操作者必须同时具备关联操作的权限，操作才能成功。
操作
访问级别
资源类型
条件关键字
关联操作
dianjin:GetTaskStatus
get
\*全部资源
`*`
无
无
## 请求语法
```
GET /{workspaceId}/api/task/status HTTP/1.1
```
## 请求参数
名称
类型
必填
描述
示例值
workspaceId
string
是
业务空间 Id
llm-xxxx
taskId
string
是
任务 Id
17071319
## 返回参数
名称
类型
描述
示例值
object
ResultCode
cost
long
耗时
null
data
string
响应数据，任务状态。
- invalid：参数/依赖的资源状态不可用。
- success：成功。
- evict: 被取消。
- error：失败。
- running：运行中。
- pending：排队中。
running
dataType
string
数据类型
null
errCode
string
错误码
0
message
string
错误信息
ok
requestId
string
请求 id
30F6AD44-F078-540D-B5A5-1E519C8E9E6D
success
boolean
是否成功
true
time
string
时间戳
2024-04-24 11:54:34
## 示例
正常返回示例
`JSON`格式
```
{
"cost": 0,
"data": "running",
"dataType": null,
"errCode": 0,
"message": "ok",
"requestId": "30F6AD44-F078-540D-B5A5-1E519C8E9E6D",
"success": true,
"time": "2024-04-24 11:54:34"
}
```
## 错误码
访问[错误中心](https://api.alibabacloud.com/document/DianJin/2024-06-28/errorCode)查看更多错误码。
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中断任务。
[GetTaskStatus](raw/application-user-guide/application-gallery/tongyi-dianjin/api-reference-3/api-dianjin-2024-06-28-dir/api-dianjin-2024-06-28-dir-platform-capabilities-application/api-dianjin-2024-06-28-gettaskstatus.md)
[GetTaskStatus](https://help.aliyun.com/zh/model-studio/api-dianjin-2024-06-28-gettaskstatus)
获取任务状态
skills/bailian-docs-llm-wiki/raw/application-user-guide/application-gallery/tongyi-dianjin/api-reference-3/api-dianjin-2024-06-28-ram.md
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@@ -117,7 +117,7 @@ none
dianjin:GetTaskStatus
[GetTaskStatus](raw/application-user-guide/application-gallery/tongyi-dianjin/api-reference-3/api-dianjin-2024-06-28-dir/api-dianjin-2024-06-28-dir-platform-capabilities-application/api-dianjin-2024-06-28-gettaskstatus.md)
[GetTaskStatus](https://help.aliyun.com/zh/model-studio/api-dianjin-2024-06-28-gettaskstatus)
get
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@@ -749,7 +749,7 @@
阿里云百炼预置了常用的 MCP 服务，您可以引用这些官方MCP服务，也可以引用开源社区或自己开发的自定义 MCP 服务。
- 如果要引用官方MCP服务，您需要确认[已开通云部署 MCP 服务](https://help.aliyun.com/zh/model-studio/official-and-third-party-mcp#47ed5b5cff6hs)。
- 如果要引用自定义MCP服务，您需要先部署[自定义MCP服务](raw/application-user-guide/model-context-protocol/custom-mcp.md)。
- 如果要引用自定义MCP服务，您需要先部署[自定义MCP服务](https://help.aliyun.com/zh/model-studio/custom-mcp)。
- **参数配置**
**参数名称**
skills/bailian-docs-llm-wiki/raw/application-user-guide/model-context-protocol/custom-mcp.md
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@@ -1,253 +0,0 @@
# 自定义 MCP 服务
除官方 MCP 服务外，阿里云百炼还支持以下三种自定义 MCP 服务部署方式：
1. **使用脚本部署**：面向**代码**。适用于您拥有或找到了一个遵循 MCP 协议的**代码包**，例如您自行开发或开源社区的 MCP 服务代码。
2. **从 AI 网关导入**：面向**已有的 API**。适用于您想将一个**现成的、非 MCP 规范的 RESTful API**（例如公司内部的业务接口、第三方服务接口）封装成可供大模型调用的工具。
3. **从阿里云 OpenAPI 导入**：面向**阿里云生态**。适用于您想让大模型能够操作**其他的阿里云产品**（如 OSS, ECS 等）。
## 使用脚本部署
对于自行开发或开源社区的 MCP 服务代码，阿里云百炼支持使用[函数计算 FC](https://www.aliyun.com/product/fc)进行托管。
以部署开源社区的 Knowledge Graph Memory MCP 服务为例：
> Knowledge Graph Memory MCP 服务：该服务使大模型能够记录个性化信息，并在后续交互中调用这些信息进行回复。
1. **创建 MCP 服务**
前往[MCP 管理](https://bailian.console.aliyun.com/?tab=app#/mcp-manage)，点击**创建MCP服务**。选择**使用脚本部署**，点击**部署服务**。
2. **配置 MCP 服务**
按照以下指引填写配置，确认无误后提交部署。
**配置项**
**用途**
**本案例的配置**
**服务名称****描述**
用于区分不同的 MCP 服务，不影响大模型的判断和调用。
**服务名称**：长期记忆
**描述**：该服务使大模型能够记录个性化信息，并在后续交互中调用这些信息进行回复。
**安装方式**
如果需要托管本地 MCP 服务（stdio），支持以下方式：
- **npx**：启动使用 Node.js 开发的 MCP 服务
- **uvx**：启动使用 Python 开发的 MCP 服务。
如果需要连接远程 MCP 服务，支持以下方式：
- **http**：连接到一个已有的、运行在别处的远程 MCP 服务器。
选择：**npx**
**部署方式**
（仅限 npx/uvx）
**基础模式：按次计费**：适用于按需启动、节省成本、可以承受较慢响应的场景。
**极速模式**：适用于持续运行、快速响应、可以承受较高成本的场景。
> 详细计费规则请参考[计费说明](https://help.aliyun.com/zh/model-studio/mcp-introduction#fb482455a3u8c)。
保持**基础模式：按次计费**，关闭**极速模式**
**部署地域**
（仅限 npx/uvx）
为获得最佳响应速度，建议选择靠近其他已有云服务的地域。若不确定如何选择，推荐使用“**北京**”地域。
选择：**北京**
**MCP 服务配置**
**重要**请务必核实 MCP 服务的来源和源代码，以免遭受钓鱼攻击。
**重要**并非所有 MCP 服务都支持 npx/uvx/http 方式部署。若缺少相应配置代码，则无法直接部署到阿里云百炼。建议参考 [MCP 官方文档](https://modelcontextprotocol.io/quickstart/server)进行本地部署。
**说明**使用 Java 开发的 MCP 服务，可以[自行部署到阿里云函数计算](https://help.aliyun.com/zh/functioncompute/mcp-server)，再通过 http 托管到百炼。
- 如需部署开源社区 MCP 服务，请直接粘贴从 MCP 服务详情页获取的配置代码。
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/5067198471/p963082.png)
- 如需部署自行开发的 MCP 服务，请参考以下模板编写配置代码。
**说明**此配置模板适用于[基于 npx/uvx/http 最佳实践开发的 MCP 服务](https://modelcontextprotocol.io/examples)。若采用其他开发方式，请对模板进行相应修改。
```
{
"mcpServers": {
"本地 MCP 服务": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@your_acc_name/your_pkg_name"
],
"env": {
"YOUR_ENV_KEY": "YOUR_ENV_VALUE"
}
},
"远程 MCP 服务": {
"type": "sse/streamableHttp",
"url": "https://your-mcp-server/sse"
}
}
}
```
直接使用以下配置代码:
```
{
"mcpServers": {
"memory": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-memory"
]
}
}
}
```
或在[MCP 广场](https://bailian.console.aliyun.com/?tab=mcp#/mcp-market)找到这个 MCP 服务，单击进入详情页，找到标题为 `NPX` 的配置代码。
3. **管理 MCP 服务**
提交部署后，可查看部署状态、测试工具效果以及修改服务配置。
1. **查看部署状态**
可前往函数计算 FC 控制台查看 MCP 服务的详细部署状态。
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/9001984471/p944519.png)
（可选）建议启用日志服务，方便实时查看服务运行状态，排查可能出现的错误。
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/9001984471/p944533.png)
**说明**函数计算 FC 的日志服务会产生一定费用，详情请参考控制台的日志服务计费说明。
2. **测试工具效果：**
在**工具**页可测试 MCP 服务的运行效果
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/9001984471/p944542.png)
3. **修改服务配置：**
部署后，仅支持编辑**服务名称**和**描述**。如需修改**部署方式**、**部署地域**、**安装方式**和 **MCP 服务配置**，必须先停止当前部署，在修改配置后重新部署。
4. **使用 MCP 服务**
部署完成后，即可将自定义 MCP 服务[在智能体或工作流中配置 MCP 服务](https://help.aliyun.com/zh/model-studio/official-and-third-party-mcp#8319d03864jym)。
## 从 AI 网关导入
对于已有的自建或第三方业务接口，可使用AI 网关将 RESTful API 升级为 MCP 服务，详情请参考[MCP网关管理](https://help.aliyun.com/zh/api-gateway/ai-gateway/user-guide/gateway-managed-mcp-services)。通过阿里云百炼，可以快速导入在 AI 网关托管的 MCP 服务。
1. **导入准备**
请确保已在 [AI 网关](https://apig.console.aliyun.com/#/cn-hangzhou/ai-gateway)托管了 MCP 服务。
2. **导入 MCP 服务**
前往 [MCP 管理](https://bailian.console.aliyun.com/?tab=app#/mcp-manage)，单击**创建MCP服务**。选择**AI网关**，单击**导入服务**。
3. **配置 MCP 服务**
按照下表指引填写配置信息，确认无误后单击**确认导入**。
**配置项**
**配置说明**
**示例**
**网关所在区域**
AI 网关所在地域
华东 1（杭州）
**网关实例**
AI 网关实例
test
**选择MCP服务**
AI 网关托管的 MCP 服务
test
**选择接入点**
AI 网关托管的 MCP 服务的接入点。系统会自动生成一个可供测试的接入点。
\[自动生成\]
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/7729282571/p987431.png)
4. **完成导入**
导入完成后，可在**MCP 管理自定义服务**查看从 AI 网关导入的 MCP 服务。
## 从阿里云 OpenAPI 导入
如需通过操作阿里云云资源创建服务，可使用 OpenAPI 开发者门户将官方 OpenAPI 快速发布为 MCP 服务，详情请参考[OpenAPI MCP Server 使用指南](https://help.aliyun.com/zh/openapi/user-guide/openapi-mcp-server-guide)。通过阿里云百炼，可以快速导入 OpenAPI 开发者门户 MCP 服务。
1. **导入准备**
请确保在 [OpenAPI 开发者门户](https://api.aliyun.com/mcp)创建过 MCP 服务。
2. **导入 MCP 服务**
前往[MCP 管理](https://bailian.console.aliyun.com/?tab=app#/mcp-manage)，点击**创建MCP服务**。选择**阿里云OpenAPI**，点击**导入服务**。
3. **配置 MCP 服务**
按照以下指引填写配置，检查无误后点击**确认导入**。
**配置项**
**配置说明**
**示例**
**服务名称**
需要导入的 OpenAPI MCP 服务。
oss
**访问阿里云 OpenAPI 角色**
阿里云百炼访问 OpenAPI 时的角色。可使用已有角色，或在此处**创建角色**。
BailianMcpRoleFoross
**权限策略**
前往[RAM 权限策略](https://ram.console.aliyun.com/policies)，使用此处的 JSON 脚本创建权限策略，并为**访问阿里云 OpenAPI 角色**授予该权限策略。
\[自动生成\]
![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/7729282571/p987389.png)
4. **完成导入**
导入完成后，可在**MCP 管理自定义服务**查看从 OpenAPI 导入的 MCP 服务。
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@@ -40,7 +40,7 @@
- **Python：** 软件包发布到 PyPI 仓库，通过 uvx <package-name> 部署。
- **已部署在远程服务器：** 通过 SSE 连接。
详见[自定义MCP服务](raw/application-user-guide/model-context-protocol/custom-mcp.md)。
详见[自定义MCP服务](https://help.aliyun.com/zh/model-studio/custom-mcp)。
4. **阿里云百炼 MCP 服务能在其他 MCP 客户端 （Cline、Cherry Studio）中使用吗？**
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@@ -24,7 +24,7 @@
## 如何使用 MCP
现在，阿里云百炼智能体和工作流应用已支持接入两种 MCP 服务。使用 MCP 服务的详细方法，请参考[官方 MCP 服务](raw/application-user-guide/model-context-protocol/official-and-third-party-mcp.md)和[自定义MCP服务](raw/application-user-guide/model-context-protocol/custom-mcp.md)。
现在，阿里云百炼智能体和工作流应用已支持接入两种 MCP 服务。使用 MCP 服务的详细方法，请参考[官方 MCP 服务](raw/application-user-guide/model-context-protocol/official-and-third-party-mcp.md)和[自定义MCP服务](https://help.aliyun.com/zh/model-studio/custom-mcp)。
- 官方 MCP 服务：阿里云百炼官方部署了多种 MCP 服务，方便您快速接入阿里云百炼应用。
- 自定义 MCP 服务：阿里云百炼还支持部署自定义 MCP 服务。您可以在 MCP 市场或互联网上搜寻 MCP 服务，以自定义 MCP 服务的形式部署在阿里云百炼上。
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@@ -413,7 +413,7 @@ MCP
新增MCP市场与MCP管理功能
- 您可以开通阿里云百炼预置的MCP服务或部署自定义MCP服务，并在智能体应用和工作流应用中引用这些 MCP 服务，使应用具备更强大的能力。使用方法请参见[官方 MCP 服务](raw/application-user-guide/model-context-protocol/official-and-third-party-mcp.md)和[自定义MCP服务](raw/application-user-guide/model-context-protocol/custom-mcp.md)。
- 您可以开通阿里云百炼预置的MCP服务或部署自定义MCP服务，并在智能体应用和工作流应用中引用这些 MCP 服务，使应用具备更强大的能力。使用方法请参见[官方 MCP 服务](raw/application-user-guide/model-context-protocol/official-and-third-party-mcp.md)和[自定义MCP服务](https://help.aliyun.com/zh/model-studio/custom-mcp)。
4月1日
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@@ -1,3 +0,0 @@
# 文件管理
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# 查询和管理文件
查询、列举和删除文件，并了解文件对象的属性信息。
## 获取指定文件信息
```
GET https://dashscope.aliyuncs.com/api/v1/files/{file_id}
Accept: application/json
```
### 请求示例
```
curl --location --request GET "https://dashscope.aliyuncs.com/api/v1/files/123" \
--header "Authorization: Bearer ${DASHSCOPE_API_KEY}"
```
### 请求参数
**字段**
**类型**
**传参方式**
**必选**
**描述**
file\_id
String
path
是
文件ID，可通过[上传文件](raw/model-api-reference/file-management-api/upload-file-api.md)或列举文件接口获取
### 返回示例
```
{
"request_id": "bddee90e-d320-4bf9-b32b-ac3359192d1e",
"data": {
