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Merge pull request #64 from modelstudioai/feat/bailian-docs-update
chore: update bailian-docs-llm-wiki (2026-08-02)
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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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{"slug":"animate-anyone-gen2","name":"舞动人像AnimateAnyone","description":"AnimateAnyone是一款视频生成模型，可基于人物图片和动作模板生成人物全身动作视频。","primaryCapability":"VG","capabilities":["VG"],"providers":["qwen"],"itemCount":1,"items":[{"model":"animate-anyone-gen2","name":"舞动人像AnimateAnyone","capabilities":["VG"]}],"detailPath":"groups/animate-anyone-gen2.json"}
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{"slug":"animate-anyone-template-gen2","name":"舞动人像AnimateAnyone-template","description":"AnimateAnyone-Template是辅助AnimateAnyone的动作模板生成模型，可基于视频提取人物动作并制作模板。","primaryCapability":"VG","capabilities":["VG"],"providers":["qwen"],"itemCount":1,"items":[{"model":"animate-anyone-template-gen2","name":"舞动人像AnimateAnyone-template","capabilities":["VG"]}],"detailPath":"groups/animate-anyone-template-gen2.json"}
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{"slug":"cosyvoice","name":"CosyVoice大模型","description":"基于新一代生成式语音大模型，CosyVoice将文本理解和语音生成技术深度融合，能够精准解析并诠释各种文本内容，将其转化为如同真人发声般的自然语音，带来高度拟人化的自然语音合成体验。","primaryCapability":"TTS","capabilities":["TTS"],"providers":["qwen","qwen-domain-model"],"itemCount":7,"items":[{"model":"cosyvoice-clone-v1","name":"声音复刻CosyVoice大模型","capabilities":["TTS"]},{"model":"cosyvoice-v1","name":"语音合成CosyVoice大模型","capabilities":["TTS"]},{"model":"cosyvoice-v2","name":"语音生成cosyvoice-v2大模型","capabilities":["TTS"]},{"model":"cosyvoice-v3-flash","name":"语音生成CosyVoice-v3-flash大模型","capabilities":["TTS"]},{"model":"cosyvoice-v3-plus","name":"语音生成CosyVoice-v3-plus大模型","capabilities":["TTS"]},{"model":"cosyvoice-v3.5-flash","name":"语音生成CosyVoice-v3.5-flash大模型","capabilities":["TTS"]},{"model":"cosyvoice-v3.5-plus","name":"语音生成CosyVoice-v3.5-plus大模型","capabilities":["TTS"]}],"detailPath":"groups/cosyvoice.json"}
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{"slug":"deepseek","name":"DeepSeek","description":"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。","primaryCapability":"Reasoning","capabilities":["Reasoning","TG"],"providers":["deepseek"],"itemCount":12,"items":[{"model":"deepseek-r1","name":"DeepSeek-R1","contextWindow":131072,"capabilities":["Reasoning"]},{"model":"deepseek-r1-0528","name":"DeepSeek-R1-0528","contextWindow":131072,"capabilities":["Reasoning"]},{"model":"deepseek-r1-distill-qwen-1.5b","name":"DeepSeek-R1-Distill-Qwen-1.5B","contextWindow":32768,"capabilities":["TG"]},{"model":"deepseek-r1-distill-qwen-14b","name":"DeepSeek-R1-Distill-Qwen-14B","contextWindow":32768,"capabilities":["TG"]},{"model":"deepseek-r1-distill-qwen-32b","name":"DeepSeek-R1-Distill-Qwen-32B","contextWindow":32768,"capabilities":["TG"]},{"model":"deepseek-r1-distill-qwen-7b","name":"DeepSeek-R1-Distill-Qwen-7B","contextWindow":32768,"capabilities":["TG"]},{"model":"deepseek-v3","name":"DeepSeek-V3","contextWindow":65536,"capabilities":["TG","Reasoning"]},{"model":"deepseek-v3.1","name":"DeepSeek-V3.1","contextWindow":131072,"capabilities":["TG","Reasoning"]},{"model":"deepseek-v3.2","name":"DeepSeek-V3.2","contextWindow":131072,"capabilities":["Reasoning","TG"]},{"model":"deepseek-v3.2-exp","name":"Deepseek-V3.2-Exp","contextWindow":131072,"capabilities":["TG","Reasoning"]},{"model":"deepseek-v4-flash","name":"DeepSeek-V4-Flash","contextWindow":1000000,"capabilities":["TG","Reasoning"]},{"model":"deepseek-v4-pro","name":"DeepSeek-V4-Pro","contextWindow":1000000,"capabilities":["Reasoning","TG"]}],"detailPath":"groups/deepseek.json","maxContextWindow":1000000}
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{"slug":"deepseek","name":"DeepSeek","description":"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。","primaryCapability":"Reasoning","capabilities":["Reasoning","TG"],"providers":["deepseek"],"itemCount":13,"items":[{"model":"deepseek-r1","name":"DeepSeek-R1","contextWindow":131072,"capabilities":["Reasoning"]},{"model":"deepseek-r1-0528","name":"DeepSeek-R1-0528","contextWindow":131072,"capabilities":["Reasoning"]},{"model":"deepseek-r1-distill-qwen-1.5b","name":"DeepSeek-R1-Distill-Qwen-1.5B","contextWindow":32768,"capabilities":["TG"]},{"model":"deepseek-r1-distill-qwen-14b","name":"DeepSeek-R1-Distill-Qwen-14B","contextWindow":32768,"capabilities":["TG"]},{"model":"deepseek-r1-distill-qwen-32b","name":"DeepSeek-R1-Distill-Qwen-32B","contextWindow":32768,"capabilities":["TG"]},{"model":"deepseek-r1-distill-qwen-7b","name":"DeepSeek-R1-Distill-Qwen-7B","contextWindow":32768,"capabilities":["TG"]},{"model":"deepseek-v3","name":"DeepSeek-V3","contextWindow":65536,"capabilities":["TG","Reasoning"]},{"model":"deepseek-v3.1","name":"DeepSeek-V3.1","contextWindow":131072,"capabilities":["TG","Reasoning"]},{"model":"deepseek-v3.2","name":"DeepSeek-V3.2","contextWindow":131072,"capabilities":["Reasoning","TG"]},{"model":"deepseek-v3.2-exp","name":"Deepseek-V3.2-Exp","contextWindow":131072,"capabilities":["TG","Reasoning"]},{"model":"deepseek-v4-flash","name":"DeepSeek-V4-Flash","contextWindow":1000000,"capabilities":["TG","Reasoning"]},{"model":"deepseek-v4-flash-0731","name":"DeepSeek-V4-Flash-0731","contextWindow":1000000,"capabilities":["Reasoning","TG"]},{"model":"deepseek-v4-pro","name":"DeepSeek-V4-Pro","contextWindow":1000000,"capabilities":["Reasoning","TG"]}],"detailPath":"groups/deepseek.json","maxContextWindow":1000000}
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{"slug":"embedding","name":"通义多模态向量","description":"基于LLM底座的通用多模态表征模型，支持文本、图像、视频3种模态，具有以视觉为中心、全场景性能优异、高性价比的特点，适用于以图搜图、以文搜图、以文搜视频、以视频搜视频、以文搜文等下游多样化任务场景。","primaryCapability":"ME","capabilities":["ME"],"providers":["qwen-domain-model"],"itemCount":3,"items":[{"model":"multimodal-embedding-v1","name":"通用多模态向量","contextWindow":0,"capabilities":["ME"]},{"model":"tongyi-embedding-vision-flash","name":"视觉向量-flash","capabilities":["ME"]},{"model":"tongyi-embedding-vision-plus","name":"视觉向量-plus","capabilities":["ME"]}],"detailPath":"groups/embedding.json"}
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{"slug":"emo-detect-v1","name":"悦动人像EMO-detect","description":"EMO-Detect是辅助EMO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":"emo-detect-v1","name":"悦动人像EMO-detect","capabilities":["VG"]}],"detailPath":"groups/emo-detect-v1.json"}
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{"slug":"emo-v1","name":"悦动人像EMO","description":"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。","primaryCapability":"VG","capabilities":["VG"],"providers":["qwen"],"itemCount":1,"items":[{"model":"emo-v1","name":"悦动人像EMO","capabilities":["VG"]}],"detailPath":"groups/emo-v1.json"}

‎skills/bailian-docs-llm-wiki/models/groups/deepseek.json‎

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}
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}
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},
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{
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"inferenceMetadata": {
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"response_modality": [
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"Text"
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],
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"request_modality": [
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"Text"
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]
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},
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"description": "高效轻量化MoE模型，总参284B，激活13B，原生支持百万超长上下文能力。推理速度快、延迟低、调用成本低廉，综合能力均衡，主打高并发、轻量化任务，适合日常对话、内容创作、基础 RAG、批量文案处理等普惠刚需场景。",
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"features": [
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"cache",
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"function-calling",
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"model-experience",
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"web-search"
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],
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"provider": "deepseek",
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"model": "deepseek-v4-flash-0731",
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"prices": [
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{
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"priceUnit": "每百万tokens",
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"price": "1",
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"type": "input_token",
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"priceName": "输入"
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},
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{
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"priceUnit": "每百万tokens",
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"price": "2",
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"type": "output_token",
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