"file_id": "mbSwIGR9yA6i1EtwIpndFErIoDKFyQ6W5601487ECAB111EDA20422ECF4959B5E",
"name": "dogs.jpg",
"description": "1",
"size": 129862,
"md5": "1d5ee55c2453009b14db98e74c453abb",
"gmt_create": "2023-03-25 10:04:11",
"url": "http://xxxxx.oss-cn-hangzhou.aliyuncs.com/oss%3A//dashscope-pre/api-fs/1253/236/dogs.jpg?Expires=1679797621&OSSAccessKeyId=YOUR_ACCESS_KEY_ID&Signature=YOUR_SIGNATURE"
}
}
```
### 返回参数
**字段**
**类型**
**描述**
data.file\_id
String
文件ID
data.url
String
文件下载链接
data.name
String
文件名
data.size
Number
文件大小
data.md5
String
文件的md5
data.description
String
文件的描述
data.gmt\_create
Date
文件上传时间
data.id
String
文件的内部ID
data.region
String
文件所在地域
data.user\_id
String
用户ID
data.api\_key\_id
String
API Key ID
request\_id
String
本次请求的系统唯一码
## 列举当前所有的文件
```
GET https://dashscope.aliyuncs.com/api/v1/files
Accept: application/json
```
### 请求示例
```
curl --location --request GET "https://dashscope.aliyuncs.com/api/v1/files?page_no=1&page_size=20" \
--header "Authorization: Bearer ${DASHSCOPE_API_KEY}"
```
### 请求参数
**字段**
**类型**
**传参方式**
**必选**
**描述**
page\_no
Number
Query
是
当前页，最小值为1，默认值为1
page\_size
Number
Query
是
分页大小。最小值为1，最大值为100，默认值为10
### 返回示例
```
{
"request_id": "123456",
"data": {
"total": 2,
"page_size": 20,
"page_no": 1,
"files": [
{
"id": 1001,
"file_id": "9G2EaQtq7p1fw7oRhYXdHTtDFYAMVQSh95432B38CAB211EDB8F952C2E8001733",
"name": "test.txt",
"description": "1",
"size": 0,
"md5": "d41d8cd98f00b204e9800998ecf8427e",
"gmt_create": "2023-03-25 10:13:07",
"url": "http://xxx.oss-cn-hangzhou.aliyuncs.com/oss%3A//dashscope-dev/api-fs/123456/123456/test.txt",
"region": "cn-hangzhou",
"user_id": "123456",
"api_key_id": "sk-xxxx",
"purpose": "fine-tune"
},
{
"id": 1002,
"file_id": "wmIDj6zemqjIb8L2o8NHIlMRk3QinjGP00E987C7CA3711ED83E0000EC63B0D1C",
"name": "sdsdsd.mp4",
"description": "1",
"size": 780635,
"md5": "8382b3d3137bce6eaf1beef9c8920ef6",
"gmt_create": "2023-03-24 19:28:30",
"url": "http://xxxx.oss-cn-hangzhou.aliyuncs.com/oss%3A//dashscope-dev/api-fs/123456/123456/sdsdsd.mp4",
"region": "cn-hangzhou",
"user_id": "123456",
"api_key_id": "sk-xxxx",
"purpose": "file-extract"
}
]
}
}
```
### 返回参数
**字段**
**类型**
**描述**
data.total
Number
总记录数
data.page\_size
Number
分页大小
data.page\_no
Number
当前页
data.files
Array
文件列表，元素为文件对象，属性详见文件对象小节
request\_id
String
本次请求的系统唯一码
## 删除文件
```
DELETE https://dashscope.aliyuncs.com/api/v1/files/{file_id}
Accept: application/json
```
### 请求示例
```
curl --location --request DELETE "https://dashscope.aliyuncs.com/api/v1/files/123" \
--header "Authorization: Bearer ${DASHSCOPE_API_KEY}"
```
### 请求参数
**字段**
**类型**
**传参方式**
**必选**
**描述**
file\_id
String
path
是
文件ID，可通过[上传文件](raw/model-api-reference/file-management-api/upload-file-api.md)或列举文件接口获取
### 返回示例
```
{
"request_id": "038e9953-6f0e-4691-afcc-1f0fe07b32c2"
}
```
### 返回参数
**字段**
**类型**
**描述**
code
String
错误码，仅当本次请求出错时返回
message
String
错误信息，仅当本次请求出错时返回
request\_id
String
本次请求的系统唯一码
## 文件对象
文件对象表示已上传到百炼平台的文件及其属性信息。通过[上传文件](raw/model-api-reference/file-management-api/upload-file-api.md)接口创建，可通过列举文件和查询文件详情接口获取。
除 DashScope 原生接口外，您也可以通过 [OpenAI 兼容-File 接口](raw/model-api-reference/toolkits-and-frameworks/openai-file-interface.md)（`/compatible-mode/v1/files`）管理文件。
> 当前文件管理 API 仅在北京 Region 开放。如您使用其他 Region，请通过该 Region 的百炼控制台完成文件管理操作。
**字段**
**类型**
**描述**
file\_id
String
文件ID
name
String
文件名
description
String
文件的描述
size
Number
文件大小
md5
String
文件的md5
url
String
文件下载链接
gmt\_create
Date
文件上传时间
id
String
文件的内部ID
region
String
文件所在地域
user\_id
String
用户ID
api\_key\_id
String
API Key ID
purpose
String
文件用途
skills/bailian-docs-llm-wiki/raw/model-api-reference/file-management-api/upload-file-api.md
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@@ -0,0 +1,199 @@
# 上传文件
上传文件到百炼平台，支持多文件上传，上传后可在多个任务中复用。
## 使用限制
当您使用本服务时，会有如下限制存在
- 单个文件大小限制因 purpose 而异：file-extract 最大 150MB，batch 最大 500MB，fine-tune 最大 300MB。
- 有效文件（未删除）总使用空间配额为100GB。
- 有效文件（未删除）总数量配额为10000个。
OpenAI 兼容方式调用请参考：相关文档。
**说明**通过 fine-tune purpose 上传的调优文件，在百炼控制台模型调优页面与 API 调用中均可见可用。
## 上传文件
```
POST https://dashscope.aliyuncs.com/api/v1/files
Content-type: multipart/form-data
```
### 请求示例
```
curl --request POST "https://dashscope.aliyuncs.com/api/v1/files" \
--header "Authorization: Bearer ${DASHSCOPE_API_KEY}" \
--form 'files=@"/path/to/your/file1.jsonl"' \
--form 'purpose="fine-tune"'\
--form 'descriptions="a sample fine-tune data file for qwen"' \
--form 'files=@"/path/to/your/file2.jsonl"' \
--form 'purpose="fine-tune"'\
--form 'descriptions="a sample fine-tune data file for qwen"'
```
### 请求参数
**字段**
**类型**
**传参方式**
**必选**
**描述**
files
文件流
multipart/form-data
是
调优文件，支持多文件上传
purpose
字符串
multipart/form-data
否
**字段**
**用途**
`fine-tune`
通过上传后的`file_id`进行模型调优。视频/图像生成模型微调的训练数据需以 .zip 格式上传（单个 zip 包不超过 1 GB）；也可通过 OSS 挂载方式加载，将未经压缩的数据集文件夹整体上传到 OSS，不支持 zip 文件。
`file-extract`
通过上传后的`file_id`进行内容分析。
`batch`
通过上传后的`file_id`来[创建Batch任务](raw/model-api-reference/toolkits-and-frameworks/batch-interfaces-compatible-with-openai.md)。
不传 purpose 时文件仍可上传成功，建议明确指定 purpose 以便后续按用途管理文件。
descriptions
文件流
multipart/form-data
否
文件描述
### 返回示例
```
{
"request_id": "xxx",
"data": {
"uploaded_files": [
{
"file_id": "9G2EaQtq7p1fw7oRhYXdHTtDFYAMVQSh95432B38CAB211EDB8F952C2E8001733",
"name": "test.txt"
}
],
"failed_uploads": [
{
"name": "test1.jpg",
"code": "BadRequest.TooLarge",
"message": "Out of space, <839> B of <1024> B storage space has been used."
},
{
"name": "test2.jpg",
"code": "BadRequest.TooMany",
"message": "Out of number, <10> of <10> files has been uploaded."
}
]
}
}
```
### 返回参数
**字段**
**类型**
**描述**
data.failed\_uploads
Array
上传失败的文件信息
data.uploaded\_files
Array
上传成功的文件信息
data.uploaded\_files.$.file\_id
String
文件ID，可用于查询文件详情和删除文件接口
data.uploaded\_files.$.name
String
文件名称
request\_id
String
本次请求的系统唯一码
## 请求异常
### 返回示例
当返回的HTTP状态码不为200时，表示请求失败，此时示例返回如下。
```
{
"request_id": "8f25f57c-5cc0-9881-9c83-62bc173dc9ad",
"code": "InvalidParameter",
"message": "File not found."
}
```
### 返回参数
**字段**
**类型**
**描述**
code
String
错误码。
request\_id
String
本次请求的系统唯一码。
message
String
错误信息。
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@@ -2,42 +2,6 @@
在控制台获取或创建 API Key 后，您将使用它来安全地调用百炼的模型服务。
## 套餐类型识别
百炼提供三种 API Key 类型。API Key 与 Base URL 必须配套使用，否则调用 Anthropic 兼容接口时可能返回 401 或 403 错误。
**API Key 前缀**
**套餐类型**
**Anthropic 兼容 Base URL**
`sk-`
按量付费
`[https://dashscope.aliyuncs.com/apps/anthropic](https://dashscope.aliyuncs.com/apps/anthropic)`
`sk-sp-`
Token Plan
`[https://token-plan.cn-beijing.maas.aliyuncs.com/apps/anthropic](https://token-plan.cn-beijing.maas.aliyuncs.com/apps/anthropic)`
`sk-ws-`
Coding Plan
`[https://coding.dashscope.aliyuncs.com/apps/anthropic](https://coding.dashscope.aliyuncs.com/apps/anthropic)`
**重要**关于 `sk-ws-` 前缀的归属存在待确认的事实冲突：需求断言 `sk-ws-` 对应 Coding Plan，但同一篇文档的「API key security upgrade」章节已写明 `sk-ws` 是按量付费 API Key 安全升级后的新格式。两种说法互斥。请在发布前确认 `sk-ws-` 的真实归属、按量付费升级后的前缀口径，以及三个 `/apps/anthropic` Base URL 的最终写法。
### 查看套餐类型
1. 登录百炼控制台。
2. 单击顶部导航栏的**订阅**，展开后选择**Token Plan**，或进入**我的订阅**页面。
3. 在**我的订阅**页面，通过**Coding Plan**和**Token Plan**页签查看各自的订阅状态，以及对应的 API Key 和 Base URL。
## 第一步：获取 API Key
1. 前往阿里云百炼的**密钥管理**（[中国大陆版](https://bailian.console.aliyun.com/?tab=model#/api-key) | [国际版](https://modelstudio.console.aliyun.com/?tab=playground#/api-key)）页面。
@@ -54,18 +18,18 @@ Coding Plan
> RAM 用户的账号格式为`username@<AccountAlias>.onaliyun.com`。
- **归属业务空间：**选择“**默认业务空间**”，此空间下的 Key 可调用所有[标准模型](raw/model-user-guide/get-started-with-models/models.md)。若下拉框不可选，说明系统已默认选择**默认业务空间**，直接单击**确定**即可完成创建。下拉框呈灰色不可选，原因是当前账号下未创建子业务空间，或当前账号为 RAM 用户且未被加入任何子业务空间。由阿里云账号（主账号）在**业务空间管理**页面创建子业务空间后，重新创建 API Key 即可选择该子业务空间。
- **归属业务空间：**选择“**默认业务空间**”，此空间下的 Key 可调用所有[标准模型](raw/model-user-guide/get-started-with-models/models.md)。
> 单个业务空间最多支持创建 20 个 API Key。
如需以下功能，请选择非默认的业务空间，相关信息请参考[业务空间管理](https://help.aliyun.com/zh/model-studio/use-workspace)：
如需以下功能，请选择非默认的业务空间，相关信息请参考[业务空间管理](raw/model-user-guide/security-and-compliance/permission-management-overview.md)：
- 对不同项目（业务空间）进行权限隔离
- 精细化的模型访问控制
- 进行独立的成本核算
- **权限**：建议选择**全部**，若有更精细的权限控制需求，可以选择**自定义**来控制可访问 IP 和 可访问模型。
> IP 白名单默认设置：IPv4（`0.0.0.0/0`）全部放通；IPv6 仅华北2（北京）地域支持，美国（弗吉尼亚）地域仅支持 IPv4。最多支持 **20** 个地址或网段，批量输入请用英文逗号隔开。输入 IP 地址后请按回车键确认，系统才会识别并保存该地址。不按回车键直接单击**确定**，输入的 IP 地址不会被保存。IP 白名单按发起请求的连接 IP 校验，不校验 `X-Forwarded-For` 等请求头中携带的 IP 地址。通过代理或网关访问百炼时，请将代理节点的出口 IP 添加到白名单，无需添加客户端自身 IP。来自白名单以外 IP 的请求会被拒绝，返回 `HTTP 403` 及 `IP access denied by API-Key restrictions`。
> IP 白名单默认设置：IPv4（`0.0.0.0/0`）全部放通；IPv6 仅华北2（北京）地域支持，美国（弗吉尼亚）地域仅支持 IPv4。最多支持 **20** 个地址或网段，批量输入请用英文逗号隔开。
3. 点击新创建的 API Key 旁的![image](https://help-static-aliyun-doc.aliyuncs.com/assets/img/zh-CN/8412544571/p994217.png)图标复制 API Key。
@@ -87,8 +51,6 @@ Coding Plan
我们也提供了一些常用工具的配置方法：[Dify](raw/model-user-guide/use-chat-client-or-development-tool/dify.md)、[Chatbox](https://help.aliyun.com/zh/model-studio/cline-tool)、[Cline](raw/model-user-guide/use-chat-client-or-development-tool/cline.md)、[Claude Code](raw/model-user-guide/use-chat-client-or-development-tool/claude-code.md)、[Postman](raw/model-user-guide/use-chat-client-or-development-tool/first-call-to-image-and-video-api.md)。
百炼 API Key 用于调用百炼的 API 端点，不同套餐对应的 API Key 前缀与 Base URL 请以本文「套餐类型识别」章节为准。部分第三方工具的千问模式使用千问网页版账号登录（qwenauth），与百炼 API Key 属于不同的认证方式：工具要求填写 API Key 时，请使用百炼 API Key；工具要求账号登录时，请使用千问网页版账号。两者不能混用，否则调用会失败，并可能返回「没有额度」等错误。
- **方式二：配置 API Key 到环境变量，后续支持 cURL 和代码调用**
**重要**请勿在客户端代码（如浏览器、移动应用）或不可信环境中配置或使用长期有效的 API Key。可以使用[临时 API Key](raw/model-api-reference/more-about-models/generate-temporary-api-key.md)（最长 1800 秒）。
@@ -292,13 +254,13 @@ API Key 的状态随账号操作而变化：
请访问[文本生成](raw/model-api-reference/qwen-api-reference.md)以及更多模型的 API 参考。
OpenAI SDK 支持的模型，请参考[OpenAI 兼容](https://help.aliyun.com/zh/model-studio/compatible-with-openai)。
OpenAI SDK 支持的模型，请参考[OpenAI 兼容](https://help.aliyun.com/zh/model-studio/toolkits-and-frameworks)。
## 常见问题
#### 无法创建 API Key？
API Key 的创建需使用[超级管理员](https://help.aliyun.com/zh/model-studio/permission-management-overview#982297bd47p3i)或[业务空间管理员](https://help.aliyun.com/zh/model-studio/permission-management-overview#c82f37c2033vw)操作。若系统返回 `Apikey limit number exceeded for MainAccount`，表示当前账号的 API Key 数量已达上限，请删除不再使用的 API Key 后重试。
API Key 的创建需使用[超级管理员](https://help.aliyun.com/zh/model-studio/permission-management-overview#982297bd47p3i)或[业务空间管理员](https://help.aliyun.com/zh/model-studio/permission-management-overview#c82f37c2033vw)操作。
#### 如何管理 API Key 的权限？
@@ -312,7 +274,7 @@ API Key 的调用权限完全由其**归属业务空间**决定。**同一空间
如需在业务空间之外做更细粒度的控制，可在创建或编辑 API Key 时将**权限**切换为**自定义**，配置：
- **IP 访问白名单**：仅允许名单内的 IP 使用该 API Key 发起调用。支持 IPv4 与网段，默认 IPv4（`0.0.0.0/0`）全部放通；IPv6 仅华北2（北京）地域支持，美国（弗吉尼亚）地域仅支持 IPv4。最多支持 **20** 个地址或网段，批量输入请用英文逗号隔开。
- **访问模型范围**：开关开启后，该 API Key 仅能调用已勾选的模型，可选模型限于该业务空间已授权的模型（最多 30 个）。
- **访问模型范围**：开关开启后，该 API Key 仅能调用已勾选的模型，可选模型限于该业务空间已授权的模型。
#### 环境变量已设置，为何代码仍提示找不到 API Key？
@@ -329,19 +291,6 @@ API Key 的调用权限完全由其**归属业务空间**决定。**同一空间
- 情况四：**用了sudo命令**。如果使用`sudo python xx.py`运行脚本，可能会遗漏当前用户环境变量，因为`sudo`默认不继承所有环境变量。您可采用`sudo -E python xx.py`命令，其中的`-E` 参数确保环境变量被传递。如有权限执行该脚本，可以直接执行 `python xx.py`。
#### 百炼 API Key 与 RAM AccessKey 有何区别？
两者是用途不同的凭证。百炼 API Key 不是密钥对，创建后只会得到一个字符串，不存在 AccessKeyId 与 AccessKeySecret，因此在**密钥管理**页面看不到“访问密钥密文”。具体区别如下：
- **格式**：百炼 API Key 是以`sk-`开头的单个字符串；RAM AccessKey 由 AccessKeyId 和 AccessKeySecret 组成密钥对。
- **用途**：百炼 API Key 用于调用百炼的模型与应用服务；RAM AccessKey 用于调用阿里云 OpenAPI。
- **获取方式**：百炼 API Key 在阿里云百炼的**密钥管理**页面创建，具体步骤参见本文“第一步：获取 API Key”；RAM AccessKey 在 RAM 控制台创建。
- **显示规则**：百炼 API Key 仅在创建成功的弹窗中完整显示一次，供您复制或下载保存，关闭弹窗后列表中只显示脱敏形式，无法再次查看完整明文；RAM AccessKey 的 AccessKeySecret 同样仅在创建时显示一次。
#### 单个主账号下最多能创建多少个API Key？
每个主账号下最多可创建20个业务空间（包括默认业务空间），每个业务空间下最多可创建20个API Key。
#### 百炼是否支持批量导出所有 API Key 信息？
不支持。百炼控制台的**API Key 管理**页面未提供批量导出或下载功能，如需获取多个 API Key 的信息，请在该页面逐个查看并复制。
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@@ -84,6 +84,14 @@ Android / iOS / HarmonyOS
全平台（任何支持 WebSocket 的环境）
Token Plan
支持
支持
支持
开发者可根据实际需求选择协议方案：
- **AOQ 方案**：适合对延迟、弱网对抗、多模态数据传输有极致要求的 AI 实时交互场景，同时内置回声消除和降噪能力，尤其是移动端原生应用。
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@@ -97,7 +97,7 @@ endpoint
根据业务情况选择接入域名
指定对应的接入域名，详情请参见[选择地域、服务部署范围和接入域名](raw/model-user-guide/get-started-with-models/regions.md)
指定对应的接入域名，详情请参见[选择地域、服务部署范围和接入域名](raw/model-user-guide/get-started-with-models/regions.md)。如果使用[Token Plan](raw/model-user-guide/token-plan-guide/token-plan-overview.md)，接入域名中的 `WorkspaceId` 固定为 `token-plan`。
Content-Type
skills/bailian-docs-llm-wiki/raw/model-api-reference/toolkits-and-frameworks/batch-interfaces-compatible-with-openai.md
+13 -13
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@@ -36,7 +36,7 @@
- 图像与视频理解：qwen3.8-max、qwen3.8-flash、qwen3.7-plus、qwen3.6-plus、qwen3.7-flash、qwen3.6-flash、qwen3.5-plus、qwen3.5-flash、qwen3-vl-plus、qwen3-vl-flash
- 文字提取：qwen-vl-ocr、qwen-vl-ocr-latest
- 全模态：qwen3.5-omni-plus
- [**文本向量模型**](https://help.aliyun.com/zh/model-studio/user-guide/embedding)**：**text-embedding-v1、text-embedding-v2、text-embedding-v3、text-embedding-v4
- [**向量模型**](https://help.aliyun.com/zh/model-studio/user-guide/embedding)**：**text-embedding-v1、text-embedding-v2、text-embedding-v3、text-embedding-v4、qwen3.7-text-embedding
**重要**
@@ -124,7 +124,7 @@ def upload_file(file_path):
def create_batch_job(input_file_id):
print(f"正在基于文件ID，创建Batch任务...")
# 请注意:此处endpoint参数值需和输入文件中的url字段保持一致.测试模型(batch-test-model)填写/v1/chat/ds-test,Embedding文本向量模型填写/v1/embeddings,其他模型填写/v1/chat/completions
# 请注意:此处endpoint参数值需和输入文件中的url字段保持一致.测试模型(batch-test-model)填写/v1/chat/ds-test,向量模型填写/v1/embeddings,其他模型填写/v1/chat/completions
batch = client.batches.create(input_file_id=input_file_id, endpoint="/v1/chat/ds-test", completion_window="24h")
print(f"Batch任务创建完成。 得到Batch任务ID: {batch.id}\n")
return batch.id
@@ -748,7 +748,7 @@ Batch 推理需要构造 JSONL 文件，每行为一个独立请求。以下 Pyt
Batch 推理 JSONL 文件生成工具
支持的地域：
- 华北2（北京）：文本生成、多模态、通用文本向量
- 华北2（北京）：文本生成、多模态、向量
- 新加坡：文本生成（qwen-max、qwen-plus、qwen-turbo）
支持的模型：
@@ -764,7 +764,7 @@ Batch 推理 JSONL 文件生成工具
qwen3.5-plus*, qwen3.5-flash*, qwen3-vl-plus*, qwen3-vl-flash*,
qwen-vl-max, qwen-vl-max-latest, qwen-vl-plus, qwen-vl-plus-latest,
qwen-vl-ocr, qwen-vl-ocr-latest, qwen-omni-turbo
文本向量（华北2）：text-embedding-v1/v2/v3/v4
向量模型（华北2）：text-embedding-v1/v2/v3/v4, qwen3.7-text-embedding
文件大小限制：单行 ≤6MB，总文件 ≤500MB，建议 ≤10000 行
@@ -858,16 +858,16 @@ TASKS = [
# - 思考模式需额外设置 ENABLE_THINKING = True
```
## 文本向量模型请求格式
## 向量模型请求格式
文本向量模型使用 `/v1/embeddings` 接口，请求格式不同于文本生成模型。可使用以下独立脚本生成：
向量模型使用 `/v1/embeddings` 接口，请求格式不同于文本生成模型。可使用以下独立脚本生成：
```
#!/usr/bin/env python3
"""文本向量模型 Batch JSONL 生成（使用 /v1/embeddings 接口）"""
"""向量模型 Batch JSONL 生成（使用 /v1/embeddings 接口）"""
import json
MODEL = "text-embedding-v3" # 可选: v1, v2, v3, v4
MODEL = "text-embedding-v3" # 可选: v1, v2, v3, v4, qwen3.7-text-embedding
TEXTS = [
"衣服的质量杠杠的，很漂亮，不枉我等了这么久啊，喜欢，以后还来这里买。",
@@ -900,7 +900,7 @@ print(f"已生成 {output_file}，共 {len(TEXTS)} 条请求")
`/v1/chat/completions`
文本向量模型
向量模型
`/v1/embeddings`
@@ -1165,7 +1165,7 @@ client = OpenAI(
batch = client.batches.create(
input_file_id="file-batch-xxx", # 上传文件返回的id或OSS文件URL或OSS文件资源标识符
endpoint="/v1/chat/completions", # 测试模型batch-test-model填写/v1/chat/ds-test，文本向量模型填写/v1/embeddings，文本生成/多模态模型填写/v1/chat/completions
endpoint="/v1/chat/completions", # 测试模型batch-test-model填写/v1/chat/ds-test，向量模型填写/v1/embeddings，文本生成/多模态模型填写/v1/chat/completions
completion_window="24h",
metadata={'ds_name':"任务名称",'ds_description':'任务描述'} # metadata数据，非必填字段，用于创建任务名称、描述
)
@@ -1357,7 +1357,7 @@ Body
访问路径，须与输入文件中的URL字段一致。
- Embedding文本向量模型填写`/v1/embeddings`
- 向量模型填写`/v1/embeddings`
- 测试模型`batch-test-model`填写`/v1/chat/ds-test`
@@ -2773,7 +2773,7 @@ client = OpenAI(
batch = client.batches.create(
input_file_id="file-batch-xxx", # 上传文件返回的 id
endpoint="/v1/chat/completions", # Embedding文本向量模型填写"/v1/embeddings",测试模型batch-test-model填写/v1/chat/ds-test,其他模型填写/v1/chat/completions
endpoint="/v1/chat/completions", # 向量模型填写"/v1/embeddings",测试模型batch-test-model填写/v1/chat/ds-test,其他模型填写/v1/chat/completions
completion_window="24h",
metadata={
"ds_batch_finish_callback": "https://xxx/xxx"
@@ -2848,7 +2848,7 @@ with open("input_demo.csv", "r") as fin:
csvreader = csv.reader(fin)
for row in csvreader:
body = {"model": "qwen-turbo", "messages": messages_builder_example(row[1])}
# 选择Embedding文本向量模型进行调用时，url的值需填写"/v1/embeddings",其他模型填写/v1/chat/completions
# 选择向量模型进行调用时，url的值需填写"/v1/embeddings",其他模型填写/v1/chat/completions
request = {"custom_id": row[0], "method": "POST", "url": "/v1/chat/completions", "body": body}
fout.write(json.dumps(request, separators=(',', ':'), ensure_ascii=False) + "\n")
```
skills/bailian-docs-llm-wiki/raw/model-api-reference/video-generation-api/kling-api-reference/kling-video-generation-api-reference.md
+131 -13
View File
@@ -16,6 +16,75 @@
- **选择 URL**：选择对应的地域 Endpoint URL，支持HTTP URL。
- **配置 API Key**：选择地域并[获取与配置 API Key](raw/model-api-reference/preparations/get-api-key.md)，再[配置API Key到环境变量](raw/model-api-reference/preparations/get-api-key.md)。
## 模型概览
**模型名称**
**能力支持**
**输入模态**
**输出视频规格**
kling/kling-v3-turbo-video-generation
- 文生视频
- 图生视频-基于首帧
文本、图像
分辨率模式：std（720P）、pro（1080P）
宽高比：16:9、9:16、1:1
视频时长：3-15秒
视频格式：MP4
kling/kling-v3-video-generation
- 文生视频
- 图生视频-基于首帧
- 图生视频-基于首尾帧
文本、图像
分辨率模式：std（720P）、pro（1080P）、4k
宽高比：16:9、9:16、1:1
视频时长：3-15秒
视频格式：MP4
kling/kling-v3-omni-video-generation
- 文生视频
- 图生视频-基于首帧
- 图生视频-基于首尾帧
- 参考生视频
- 视频编辑
文本、图像、视频
分辨率模式：std（720P）、pro（1080P）、4k
宽高比：16:9、9:16、1:1
视频时长：3-15秒
视频格式：MP4
## HTTP调用
由于视频生成任务耗时较长（通常为1-5分钟），API采用异步调用。整个流程包含 **"创建任务 -> 轮询获取"** 两个核心步骤，具体如下：
@@ -57,8 +126,9 @@
可选值：
- `kling/kling-v3-omni-video-generation`
- `kling/kling-v3-turbo-video-generation`
- `kling/kling-v3-video-generation`
- `kling/kling-v3-omni-video-generation`
**input** `object` **（必选）**
@@ -85,6 +155,13 @@ Omni模型可通过prompt与主体、图片、视频等内容实现多种能力
- 引用顺序：按照`media`数组顺序定义prompt中引用的顺序。
- 用法示例：`一只<<<element_1>>>在月光下奔跑`。
**negative\_prompt** `string` （可选）
负向提示词，用于描述不希望在结果中出现的内容。不超过2500个字符。
- `kling/kling-v3-turbo-video-generation` 无独立负向提示词能力，该字段不生效（负向描述可写入`prompt`）。
- 支持模型：`kling/kling-v3-video-generation`、`kling/kling-v3-omni-video-generation`。
**media** `array` （可选）
文生视频任务无需填写此参数。
@@ -95,6 +172,14 @@ Omni模型可通过prompt与主体、图片、视频等内容实现多种能力
素材组合规则
kling/kling-v3-turbo-video-generation
仅支持以下媒体素材组合，非法组合将报错。
- **图生视频-基于首帧**：`first_frame`。
- 数量限制：首帧恰好1张。不支持`last_frame`。
kling/kling-v3-video-generation
仅支持以下媒体素材组合，非法组合将报错。
@@ -133,6 +218,14 @@ kling/kling-v3-omni-video-generation
媒体素材类型。可选值与所选模型有关：
kling/kling-v3-turbo-video-generation
可选值：
- `first_frame`：首帧图片。
不支持`last_frame`。不同媒体素材之间的搭配限制，请参见 `media` 参数下的”素材组合规则”。
kling/kling-v3-video-generation
可选值：
@@ -140,7 +233,7 @@ kling/kling-v3-video-generation
- `first_frame`：首帧图片。
- `last_frame`：尾帧图片。
不同媒体素材之间的搭配限制，请参见 `media` 参数下的“素材组合规则”。
不同媒体素材之间的搭配限制，请参见 `media` 参数下的”素材组合规则”。
kling/kling-v3-omni-video-generation
@@ -179,7 +272,7 @@ kling/kling-v3-omni-video-generation
- 支持 HTTP 和 HTTPS 协议。
- 示例值：[](https://xxx/xxx.mp4)[https://xxx/xxx.mp4](https://xxx/xxx.mp4)。
视频限制：
**`kling/kling-v3-video-generation`视频限制：**
- 格式：mp4、mov。
- 时长：3～10s。
@@ -187,6 +280,15 @@ kling/kling-v3-omni-video-generation
- 文件大小：不超过200MB。
- 帧率：24～60fps。
**`kling/kling-v3-omni-video-generation`视频限制：**
- 格式：mp4、mov。
- 文件大小：不超过200MB。
- 时长：3～15.5s。
- 分辨率：宽和高的范围为\[700, 4553\]像素，像素总面积不超过8294400。
- 宽高比：0.4～2。
- 帧率：24～60fps。
**keep\_original\_sound** `string` （可选）
支持模型：kling/kling-v3-omni-video-generation 。
@@ -205,6 +307,12 @@ kling/kling-v3-omni-video-generation
- `false`：默认值，不开启多镜头。
- `true`：开启多镜头生成。
注意：
- 视频特征参考（feature）场景支持开启多镜头（`shot_type`仅支持`intelligence`）。
- 视频编辑（base）场景不支持多镜头。
- `kling/kling-v3-turbo-video-generation` 支持多镜头。
**shot\_type** `string` （条件必填）
当`multi_shot=true` 时必填。多镜头模式类型。
@@ -236,6 +344,8 @@ kling/kling-v3-omni-video-generation
主体列表，用于指定视频中需要引入的主体元素。
注意：`kling/kling-v3-turbo-video-generation` 不支持主体，该字段不可传。
属性
**element\_id** `integer` （条件必填）
@@ -248,6 +358,7 @@ kling/kling-v3-omni-video-generation
- 图生视频-基于首尾帧：最多支持3个主体。
- 参考生视频（`type=refer`）：参考图片与多图主体数量之和不得超过7。
- 参考生视频（`type=feature+refer`）：参考图片与多图主体数量之和不得超过4。
- 视频输入场景（omni模型）：支持视频类型主体，最多1个。引用视频类型主体后，不可再传参考图/图片类型主体。
**parameters** `object` （可选）
@@ -263,6 +374,8 @@ kling/kling-v3-omni-video-generation
- `std`：标准模式，输出视频分辨率为720P。
- `4k`：4K模式，输出视频分辨率为4K。
注意：`kling/kling-v3-turbo-video-generation` 仅支持`std`（720P）和`pro`（1080P），不支持4K。
**aspect\_ratio** `string` （条件必填）
生成视频的宽高比例。
@@ -291,14 +404,16 @@ kling/kling-v3-omni-video-generation
生成视频的时长，单位为秒。
- kling/kling-v3-turbo-video-generation：取值为\[3, 15\]之间的整数，默认值为5。
- kling/kling-v3-video-generation：取值为\[3, 15\]之间的整数，默认值为5。
- kling/kling-v3-omni-video-generation：取值为\[3, 15\]之间的整数，默认值为5。
- 注意：当传入视频时，该参数的行为因`type`不同而异：
- `type=feature`：取值为\[3, 10\]之间的整数，默认值为5。
- `type=base`：输出视频时长与传入视频时长相同，此时当前参数无效。按输入视频时长四舍五入取整计量计费。
- kling/kling-v3-video-generation：取值为\[3, 15\]之间的整数，默认值为5。
示例值：5。
@@ -311,7 +426,10 @@ kling/kling-v3-omni-video-generation
- `false`：默认值，输出无声视频。
- `true`：输出有声视频。
注意：当传入视频（`type=base或feature`）时，`audio`只能设置为`false`。
注意：
- 当传入视频（`type=base或feature`）时，`audio`只能设置为`false`。
- `kling/kling-v3-turbo-video-generation` 固定生成音画同出视频，该字段不生效（恒为生成）。
**watermark** `boolean` （可选）
@@ -322,7 +440,7 @@ kling/kling-v3-omni-video-generation
#### 文生视频
支持模型：`kling/kling-v3-omni-video-generation`、`kling/kling-v3-video-generation`。
支持模型：`kling/kling-v3-turbo-video-generation`、`kling/kling-v3-video-generation`、`kling/kling-v3-omni-video-generation`。
```
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID。
@@ -331,7 +449,7 @@ curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/servi
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "kling/kling-v3-video-generation",
"model": "kling/kling-v3-turbo-video-generation",
"input": {
"prompt": "一只小猫在月光下奔跑"
},
@@ -347,7 +465,7 @@ curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/servi
#### 文生视频（智能分镜）
支持模型：`kling/kling-v3-omni-video-generation`、`kling/kling-v3-video-generation`。
支持模型：`kling/kling-v3-turbo-video-generation`、`kling/kling-v3-video-generation`、`kling/kling-v3-omni-video-generation`。
```
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID。
@@ -388,7 +506,7 @@ curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/servi
#### 图生视频（首帧生视频）
支持模型：`kling/kling-v3-omni-video-generation`、`kling/kling-v3-video-generation`。
支持模型：`kling/kling-v3-turbo-video-generation`、`kling/kling-v3-video-generation`、`kling/kling-v3-omni-video-generation`。
```
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID。
@@ -418,7 +536,7 @@ curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/servi
#### 图生视频（首尾帧生视频）
支持模型：`kling/kling-v3-omni-video-generation`、`kling/kling-v3-video-generation`。
支持模型：`kling/kling-v3-video-generation`、`kling/kling-v3-omni-video-generation`。
```
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID。
@@ -686,11 +804,11 @@ curl -X GET https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/tasks/{tas
**SR** `string`
生成视频的分辨率档位。示例值：720。
生成视频的分辨率档位。示例值：720。`mode=4k`时返回`4k`；`kling/kling-v3-turbo-video-generation`仅返回`720`或`1080`。
**audio** `boolean`
生成视频是否为有声视频。示例值：false。
生成视频是否为有声视频。示例值：false。`kling/kling-v3-turbo-video-generation`恒为`true`（固定音画同出）。
**video\_count** `integer`
skills/bailian-docs-llm-wiki/raw/model-user-guide/fine-tuning/fine-tune-speech-synthesis-model/fine-tune-speech-synthesis-model-by-api.md
+1 -1
View File
@@ -720,7 +720,7 @@ with open('output.mp3', 'wb') as f:
[上传调优文件](https://help.aliyun.com/zh/model-studio/fine-tune-speech-synthesis-model-by-api#cv_upload_title)
[上传文件](https://help.aliyun.com/zh/model-studio/upload-file-api)
[上传文件](raw/model-api-reference/file-management-api/upload-file-api.md)
创建调优任务
skills/bailian-docs-llm-wiki/raw/model-user-guide/fine-tuning/fine-tune-text-generation-model/fine-tuning-api-guide.md
+1 -1
View File
@@ -68,7 +68,7 @@ curl --request POST \
通过 API 上传的调优文件，在百炼控制台模型调优页面与 API 调用中均可见可用。
更多详细信息请参见[上传文件](https://help.aliyun.com/zh/model-studio/upload-file-api)。
更多详细信息请参见[上传文件](raw/model-api-reference/file-management-api/upload-file-api.md)。
返回结果：
skills/bailian-docs-llm-wiki/raw/model-user-guide/get-started-with-models/quota-management.md
+64
View File
@@ -39,6 +39,30 @@ TPM 限流值按**百炼平台月消费金额**分档，支持的模型在不同
2000万
**qwen3.8-flash**
500万
1000万
2000万
**qwen3.8-27b**
500万
500万
500万
**qwen3.8-2.4t-a95b**
500万
500万
500万
**deepseek-v4-pro-0813**
120万
@@ -47,6 +71,14 @@ TPM 限流值按**百炼平台月消费金额**分档，支持的模型在不同
2000万
**kimi-k3**
120万
500万
1000万
#### 新加坡
**百炼平台月消费：**
@@ -69,6 +101,38 @@ TPM 限流值按**百炼平台月消费金额**分档，支持的模型在不同
1000万
**qwen3.8-flash**
250万
500万
1000万
**kimi-k3**
120万
500万
1000万
**qwen3.8-27b**
500万
500万
500万
**qwen3.8-2.4t-a95b**
500万
500万
500万
**deepseek-v4-pro-0813**
120万
skills/bailian-docs-llm-wiki/raw/model-user-guide/get-started-with-models/rate-limit.md
+20 -2
View File
@@ -1087,6 +1087,14 @@ qwen3.8-max
5,000,000
qwen3.8-flash
全球
30,000
5,000,000
qwen3.7-max
全球
@@ -1351,6 +1359,14 @@ qwen3.8-max
5,000,000
qwen3.8-flash
全球
30,000
5,000,000
qwen3.7-max
全球
@@ -5829,7 +5845,7 @@ kling/kling-v3-omni-image-generation
10
> 同一阿里云百炼API Key 下，可灵系列的 4 个模型（[图像](https://help.aliyun.com/zh/model-studio/rate-limit#fdd6e348d2md4)及[视频](raw/model-user-guide/get-started-with-models/rate-limit.md)）共享 10 个并发数。即这 4 个模型处于运行状态的任务总数加起来不能超过 10 个。
> 同一阿里云百炼API Key 下，可灵系列的 5 个模型（[图像](https://help.aliyun.com/zh/model-studio/rate-limit#fdd6e348d2md4)及[视频](https://help.aliyun.com/zh/model-studio/rate-limit#kling-third-party-title)）共享 10 个并发数。即这 5 个模型处于运行状态的任务总数加起来不能超过 10 个。
kling/kling-v3-image-generation
@@ -8101,7 +8117,9 @@ kling/kling-v3-omni-video-generation
10
> 同一阿里云百炼API Key 下，可灵系列的 4 个模型（[图像](https://help.aliyun.com/zh/model-studio/rate-limit#fdd6e348d2md4)及[视频](raw/model-user-guide/get-started-with-models/rate-limit.md)）共享 10 个并发数。即这 4 个模型处于运行状态的任务总数加起来不能超过 10 个。
> 同一阿里云百炼API Key 下，可灵系列的 5 个模型（[图像](https://help.aliyun.com/zh/model-studio/rate-limit#fdd6e348d2md4)及[视频](https://help.aliyun.com/zh/model-studio/rate-limit#kling-third-party-title)）共享 10 个并发数。即这 5 个模型处于运行状态的任务总数加起来不能超过 10 个。
kling/kling-v3-turbo-video-generation
kling/kling-v3-video-generation
skills/bailian-docs-llm-wiki/raw/model-user-guide/model-deployment-1/dtu-model-deployment.md
+567
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@@ -0,0 +1,567 @@
# DTU 模型部署
独占吞吐DTU（Dedicated Throughput Unit）以独占部署方式为指定模型提供性能和吞吐保障，按输入/输出 TPM（Tokens Per Minute，每分钟 token 数）售卖。本文介绍 DTU 的功能、计费方式、使用流程与支持模型。
## 产品概述
DTU（Dedicated Throughput Unit，独占吞吐）以独占部署方式为指定模型提供性能和吞吐保障，按 Input/Output TPM 售卖，支持基础模型与自定义模型。DTU 提供全托管推理服务，底层独占 GPU 资源，由平台负责运维。
DTU 是模型单元（Model Unit，MU）的继任方案：在保留 MU 独占算力、资源隔离与可部署微调模型等能力的同时，将计量粒度由模型单元数量调整为输入/输出 TPM，提供更直接的吞吐保障。新购独占部署建议优先选择 DTU。
- 独占 GPU 资源，推理环境与其他用户物理隔离。
- 支持基础模型和自定义模型（百炼微调模型或用户上传模型）部署。
- 按性能档位选购，平台负责底层运维，无需管理 GPU。
- 不主动设 RPM/TPM 上限，流量受实际承载力限制。
通用模型部署流程详见[模型部署文档](raw/model-user-guide/model-deployment-1/model-deployment-introduction.md)与[预置吞吐](raw/model-user-guide/model-deployment-1/ptu-long-input-and-cache.md)。
## 方案选型
百炼为推理调用提供多种容量与计费方案。DTU 适用于需要独占部署、微调模型部署、低延迟高并发或数据隔离的场景。各方案的对比如下表所示。
Token 按量和 PTU 部署详见[模型部署](raw/model-user-guide/model-deployment-1/model-deployment-introduction.md)。
**对比维度**
**DTU**
**PTU**
**Token 按量**
**PAI/灵骏**
特点
独占算力 + 微调模型
吞吐保障 + 低延迟
弹性灵活、零门槛
自定义运行时
资源隔离
物理隔离
逻辑隔离
共享公池
物理隔离
微调模型
全参 + LoRA
不支持
LoRA
支持
自定义框架
不支持
不支持
不支持
支持
计费模式
TPM 额度 × 时长
TPM 额度 × 时长
Token 用量
GPU × 时长
## 计费说明
DTU 按输入 TPM 和输出 TPM 分别计费，采用预付费（按月）模式。各模型有固定的输入/输出基准 TPM（见下表），**购买时需为基准 TPM 的整数倍**。输入和输出各至少购买基准 TPM（1 倍），生产环境建议各购买 2 倍以上。
费用由后端根据模型、输入/输出 TPM、服务区域和购买时长计算。各模型的价格如下表所示，起售和扩容价格均为输入输出各 1 倍基准 TPM 的总价，微调模型的价格与对应基础模型相同，具体以控制台实际展示为准。
> 部署相同模型，规格性能排序为：`Fast > Efficient > Standard`。
#### 华北-北京2
**模型**
**规格**
**基准输入（TPM）**
**基准输出（TPM）**
**起售价格(元/月)**
**扩容价格(元/月)**
deepseek-v4-flash
Standard
2,240,000
280,000
1,107,400
1,107,904
deepseek-v4-flash-0731
Fast
1,676,000
213,000
277,984
277,984
glm-5.2
Fast
1,004,000
120,000
1,895,260
1,895,260
glm-5.1
Standard
256,000
32,000
504,704
504,704
qwen3.5-122b-a10b
Standard
7,288,000
904,000
3,772,320
3,772,320
qwen3.5-27b
Standard
291,000
36,000
131,976
131,976
Efficient
2,432,000
304,000
411,616
411,616
qwen3.5-35b-a3b
Standard
656,000
82,000
109,962
109,962
qwen3.5-397b-a17b
Standard
896,000
112,000
1,107,904
1,107,904
qwen3.5-4b
Standard
1,072,000
134,000
109,478
109,478
qwen3.6-27b
Standard
273,000
34,000
49,118
49,118
qwen3.7-plus-2026-05-26
Fast
1192000
148000
483,956
483,956
各模型在标准工况下的性能参考数据如下表所示，以控制台实际展示为准。
> 以下性能参考数据均在缓存命中率为 0% 的条件下测得。实际使用中，随着缓存命中率提升，模型性能也会相应提高。
**模型**
**规格**
**输入长度**
**输出长度**
**缓存命中率**
**首字延迟（ms）**
**每token延迟（ms）**
deepseek-v4-flash
Standard
4,000
500
无缓存命中
651
19
deepseek-v4-flash-0731
Fast
16,000
2,000
无缓存命中
1,079
13
glm-5.2
Fast
16,000
2,000
无缓存命中
1,558
15
glm-5.1
Standard
4,000
500
无缓存命中
769
27
qwen3.5-122b-a10b
Standard
16,000
2,000
无缓存命中
568
8
qwen3.5-27b
Standard
4,000
500
无缓存命中
1,448
23
Efficient
4,000
500
无缓存命中
703
14
qwen3.5-35b-a3b
Standard
4,000
500
无缓存命中
471
10
qwen3.5-397b-a17b
Standard
4,000
500
无缓存命中
996
27
qwen3.5-4b
Standard
4,000
500
无缓存命中
552
6
qwen3.6-27b
Standard
4,000
500
无缓存命中
1,292
19
qwen3.7-plus-2026-05-26
Fast
16000
2000
无缓存命中
888
10
## 创建部署
使用 DTU 前需在控制台开通 DTU 功能并申请资源额度。开通后在百炼控制台模型部署页面选择目标模型，计费方式选择 DTU 即可创建部署。
**警告**DTU 部署暂不支持通过 API 创建与管理，请在百炼控制台完成开通、创建、扩缩容与续费等操作。
通用模型部署的基础流程详见[模型部署文档](raw/model-user-guide/model-deployment-1/model-deployment-introduction.md)。
创建部署的表单字段如下表所示。
**参数**
**说明**
**是否必填**
**取值说明**
服务名称
部署服务的名称
是
自定义
模型
部署的目标模型
是
下拉选择
部署模版
部署架构
否
下拉选择，默认选第一个
付费类型
计费方式
是
预付费（按月）
输入吞吐额度
购买的输入 TPM 容量
是
基准输入 TPM 的整数倍（kTPM）
输出吞吐额度
购买的输出 TPM 容量
是
基准输出 TPM 的整数倍（kTPM）
购买时长
购买周期
是
1-12（整数，月）
自动续费
到期自动续费
否
开/关
单次续费时长
开启自动续费时必填
是
1-12（整数，月）
## 容量规划
DTU 按输入/输出 TPM 售卖，所购 TPM 即每分钟可处理的最大 token 量。容量规划的目标是：依据业务峰值 token 需求推算需购买的倍数，再通过压测验证实际可承载的并发与延迟是否达标。各模型的基准输入/输出 TPM 与标准工况性能参考见上方表格。
建议使用 evalscope 等压测工具，按真实业务场景（输入/输出长度、并发数、延迟要求）压测，再对照定价表确定购买倍数。
### 容量规划方法
1. 梳理业务峰值指标：典型请求的输入/输出 token 长度、目标并发数，以及对首字延迟（TTFT）和每 token 生成延迟（TPOT）的要求。
2. 估算峰值吞吐需求：峰值输入 TPM ≈ 并发数 × 单请求输入长度 ÷ 单请求处理时长（分钟）；峰值输出 TPM ≈ 并发数 × 单请求输出长度 ÷ 单请求生成时长（分钟）。
3. 计算购买倍数：输入倍数 = ⌈峰值输入 TPM ÷ 基准输入 TPM⌉，输出倍数 = ⌈峰值输出 TPM ÷ 基准输出 TPM⌉；DTU 输入与输出需同增同减，取两者较大值作为最终倍数。
4. 压测验证：按计算倍数购买后，用 evalscope 在目标工况下复测，确认实际吞吐与延迟满足业务要求。若延迟偏高，可在 TPM 余量内提高并发以提升有效吞吐。
如对延迟要求不严格，可在不超过所购 TPM 上限的前提下，通过提高并发数提升实际吞吐量。
业务测算示例：
某模型基准输入 656K TPM、基准输出 82K TPM。经业务峰值梳理，峰值输入约 1,300K TPM、峰值输出约 160K TPM。计算倍数：输入 ⌈1300÷656⌉=2、输出 ⌈160÷82⌉=2，取较大值 2 倍，购买输入 1,312K TPM（656K×2）+ 输出 164K TPM（82K×2）；随后用 evalscope 在峰值工况压测，确认延迟达标。若业务量翻倍，峰值输入约 2,600K、输出约 320K，则输入 ⌈2600÷656⌉=4、输出 ⌈320÷82⌉=4，需购买 4 倍：输入 2,624K TPM（656K×4）+ 输出 328K TPM（82K×4），并复测。
测算公式：
```
购买倍数 = max(⌈峰值输入 TPM ÷ 基准输入 TPM⌉, ⌈峰值输出 TPM ÷ 基准输出 TPM⌉)
```
## 部署管理与扩缩容
部署列表展示已购买的输入/输出 TPM 额度，详情页展示 TPM 容量详情。
扩缩容：在部署列表中点击「扩缩容」修改输入/输出 TPM 容量。扩容显示需补交金额，缩容显示预计退款金额。输入和输出需同增或同减，不能一增一减。仅运行中的部署可操作。
续费：预付费包月到期可续费，详情页续费按钮进入续费流程，支持自动续费开关与单次续费时长。
## 常见问题
**DTU 部署和 Token 按量计费有什么区别？**
Token 按量计费在共享资源池上按 token 用量计费；DTU 为独占 GPU 资源，按输入/输出 TPM 计费，适合需要稳定吞吐和独占部署的场景。
**DTU 部署支持哪些计费方式？**
仅支持预付费（按月）计费。
**如何查看已购 TPM 的使用情况？**
在百炼控制台模型部署的部署列表和详情页可查看已购的输入/输出 TPM 额度，详见[模型部署文档](raw/model-user-guide/model-deployment-1/model-deployment-introduction.md)。
skills/bailian-docs-llm-wiki/raw/model-user-guide/model-deployment-1/model-deployment-introduction.md
+13 -1
View File
@@ -1512,7 +1512,7 @@ MU5
**元/百万Token**
千问3.5-27B 邀测中
千问3.5-27B
qwen3.5-27b
@@ -1560,6 +1560,18 @@ qwen3-8b
思考模式：¥5
Qwen3-4B-Instruct-2507
qwen3-4b-instruct-2507
非思考模式：¥0.3
思考模式：¥0.3
非思考模式：¥1.2
思考模式：¥3
千问3-VL-8B-Instruct
qwen3-vl-8b-instruct
skills/bailian-docs-llm-wiki/raw/model-user-guide/release-notes/newly-released-models.md
+10 -2
View File
@@ -12,6 +12,14 @@
**功能说明**
视频生成
2026-08-27
`kling/kling-v3-turbo-video-generation`
可灵V3 Turbo极速视频生成模型，固定音画同出，支持文生视频、图生视频（首帧）及多镜头生成。
文本生成、深度思考、视觉理解
2026-08-26
@@ -6484,7 +6492,7 @@ Qwen3系列Plus模型，实现思考模式和非思考模式的有效融合，
2026-08-26
国际
全球
`qwen3.8-flash`
@@ -7396,7 +7404,7 @@ Qwen3系列Plus模型，实现思考模式和非思考模式的有效融合，
2026-08-26
国际
全球
`qwen3.8-flash`
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/deepseek-v4-flash-us.md
+8 -6
View File
@@ -214,6 +214,8 @@ Function Calling
本文仅展示模型调用原价，不包含限时优惠等活动信息，请前往[百炼控制台](https://bailian.console.aliyun.com/cn-beijing?tab=model#/model-market/all)查看活动优惠。
**说明**闲时为东八区 22:00 至次日 08:00，其余时段为忙时，以账单时间为准。
#### 美国（弗吉尼亚）
部署范围：美国
@@ -224,37 +226,37 @@ Function Calling
单位
输入
输入（闲时）
1.604
每百万tokens
输入
输入（忙时）
3.208
每百万tokens
输出
输出（闲时）
4.813
每百万tokens
输出
输出（忙时）
9.625
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.16
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.321
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/deepseek-v4-flash.md
+32 -30
View File
@@ -596,6 +596,8 @@ Function Calling
本文仅展示模型调用原价，不包含限时优惠等活动信息，请前往[百炼控制台](https://bailian.console.aliyun.com/cn-beijing?tab=model#/model-market/all)查看活动优惠。
**说明**闲时为东八区 22:00 至次日 08:00，其余时段为忙时，以账单时间为准。
#### 华北2（北京）
计费项
@@ -604,37 +606,37 @@ Function Calling
单位
输入
输入（闲时）
1.5
每百万tokens
输入
输入（忙时）
3
每百万tokens
输出
输出（闲时）
4.5
每百万tokens
输出
输出（忙时）
9
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.15
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.3
@@ -650,37 +652,37 @@ Function Calling
单位
输入
输入（闲时）
1.604
每百万tokens
输入
输入（忙时）
3.208
每百万tokens
输出
输出（闲时）
4.813
每百万tokens
输出
输出（忙时）
9.625
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.16
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.321
@@ -696,37 +698,37 @@ Function Calling
单位
输入
输入（闲时）
1.5
每百万tokens
输入
输入（忙时）
3
每百万tokens
输出
输出（闲时）
4.5
每百万tokens
输出
输出（忙时）
9
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.15
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.3
@@ -742,37 +744,37 @@ Function Calling
单位
输入
输入（闲时）
1.5
每百万tokens
输入
输入（忙时）
3
每百万tokens
输出
输出（闲时）
4.5
每百万tokens
输出
输出（忙时）
9
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.15
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.3
@@ -788,37 +790,37 @@ Function Calling
单位
输入
输入（闲时）
0.212
每百万tokens
输入
输入（忙时）
0.424
每百万tokens
输出
输出（闲时）
0.636
每百万tokens
输出
输出（忙时）
1.272
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.021
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.042
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/deepseek-v4-pro.md
+34 -30
View File
@@ -608,6 +608,8 @@ Function Calling
本文仅展示模型调用原价，不包含限时优惠等活动信息，请前往[百炼控制台](https://bailian.console.aliyun.com/cn-beijing?tab=model#/model-market/all)查看活动优惠。
**说明**闲时为东八区 22:00 至次日 08:00，其余时段为忙时，以账单时间为准。
#### 华北2（北京）
计费项
@@ -616,37 +618,37 @@ Function Calling
单位
输入
输入（闲时）
4.5
每百万tokens
输入
输入（忙时）
9
每百万tokens
输出
输出（闲时）
13.5
每百万tokens
输出
输出（忙时）
27
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.45
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.9
@@ -662,37 +664,37 @@ Function Calling
单位
输入
输入（闲时）
4.813
每百万tokens
输入
输入（忙时）
9.625
每百万tokens
输出
输出（闲时）
14.438
每百万tokens
输出
输出（忙时）
28.875
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.481
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.963
@@ -708,37 +710,37 @@ Function Calling
单位
输入
输入（闲时）
4.5
每百万tokens
输入
输入（忙时）
9
每百万tokens
输出
输出（闲时）
13.5
每百万tokens
输出
输出（忙时）
27
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.45
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.9
@@ -754,37 +756,37 @@ Function Calling
单位
输入
输入（闲时）
4.5
每百万tokens
输入
输入（忙时）
9
每百万tokens
输出
输出（闲时）
13.5
每百万tokens
输出
输出（忙时）
27
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.45
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.9
@@ -800,37 +802,37 @@ Function Calling
单位
输入
输入（闲时）
0.636
每百万tokens
输入
输入（忙时）
1.272
每百万tokens
输出
输出（闲时）
1.908
每百万tokens
输出
输出（忙时）
3.816
每百万tokens
输入（缓存命中）
输入（缓存命中-闲时）
0.064
每百万tokens
输入（缓存命中）
输入（缓存命中-忙时）
0.127
@@ -838,6 +840,8 @@ Function Calling
#### 限流
本模型采用[动态限流](raw/model-user-guide/get-started-with-models/quota-management.md)机制，TPM 按百炼月消费分档调整。
#### 华北2（北京）
参数
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/kimi-k3.md
+2
View File
@@ -364,6 +364,8 @@ Function Calling
## 限流
本模型采用[动态限流](raw/model-user-guide/get-started-with-models/quota-management.md)机制，TPM 按百炼月消费分档调整。
#### 华北2（北京）
参数
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/qwen3-8-2-4t-a95b.md
+2
View File
@@ -174,6 +174,8 @@ Function Calling
## 限流
本模型采用[动态限流](raw/model-user-guide/get-started-with-models/quota-management.md)机制，TPM 按百炼月消费分档调整。
#### 华北2（北京）
参数
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/qwen3-8-27b.md
+2
View File
@@ -176,6 +176,8 @@ Function Calling
## 限流
本模型采用[动态限流](raw/model-user-guide/get-started-with-models/quota-management.md)机制，TPM 按百炼月消费分档调整。
#### 华北2（北京）
参数
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/qwen3-8-flash.md
+499
View File
@@ -0,0 +1,499 @@
# qwen3.8-flash
Qwen3.8-Flash 是通义千问最新推出的多模态大模型，兼具强大的理解与生成能力和出色的响应速度。模型原生支持百万级上下文窗口，能够一次性处理超长文档、代码仓库和复杂对话。在编程辅助、智能体协作、图文理解等场景中表现尤为出色——无论是自动修复代码、操作桌面应用，还是分析图表与长视频，都能给出准确、高质量的结果。同时兼容 OpenAI 与 Anthropic 主流接口协议，可无缝接入 Claude Code、Codex 等开发者工具，轻松构建高并发应用与智能工作流。凭借优异的性能与极具竞争力的推理成本，Qwen3.8-Flash 是开发者和企业在 AI 应用中兼顾效果与效率的理想选择。
模型调用 ID（`model` 参数取值）：`qwen3.8-flash`
## 模型能力
#### 华北2（北京）
能力项
支持情况
能力项
支持情况
输入模态
**Image** **Text** **Video**
输出模态
**Text**
模型体验
支持
Function Calling
支持
结构化输出
支持
联网搜索
支持
前缀续写
支持
上下文缓存
支持
批量推理
支持
模型调优
不支持
#### 新加坡
部署范围：国际
能力项
支持情况
能力项
支持情况
输入模态
**Image** **Text** **Video**
输出模态
**Text**
模型体验
支持
Function Calling
支持
结构化输出
支持
联网搜索
支持
前缀续写
支持
上下文缓存
支持
批量推理
不支持
模型调优
不支持
#### 德国（法兰克福）
部署范围：全球
能力项
支持情况
能力项
支持情况
输入模态
**Image** **Text** **Video**
输出模态
**Text**
模型体验
支持
Function Calling
支持
结构化输出
支持
联网搜索
不支持
前缀续写
支持
上下文缓存
支持
批量推理
不支持
模型调优
不支持
#### 日本（东京）
部署范围：全球
能力项
支持情况
能力项
支持情况
输入模态
**Image** **Text** **Video**
输出模态
**Text**
模型体验
支持
Function Calling
支持
结构化输出
支持
联网搜索
不支持
前缀续写
支持
上下文缓存
支持
批量推理
不支持
模型调优
不支持
## 上下文限制
参数
值
参数
值
最大输入长度
991808
最大输出长度
131072
最大输入长度（思考模式下）
983616
最大输出长度（思考模式下）
131072
上下文长度
1000000
最大思维链长度
262144
## 模型价格
本文仅展示模型调用原价，不包含限时优惠等活动信息，请前往[百炼控制台](https://bailian.console.aliyun.com/cn-beijing?tab=model#/model-market/all)查看活动优惠。
#### 华北2（北京）
计费项
价格（元）
单位
输入
0.8
每百万tokens
输出
2.7
每百万tokens
输入（缓存命中）
0.1
每百万tokens
输入（Batch File）
0.4
每百万tokens
输出（Batch File）
1.35
每百万tokens
显式缓存创建
1.25
每百万tokens
显式缓存命中
0.1
每百万tokens
输入（Batch Chat)
0.8
每百万tokens
输出（Batch Chat)
2.7
每百万tokens
#### 新加坡
部署范围：国际
计费项
价格（元）
单位
输入
1.094
每百万tokens
输出
3.427
每百万tokens
输入（缓存命中）
0.117
每百万tokens
显式缓存创建
1.458
每百万tokens
显式缓存命中
0.117
每百万tokens
#### 德国（法兰克福）
部署范围：全球
计费项
价格（元）
单位
输入
0.8
每百万tokens
输出
2.7
每百万tokens
输入（缓存命中）
0.1
每百万tokens
显式缓存创建
1.25
每百万tokens
显式缓存命中
0.1
每百万tokens
#### 日本（东京）
部署范围：全球
计费项
价格（元）
单位
输入
0.8
每百万tokens
输出
2.7
每百万tokens
输入（缓存命中）
0.1
每百万tokens
显式缓存创建
1.25
每百万tokens
显式缓存命中
0.1
每百万tokens
## 限流
本模型采用[动态限流](raw/model-user-guide/get-started-with-models/quota-management.md)机制，TPM 按百炼月消费分档调整。
#### 华北2（北京）
参数
值
RPM（每分钟请求数）
30,000
TPM（每分钟tokens）
5,000,000
#### 新加坡
部署范围：国际
参数
值
RPM（每分钟请求数）
15,000
TPM（每分钟tokens）
2,000,000
#### 德国（法兰克福）
部署范围：全球
参数
值
RPM（每分钟请求数）
30,000
TPM（每分钟tokens）
5,000,000
#### 日本（东京）
部署范围：全球
参数
值
RPM（每分钟请求数）
30,000
TPM（每分钟tokens）
5,000,000
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/qwen3-8-max.md
+2
View File
@@ -528,6 +528,8 @@ Function Calling
## 限流
本模型采用[动态限流](raw/model-user-guide/get-started-with-models/quota-management.md)机制，TPM 按百炼月消费分档调整。
#### 华北2（北京）
参数
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/tongyi-xiaomi-analysis-flash.md
+7 -1
View File
@@ -46,7 +46,7 @@ Function Calling
上下文缓存
不支持
支持
批量推理
@@ -102,6 +102,12 @@ Function Calling
每百万tokens
输入（缓存命中）
0.04
每百万tokens
## 限流
#### 华北2（北京）
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/tongyi-xiaomi-analysis-pro.md
+7 -1
View File
@@ -46,7 +46,7 @@ Function Calling
上下文缓存
不支持
支持
批量推理
@@ -102,6 +102,12 @@ Function Calling
每百万tokens
输入（缓存命中）
0.2
每百万tokens
## 限流
#### 华北2（北京）
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-text-generation/deepseek-v4-pro-by-vanchin.md → skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-video-generation/kling-v3-turbo-video-generation.md
+18 -36
View File
@@ -1,10 +1,10 @@
# vanchin/deepseek-v4-pro
# kling/kling-v3-turbo-video-generation
DeepSeek-V4系列是强大的混合专家（MoE）语言模型，包含DeepSeek-V4-Pro（1.6T总参数，49B激活参数）。支持高达100万（1M）token的上下文长度，是在超过32T高质量多样化token上预训练的开源模型。
极速视频生成，固定音画同出，支持文生视频、图生视频（首帧）及多镜头生成。
## 推理服务供应商
`vanchin/deepseek-v4-pro`模型的推理服务供应商为快手万擎。
`kling/kling-v3-turbo-video-generation`模型的推理服务供应商为可灵AI。
## 模型能力
@@ -18,11 +18,11 @@ DeepSeek-V4系列是强大的混合专家（MoE）语言模型，包含DeepSeek-
输入模态
**Text**
**Image** **Text**
输出模态
**Text**
**Video**
模型体验
@@ -30,11 +30,11 @@ DeepSeek-V4系列是强大的混合专家（MoE）语言模型，包含DeepSeek-
Function Calling
支持
不支持
结构化输出
支持
不支持
联网搜索
@@ -46,7 +46,7 @@ Function Calling
上下文缓存
支持
不支持
批量推理
@@ -68,23 +68,15 @@ Function Calling
最大输入长度
1048576
—
最大输出长度
393216
—
上下文长度
1048576
最大输入长度（思考模式下）
1048576
最大输出长度（思考模式下）
393216
—
## 模型价格
@@ -98,23 +90,17 @@ Function Calling
单位
输入
视频生成（720P）
12
0.8
每百万tokens
每秒
输出
视频生成（1080P）
24
1.0
每百万tokens
输入（缓存命中）
1
每百万tokens
每秒
## 限流
@@ -126,8 +112,4 @@ Function Calling
RPM（每分钟请求数）
60
TPM（每分钟tokens）
300,000
300
skills/bailian-docs-llm-wiki/raw/model-user-guide/support/model-studio-model-list/model-list-video-generation/pixverse-v6-r2v.md
-151
View File
@@ -1,151 +0,0 @@
# pixverse/pixverse-v6-r2v
V6是PixVerse在26年3月底推出的新模型，r2v（多主体参考生成视频）模型全球排名第二，输入2-7张图像，智能融合不同主体，适合复杂的中景、远景视频镜头。同时拥有t2v（文字生成视频）的提示词控制能力，和it2v（图片生成视频）的一致性保持能力、更强的情绪表现力、和更流畅的高速运动画面。支持15秒长视频、音乐和视频直出、支持多种语言文字。在电商产品特写、广告宣传片、模拟c4d建模展示产品结构等场景下可一键直出。
## 推理服务供应商
`pixverse/pixverse-v6-r2v`模型的推理服务供应商为PixVerse。
## 模型能力
能力项
支持情况
能力项
支持情况
输入模态
**Image** **Text**
输出模态
**Video**
模型体验
不支持
Function Calling
不支持
结构化输出
不支持
联网搜索
不支持
前缀续写
不支持
上下文缓存
不支持
批量推理
不支持
模型调优
不支持
## 上下文限制
参数
值
参数
值
最大输入长度
—
最大输出长度
—
上下文长度
—
## 模型价格
本文仅展示模型调用原价，不包含限时优惠等活动信息，请前往[百炼控制台](https://bailian.console.aliyun.com/cn-beijing?tab=model#/model-market/all)查看活动优惠。
#### 华北2（北京）
计费项
价格（元）
单位
视频生成（360P）
0.21
每秒
视频生成（540P）
0.27
每秒
视频生成（720P）
0.36
每秒
视频生成（1080P）
0.68
每秒
视频生成（360P 无声）
0.15
每秒
视频生成（540P 无声）
0.21
每秒
视频生成（720P 无声）
0.27
每秒
视频生成（1080P 无声）
0.53
每秒
## 限流
#### 华北2（北京）
参数
值
RPM（每分钟请求数）
300
skills/bailian-docs-llm-wiki/raw/model-user-guide/test-1/model-pricing.md
+121 -5
View File
@@ -2508,9 +2508,9 @@ qwen3.8-flash
0<Token≤1M
1元
0.8元
3元
2.7元
100万Token
@@ -2990,7 +2990,7 @@ qwen3.8-flash
0<Token≤1M
1.167元
1.094元
3.427元
@@ -3174,6 +3174,20 @@ qwen-flash-2025-07-28
> **思维链+回答**
qwen3.8-flash
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
全球
非思考和思考模式
0<Token≤1M
0.8元
2.7元
qwen3.7-flash
> 当前能力等同于qwen3.7-flash-2026-07-15
@@ -3414,6 +3428,20 @@ qwen-flash-2025-07-28
> **思维链+回答**
qwen3.8-flash
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
全球
非思考和思考模式
0<Token≤1M
0.8元
2.7元
qwen3.7-flash
> 当前能力等同于qwen3.7-flash-2026-07-15
@@ -6590,6 +6618,8 @@ qwen-deep-research-2025-12-15
tongyi-xiaomi-analysis-flash
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
0.2元
0.4元
@@ -6598,6 +6628,8 @@ tongyi-xiaomi-analysis-flash
tongyi-xiaomi-analysis-pro
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
1.0元
2.7元
@@ -9298,6 +9330,16 @@ vanchin/deepseek-v4-pro-0813
有效期：自开通百炼/模型发布/申请通过之日起90天内（以较晚者为准）
kimi-k3
仅思考模式
20元
100元
100万Token
kimi-k2.7-code
仅思考模式
@@ -9360,6 +9402,26 @@ Moonshot-Kimi-K2-Instruct
**输出单价（每百万Token）**
kimi-k3
全球
仅思考模式
20元
100元
kimi-k3
国际
仅思考模式
21.875元
109.376元
kimi-k2.7-code
全球
@@ -9392,6 +9454,16 @@ kimi-k2.5
**输出单价（每百万Token）**
kimi-k3
全球
仅思考模式
20元
100元
kimi-k2.7-code
全球
@@ -9424,6 +9496,18 @@ kimi-k2.5
**输出单价（每百万Token）**
kimi-k3
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
全球
仅思考模式
20元
100元
kimi-k2.7-code
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
@@ -9458,6 +9542,16 @@ kimi-k2.5
**输出单价（每百万Token）**
kimi-k3
国际
仅思考模式
21.875元
109.376元
kimi-k2.7-code
国际
@@ -9484,6 +9578,16 @@ kimi-k2.7-code
**免费额度**[（注）](https://help.aliyun.com/zh/model-studio/new-free-quota#977b13081ab56)
kimi/kimi-k3
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
20元
100元
无
kimi/kimi-k2.7-code-highspeed
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
@@ -9492,8 +9596,6 @@ kimi/kimi-k2.7-code-highspeed
54元
无
kimi/kimi-k2.7-code
> [上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)享有折扣
@@ -16232,6 +16334,20 @@ pixverse/pixverse-upscale
**免费额度**[**（注）**](https://help.aliyun.com/zh/model-studio/new-free-quota#977b13081ab56)
kling/kling-v3-turbo-video-generation
有声视频
720P
0.8元/秒
无免费额度
1080P
1.0元/秒
kling/kling-v3-video-generation
无声视频
skills/bailian-docs-llm-wiki/raw/model-user-guide/test-1/model-training-and-deployment-billing.md
+13 -1
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@@ -1864,7 +1864,7 @@ MU5
**元/百万Token**
千问3.5-27B 邀测中
千问3.5-27B
qwen3.5-27b
@@ -1912,6 +1912,18 @@ qwen3-8b
思考模式：¥5
Qwen3-4B-Instruct-2507
qwen3-4b-instruct-2507
非思考模式：¥0.3
思考模式：¥0.3
非思考模式：¥1.2
思考模式：¥3
千问3-VL-8B-Instruct
qwen3-vl-8b-instruct
skills/bailian-docs-llm-wiki/raw/model-user-guide/token-plan-guide/coding-plan-guide/coding-plan-faq.md
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@@ -30,7 +30,7 @@
2. 通过精简输入、切换更长上下文的模型等方式可以避免输入超限问题，详见[各模型的上下文长度是多少？超出上下文长度如何处理？](https://help.aliyun.com/zh/model-studio/coding-plan-faq#d643189452x8x)
3. 若使用OpenCode，请配置`limit`来限制上下文长度，详见[OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md)。
3. 若使用OpenCode，请配置`limit`来限制上下文长度，详见[OpenCode](https://help.aliyun.com/zh/model-studio/opencode)。
**401 invalid access token or token expired**
@@ -217,13 +217,13 @@ qwen3-coder-plus
不支持思考模式
**解决方案**：打开 OpenCode 配置文件 `opencode.json`，将对应模型的 `options.thinking.budgetTokens` 值调整为不超过该模型的上限值。对于不支持思考模式的模型（qwen3-coder-next、qwen3-coder-plus），需要移除 `options.thinking` 配置项。详情请参见[OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md)。
**解决方案**：打开 OpenCode 配置文件 `opencode.json`，将对应模型的 `options.thinking.budgetTokens` 值调整为不超过该模型的上限值。对于不支持思考模式的模型（qwen3-coder-next、qwen3-coder-plus），需要移除 `options.thinking` 配置项。详情请参见[OpenCode](https://help.aliyun.com/zh/model-studio/opencode)。
### OpenCode 报错`InternalError.Algo.InvalidParameter: Range of max_tokens should be [1, xxxx]`怎么办？
**原因**：配置文件`opencode.json`中 `limit.output` 的值超过了当前模型支持的最大输出Token数（`max_tokens`）。
**解决方案**：请根据[OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md)修改配置文件 `opencode.json`，调整对应模型的`limit`值。
**解决方案**：请根据[OpenCode](https://help.aliyun.com/zh/model-studio/opencode)修改配置文件 `opencode.json`，调整对应模型的`limit`值。
### Coding Plan 模型支持开启思考模式吗？
@@ -261,7 +261,7 @@ Coding Plan 支持深度思考的模型多为默认支持思考模式，常见
#### OpenCode
参见[OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md)，配置options参数为：
参见[OpenCode](https://help.aliyun.com/zh/model-studio/opencode)，配置options参数为：
```
{
skills/bailian-docs-llm-wiki/raw/model-user-guide/token-plan-guide/coding-plan-guide/coding-plan.md
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@@ -84,7 +84,7 @@ RAM 子账号授权步骤
AI 终端编码助手，支持自然语言编程
![](https://unpkg.com/@lobehub/icons-static-svg@latest/icons/opencode.svg)**[OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md)**
![](https://unpkg.com/@lobehub/icons-static-svg@latest/icons/opencode.svg)**[OpenCode](https://help.aliyun.com/zh/model-studio/opencode)**
开源 AI 编程代理工具
skills/bailian-docs-llm-wiki/raw/model-user-guide/token-plan-guide/token-plan-best-practice/add-vision-skill.md
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@@ -127,7 +127,7 @@ mkdir -p .opencode/agents
在该目录下创建`image-analyzer.md`文件，并写入以下内容：
**说明**model 字段必须使用 OpenCode 配置文件中定义的 provider 和模型名称。参考 [OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md) 文档的配置示例，应为`bailian-token-plan/qwen3.7-plus`。
**说明**model 字段必须使用 OpenCode 配置文件中定义的 provider 和模型名称。参考 [OpenCode](https://help.aliyun.com/zh/model-studio/opencode) 文档的配置示例，应为`bailian-token-plan/qwen3.7-plus`。
```
---
skills/bailian-docs-llm-wiki/raw/model-user-guide/token-plan-guide/token-plan-personal/token-plan-personal-quick-start.md
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@@ -48,7 +48,7 @@ Anthropic 兼容
AI 终端编码助手，支持自然语言编程
![](https://unpkg.com/@lobehub/icons-static-svg@latest/icons/opencode.svg)**[OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md)**
![](https://unpkg.com/@lobehub/icons-static-svg@latest/icons/opencode.svg)**[OpenCode](https://help.aliyun.com/zh/model-studio/opencode)**
开源 AI 编程代理工具
skills/bailian-docs-llm-wiki/raw/model-user-guide/token-plan-guide/token-plan-team-edition/token-plan-team-quickstart.md
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@@ -59,7 +59,7 @@ Anthropic 兼容
AI 终端编码助手，支持自然语言编程
![](https://unpkg.com/@lobehub/icons-static-svg@latest/icons/opencode.svg)**[OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md)**
![](https://unpkg.com/@lobehub/icons-static-svg@latest/icons/opencode.svg)**[OpenCode](https://help.aliyun.com/zh/model-studio/opencode)**
开源 AI 编程代理工具
skills/bailian-docs-llm-wiki/raw/model-user-guide/use-cases/explicit-cache-guide.md
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@@ -97,7 +97,7 @@ opencode run -m "bailian/qwen3.7-max" "..."
- Token Plan 团队版：`https://token-plan.cn-beijing.maas.aliyuncs.com/apps/anthropic/v1`
- Coding Plan：`https://coding.dashscope.aliyuncs.com/apps/anthropic/v1`
详见 [OpenCode](raw/model-user-guide/use-chat-client-or-development-tool/opencode.md)。
详见 [OpenCode](https://help.aliyun.com/zh/model-studio/opencode)。
#### OpenClaw
skills/bailian-docs-llm-wiki/raw/model-user-guide/use-cases/third-party-model-integration-tutorial/glm-zhipu.md
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@@ -638,7 +638,7 @@ ZHIPU/GLM-5
上下文缓存类型为隐式缓存，自动开启，与阿里云百炼的[隐式缓存](https://help.aliyun.com/zh/model-studio/context-cache)服务有以下不同：
- 缓存最少 Token 数为 512（百炼为 256）。
- 缓存最少 Token 数为 512（百炼为 1024）。
## 参数默认值
skills/bailian-docs-llm-wiki/raw/model-user-guide/use-cases/third-party-model-integration-tutorial/minimax-api-by-minimax.md
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@@ -637,7 +637,7 @@ MiniMax/MiniMax-M2.1
上下文缓存类型为隐式缓存，自动开启，与阿里云百炼的[隐式缓存](https://help.aliyun.com/zh/model-studio/context-cache)服务有以下不同：
- MiniMax/MiniMax-M3、MiniMax/MiniMax-M2.7 命中缓存的输入 Token 折扣为 20%，MiniMax/MiniMax-M2.5、MiniMax/MiniMax-M2.1 折扣为 10%；缓存最少 Token 数为 512（百炼为 256）。
- MiniMax/MiniMax-M3、MiniMax/MiniMax-M2.7 命中缓存的输入 Token 折扣为 20%，MiniMax/MiniMax-M2.5、MiniMax/MiniMax-M2.1 折扣为 10%；缓存最少 Token 数为 512（百炼为 1024）。
- MiniMax/MiniMax-M3 不支持 `n` 参数（即不支持一次生成多条候选回复），`tool_choice` 仅支持 `none` 和 `auto`。
## 参数默认值
skills/bailian-docs-llm-wiki/raw/model-user-guide/use-cases/third-party-model-integration-tutorial/stepfun.md
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@@ -1,615 +0,0 @@
# Stepfun-阶跃星辰
本文档介绍如何在阿里云百炼平台调用阶跃星辰（Stepfun）直供的 Step 系列模型推理服务。
**重要**本文档仅适用于华北2（北京）地域。如需使用模型，需使用华北2（北京）地域的[API Key](https://bailian.console.aliyun.com/?tab=model#/api-key)。
## 快速开始
stepfun/step-3.7-flash 是阶跃星辰直供的多模态推理模型，**默认关闭思考模式**，您可以通过设置 `enable_thinking` 为 `true` 开启思考模式。开启后，模型的推理过程通过 `reasoning_content` 字段返回，您可以通过 `reasoning_effort` 参数（可选值：`low`、`medium`、`high`）控制推理深度。运行以下代码快速调用 stepfun/step-3.7-flash 模型。
需要已[获取与配置 API Key](raw/model-api-reference/preparations/get-api-key.md)并完成[配置API Key到环境变量](https://help.aliyun.com/zh/model-studio/configure-api-key-through-environment-variables)。如果通过SDK调用，需要[安装SDK](raw/model-api-reference/preparations/install-sdk.md)。
#### Python
### 示例代码
```
from openai import OpenAI
import os
# 初始化OpenAI客户端
client = OpenAI(
# 如果没有配置环境变量，请用阿里云百炼API Key替换：api_key="sk-xxx"
api_key=os.getenv("DASHSCOPE_API_KEY"),
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
)
messages = [{"role": "user", "content": "9.9和9.11哪个大？"}]
completion = client.chat.completions.create(
model="stepfun/step-3.7-flash",
messages=messages,
stream=True,
stream_options={
"include_usage": True
},
extra_body={
"enable_thinking": True
}
)
reasoning_content = "" # 完整思考过程
answer_content = "" # 完整回复
is_answering = False # 是否进入回复阶段
print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
for chunk in completion:
if not chunk.choices:
print("\n" + "=" * 20 + "Token 消耗" + "=" * 20 + "\n")
print(chunk.usage)
continue
delta = chunk.choices[0].delta
# 只收集思考内容
if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
if not is_answering:
print(delta.reasoning_content, end="", flush=True)
reasoning_content += delta.reasoning_content
# 收到content，开始进行回复
if hasattr(delta, "content") and delta.content:
if not is_answering:
print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
is_answering = True
print(delta.content, end="", flush=True)
answer_content += delta.content
```
### 返回结果
```
====================思考过程====================
好的，用户问的是9.9和9.11哪个大。首先我需要比较这两个小数。
9.9可以写成9.90，而9.11就是9.11。
比较小数部分：90 > 11，所以9.9 > 9.11。
====================完整回复====================
9.9 更大。
将两个数对齐小数位：9.9 = 9.90，而 9.11 = 9.11。比较小数部分，0.90 > 0.11，因此 **9.9 > 9.11**。
====================Token 消耗====================
CompletionUsage(completion_tokens=85, prompt_tokens=10, total_tokens=95, prompt_tokens_details={'cached_tokens': 0})
```
#### Node.js
### 示例代码
```
import OpenAI from "openai";
import process from 'process';
// 初始化OpenAI客户端
const openai = new OpenAI({
// 如果没有配置环境变量，请用阿里云百炼API Key替换：apiKey: "sk-xxx"
apiKey: process.env.DASHSCOPE_API_KEY,
// 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
baseURL: 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1'
});
let reasoningContent = ''; // 完整思考过程
let answerContent = ''; // 完整回复
let isAnswering = false; // 是否进入回复阶段
async function main() {
try {
const messages = [{ role: 'user', content: '9.9和9.11哪个大？' }];
const stream = await openai.chat.completions.create({
model: 'stepfun/step-3.7-flash',
messages,
stream: true,
stream_options: {
include_usage: true
},
enable_thinking: true
});
console.log('\n' + '='.repeat(20) + '思考过程' + '='.repeat(20) + '\n');
for await (const chunk of stream) {
if (!chunk.choices?.length) {
console.log('\n' + '='.repeat(20) + 'Token 消耗' + '='.repeat(20) + '\n');
console.log(chunk.usage);
continue;
}
const delta = chunk.choices[0].delta;
// 只收集思考内容
if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
if (!isAnswering) {
process.stdout.write(delta.reasoning_content);
}
reasoningContent += delta.reasoning_content;
}
// 收到content，开始进行回复
if (delta.content !== undefined && delta.content) {
if (!isAnswering) {
console.log('\n' + '='.repeat(20) + '完整回复' + '='.repeat(20) + '\n');
isAnswering = true;
}
process.stdout.write(delta.content);
answerContent += delta.content;
}
}
} catch (error) {
console.error('Error:', error);
}
}
main();
```
### 返回结果
```
====================思考过程====================
好的，用户问的是9.9和9.11哪个大。首先我需要比较这两个小数。
9.9可以写成9.90，而9.11就是9.11。
比较小数部分：90 > 11，所以9.9 > 9.11。
====================完整回复====================
9.9 更大。
将两个数对齐小数位：9.9 = 9.90，而 9.11 = 9.11。比较小数部分，0.90 > 0.11，因此 **9.9 > 9.11**。
====================Token 消耗====================
{ prompt_tokens: 10, completion_tokens: 85, total_tokens: 95, prompt_tokens_details: { cached_tokens: 0 } }
```
#### HTTP
### 示例代码
#### curl
```
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "stepfun/step-3.7-flash",
"messages": [
{
"role": "user",
"content": "9.9和9.11哪个大？"
}
],
"stream": true,
"stream_options": {
"include_usage": true
},
"enable_thinking": true
}'
```
## 多模态调用示例
stepfun/step-3.7-flash 不仅支持纯文本对话，还具备多模态理解能力，支持图像和视频输入。
### 图像理解
图像理解功能让模型能够识别和分析图像内容。支持通过公网 URL 或 Base64 编码传入图像。图像文件的限制请参见[图像限制](raw/model-user-guide/use-cases/third-party-model-integration-tutorial/stepfun.md)。
#### Python
```
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="stepfun/step-3.7-flash",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "图中描绘的是什么景象?"},
{
"type": "image_url",
"image_url": {
"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg",
"detail": "high"
}
}
]
}
]
)
# 输出思考过程
if hasattr(completion.choices[0].message, 'reasoning_content') and completion.choices[0].message.reasoning_content:
print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
print(completion.choices[0].message.reasoning_content)
# 输出回复内容
print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
print(completion.choices[0].message.content)
```
#### Node.js
```
import OpenAI from "openai";
import process from 'process';
const openai = new OpenAI({
apiKey: process.env.DASHSCOPE_API_KEY,
// 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
baseURL: 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1'
});
const completion = await openai.chat.completions.create({
model: 'stepfun/step-3.7-flash',
messages: [
{
role: 'user',
content: [
{ type: 'text', text: '图中描绘的是什么景象?' },
{
type: 'image_url',
image_url: {
url: 'https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg',
detail: 'high'
}
}
]
}
]
});
// 输出思考过程
if (completion.choices[0].message.reasoning_content) {
console.log('\n' + '='.repeat(20) + '思考过程' + '='.repeat(20) + '\n');
console.log(completion.choices[0].message.reasoning_content);
}
// 输出回复内容
console.log('\n' + '='.repeat(20) + '完整回复' + '='.repeat(20) + '\n');
console.log(completion.choices[0].message.content);
```
#### HTTP
#### curl
```
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "stepfun/step-3.7-flash",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "图中描绘的是什么景象?"
},
{
"type": "image_url",
"image_url": {
"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg",
"detail": "high"
}
}
]
}
]
}'
```
### 视频理解
视频理解功能让模型能够识别和分析视频内容。支持通过公网 URL 传入视频。视频文件的限制请参见[视频限制](raw/model-user-guide/use-cases/third-party-model-integration-tutorial/stepfun.md)。
#### Python
```
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="stepfun/step-3.7-flash",
messages=[
{
"role": "user",
"content": [
{
"type": "video_url",
"video_url": {
"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4"
}
},
{
"type": "text",
"text": "这段视频的内容是什么?"
}
]
}
]
)
print(completion.choices[0].message.content)
```
#### Node.js
```
import OpenAI from "openai";
const openai = new OpenAI({
apiKey: process.env.DASHSCOPE_API_KEY,
// 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
baseURL: "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
});
async function main() {
const response = await openai.chat.completions.create({
model: "stepfun/step-3.7-flash",
messages: [
{
role: "user",
content: [
{
type: "video_url",
video_url: {
url: "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4"
}
},
{
type: "text",
text: "这段视频的内容是什么?"
}
]
}
]
});
console.log(response.choices[0].message.content);
}
main();
```
#### HTTP
#### curl
```
# 以下为华北2（北京）地域的URL。请将 {WorkspaceId} 替换为您的百炼业务空间ID，各地域的URL不同。
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "stepfun/step-3.7-flash",
"messages": [
{
"role": "user",
"content": [
{
"type": "video_url",
"video_url": {
"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4"
}
},
{
"type": "text",
"text": "这段视频的内容是什么?"
}
]
}
]
}'
```
### 文件限制
#### 图像文件
- **支持的图像格式：**JPG/JPEG、PNG、WEBP、静态 GIF
- **单张图像大小：**不超过 10M
- **多图输入限制：**单次请求最多 50 张图像，多张图片总大小不超过 20M
- **图像分辨率：**建议长或宽不超过 4096 像素。分辨率越高，模型推理成本越高（网络传输时间、首字延迟和费用消耗相应增加）
- **图像传入方式：**支持通过 HTTP/HTTPS 公网 URL 或 Base64 编码传入
#### 视频文件
- **视频大小：**最大 128M
- **视频时长：**无时长限制，推荐 5 分钟以内
- **音频理解：**不支持对视频文件的音频进行理解
## 其它功能
**功能**
**支持情况**
**备注**
[多轮对话](https://help.aliyun.com/zh/model-studio/multi-round-conversation)
支持
思考模式下需在每轮 assistant 消息中保留 `reasoning_content` 字段，否则会报错
[Function Calling](https://help.aliyun.com/zh/model-studio/qwen-function-calling#dd5a3dca390k9)
支持
不支持 `tool_choice` 参数
[上下文缓存](https://help.aliyun.com/zh/model-studio/context-cache)
支持
支持隐式缓存，自动开启
[结构化输出](https://help.aliyun.com/zh/model-studio/qwen-structured-output)
支持
`response_format` 不支持 json\_schema
[联网搜索](https://help.aliyun.com/zh/model-studio/web-search)
不支持
—
[前缀续写](https://help.aliyun.com/zh/model-studio/partial-mode)
不支持
—
多模态输入
支持
支持图像（URL/Base64）和视频（URL）输入
stepfun/step-3.7-flash 不支持以下参数：`tool_choice`、`thinking_budget`、`top_k`、`modalities`、`repetition_penalty`、`vl_high_resolution_images`、`preserve_thinking`、`enable_search`、`search_options`、`seed`、`logprobs`、`top_logprobs`、`n`。
支持的参数中，部分参数取值范围与功能与百炼不一致：
**参数**
**百炼**
**Step**
`temperature`
取值范围 \[0, 2)
取值范围 \[0, 2)，默认 1.0
`top_p`
取值范围 (0, 1.0\]
取值范围 (0, 1.0\]，默认 0.95
`max_tokens`
不限制思考模型思维链长度，仅限制输出长度
限制思考过程和最终回答的总体输出长度
`reasoning_effort`
控制 DeepSeek-V4 系列模型的推理力度，可选值 `high`/`max`
可选值为 `low`、`medium`、`high`，用于控制推理深度，需开启思考模式后生效
`stream_options`
其属性 `include_usage` 默认为 `false`，可设为 `true`
其属性 `include_usage` 强制为 `true`，无法关闭
`detail`
不支持
可选 `low`/`high`，默认 `low`
`frequency_penalty`
不支持
范围 0.0~1.0，默认 0
## 模型列表与计费
Step 系列模型是阶跃星辰直供的多模态推理模型，支持文本、图像和视频输入，支持通过 `enable_thinking` 开启思考模式。
模型上下文长度与价格信息请参见[百炼控制台](https://bailian.console.aliyun.com/cn-beijing?tab=model#/model-market/all)。
按照模型的输入与输出 Token 计费。
> 思考模式下，思维链按照输出 Token 计费。
## 错误码
Step 系列模型由阶跃星辰直供，其错误码与百炼平台标准错误码存在差异。调用 Step 模型时，请以下表为准。
**错误码**
**错误原因**
**解决方法**
400 - 格式错误
请求参数格式不正确，可能包括图片无法下载、图片数量超限、模型不支持的输入类型、参数值不合法
检查请求体、模型能力和参数范围
401 - 认证失败
缺少或无效的 API Key
检查 API Key 及请求头格式是否正确
402 - 余额不足
账户余额不足
检查账户余额，及时进行充值
429 - 请求超限
请求过于频繁，超出速率限制
实现指数退避和重试逻辑，或降低请求频率
451 - 内容拦截
请求内容或响应内容未通过审核
修改请求内容，避免输入不安全或敏感内容
500 - 服务器失败
服务器内部故障
请稍后重试，持续出现时联系我们解决
503 - 服务器故障
服务器负载过高
请稍后重试
skills/bailian-docs-llm-wiki/raw/model-user-guide/use-chat-client-or-development-tool/opencode.md
-505
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@@ -1,505 +0,0 @@
# OpenCode
OpenCode 是一款终端 AI 编程工具，可以通过按量计费、Coding Plan、Token Plan 个人版或 Token Plan 团队版接入阿里云百炼。
## 安装 OpenCode
1. 安装 [Node.js](https://nodejs.org/en/download/)（v18.0 或更高版本）。
2. 在终端中执行以下命令安装 OpenCode：
```
npm install -g opencode-ai
```
运行以下命令验证安装。若有版本号输出，则表示安装成功。
```
opencode -v
```
## 配置接入凭证
使用文本编辑器打开：
- macOS / Linux：`~/.config/opencode/opencode.json`
- Windows：`C:\Users\<用户名>\.config\opencode\opencode.json`
根据所选方案写入对应配置：
- **Token Plan 个人版**：个人订阅，按 token 消耗抵扣 Credits。
- **Token Plan 团队版**：按坐席订阅，按 token 消耗抵扣 Credits。
- **Coding Plan**：固定月费订阅，按模型调用次数计量。
- **按量计费**：按实际调用量后付费。
### Token Plan 个人版
需先购买 Token Plan 个人版套餐且套餐处于有效期内。可在[Token Plan 个人版页面](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview)购买套餐。
将 `YOUR_API_KEY` 替换为 Token Plan 个人版专属 [API Key](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/overview)。可用模型请参考 Token Plan 个人版[支持的模型](raw/model-user-guide/token-plan-guide/token-plan-personal/token-plan-personal-overview.md)。
```
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"bailian-token-plan-personal": {
"npm": "@ai-sdk/anthropic",
"name": "Alibaba Cloud Model Studio",
"options": {
"baseURL": "https://token-plan.cn-beijing.maas.aliyuncs.com/apps/anthropic/v1",
"apiKey": "YOUR_API_KEY"
},
"models": {
"qwen3.8-max": {
"name": "Qwen3.8 Max",
"reasoning": true,
"limit": {
"context": 983616,
"output": 131072
},
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"effort": "xhigh"
}
},
"qwen3.8-flash": {
"name": "Qwen3.8 Flash",
"reasoning": true,
"limit": {
"context": 983616,
"output": 131072
},
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"effort": "xhigh"
}
},
"qwen3.7-max": {
"name": "Qwen3.7 Max",
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"qwen3.7-plus": {
"name": "Qwen3.7 Plus",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"qwen3.6-flash": {
"name": "Qwen3.6 Flash",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"glm-5.2": {
"name": "GLM-5.2",
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"deepseek-v4-pro": {
"name": "DeepSeek V4 Pro"
},
"deepseek-v4-pro-0813": {
"name": "DeepSeek V4 Pro 0813"
},
"deepseek-v4-flash-0731": {
"name": "DeepSeek V4 Flash 0731"
}
}
}
}
}
```
### Token Plan 团队版
需先购买 Token Plan 团队版套餐且套餐处于有效期内。可在[Token Plan 团队版页面](https://bailian.console.aliyun.com/?tab=plan#/efm/subscription/overview)购买套餐。
将 `YOUR_API_KEY` 替换为 Token Plan 团队版专属 [API Key](https://bailian.console.aliyun.com/cn-beijing?tab=plan#/efm/subscription/uac-admin/organization/members/list)。可用模型请参考 Token Plan 团队版[支持的模型](raw/model-user-guide/token-plan-guide/token-plan-overview.md)。
```
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"bailian-token-plan": {
"npm": "@ai-sdk/anthropic",
"name": "Alibaba Cloud Model Studio",
"options": {
"baseURL": "https://token-plan.cn-beijing.maas.aliyuncs.com/apps/anthropic/v1",
"apiKey": "YOUR_API_KEY"
},
"models": {
"qwen3.8-max": {
"name": "Qwen3.8 Max",
"reasoning": true,
"limit": {
"context": 983616,
"output": 131072
},
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"effort": "xhigh"
}
},
"qwen3.8-flash": {
"name": "Qwen3.8 Flash",
"reasoning": true,
"limit": {
"context": 983616,
"output": 131072
},
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"effort": "xhigh"
}
},
"qwen3.7-max": {
"name": "Qwen3.7 Max",
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"qwen3.7-plus": {
"name": "Qwen3.7 Plus",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"qwen3.6-plus": {
"name": "Qwen3.6 Plus",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"qwen3.6-flash": {
"name": "Qwen3.6 Flash",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"deepseek-v4-pro": {
"name": "DeepSeek V4 Pro"
},
"deepseek-v4-pro-0813": {
"name": "DeepSeek V4 Pro 0813"
},
"deepseek-v4-flash": {
"name": "DeepSeek V4 Flash"
},
"deepseek-v4-flash-0731": {
"name": "DeepSeek V4 Flash 0731"
},
"deepseek-v3.2": {
"name": "DeepSeek V3.2"
},
"kimi-k2.7-code": {
"name": "Kimi K2.7 Code",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"kimi-k2.6": {
"name": "Kimi K2.6",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"kimi-k2.5": {
"name": "Kimi K2.5",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"glm-5.2": {
"name": "GLM-5.2",
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"glm-5.1": {
"name": "GLM-5.1",
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"glm-5": {
"name": "GLM-5",
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"MiniMax-M2.5": {
"name": "MiniMax M2.5"
}
}
}
}
}
```
### Coding Plan
将 `YOUR_API_KEY` 替换为 Coding Plan 专属 [API Key](https://bailian.console.aliyun.com/cn-beijing/?tab=model#/efm/coding_plan)。可用模型请参考 Coding Plan [支持的模型](raw/model-user-guide/token-plan-guide/coding-plan-guide/coding-plan.md)。
```
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"bailian-coding-plan": {
"npm": "@ai-sdk/anthropic",
"name": "Alibaba Cloud Model Studio",
"options": {
"baseURL": "https://coding.dashscope.aliyuncs.com/apps/anthropic/v1",
"apiKey": "YOUR_API_KEY"
},
"models": {
"qwen3.7-plus": {
"name": "Qwen3.7 Plus",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 1024
}
}
},
"qwen3.6-plus": {
"name": "Qwen3.6 Plus",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 1024
}
}
},
"qwen3.5-plus": {
"name": "Qwen3.5 Plus",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 1024
}
}
},
"qwen3-max-2026-01-23": {
"name": "Qwen3 Max 0123"
},
"qwen3-coder-next": {
"name": "Qwen3 Coder Next"
},
"qwen3-coder-plus": {
"name": "Qwen3 Coder Plus"
},
"MiniMax-M2.5": {
"name": "MiniMax M2.5"
},
"glm-5": {
"name": "GLM-5",
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 1024
}
}
},
"glm-4.7": {
"name": "GLM-4.7"
},
"kimi-k2.5": {
"name": "Kimi K2.5",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 1024
}
}
}
}
}
}
}
```
### 按量计费
将 `YOUR_API_KEY` 替换为[阿里云百炼 API Key](raw/model-api-reference/preparations/get-api-key.md)。可用模型请参考[Anthropic 兼容 API](https://help.aliyun.com/zh/model-studio/anthropic-api-messages)。
`baseURL` 按地域设置（将 `{WorkspaceId}` 替换为真实的 [Workspace ID](https://help.aliyun.com/zh/model-studio/obtain-the-app-id-and-workspace-id#732535cfc959h)），API Key 需与所选地域对应：
- 华北2（北京）：`https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/apps/anthropic/v1`
- 新加坡：`https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1`
```
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"bailian-payg": {
"npm": "@ai-sdk/anthropic",
"name": "Alibaba Cloud Model Studio",
"options": {
"baseURL": "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/apps/anthropic/v1",
"apiKey": "YOUR_API_KEY"
},
"models": {
"qwen3.7-max": {
"name": "Qwen3.7 Max",
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"qwen3.7-plus": {
"name": "Qwen3.7 Plus",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"qwen3.6-plus": {
"name": "Qwen3.6 Plus",
"modalities": {
"input": ["text", "image"],
"output": ["text"]
},
"options": {
"thinking": {
"type": "enabled",
"budgetTokens": 8192
}
}
},
"deepseek-v3.2": {
"name": "DeepSeek V3.2"
}
}
}
}
}
```
如需添加[其他模型](https://help.aliyun.com/zh/model-studio/anthropic-api-messages)，在 `models` 中以相同格式追加即可。
## 验证配置
保存配置后，重启 OpenCode，输入 `/models`，搜索 `Alibaba Cloud Model Studio`，选择需要使用的模型即可开始对话。
## 常见问题
### 错误码
配置过程中遇到报错，请参考对应计费方案的常见问题文档：
- 按量付费：[Anthropic API 兼容 - 错误码](https://help.aliyun.com/zh/model-studio/anthropic-api-messages)
- Coding Plan：[Coding Plan 常见问题](raw/model-user-guide/token-plan-guide/coding-plan-guide/coding-plan-faq.md)
- Token Plan 个人版：[Token Plan 个人版常见问题](raw/model-user-guide/token-plan-guide/token-plan-team-edition/token-plan-team-faq.md)
- Token Plan 团队版：[Token Plan 团队版常见问题](raw/model-user-guide/token-plan-guide/token-plan-team-edition/token-plan-team-faq.md)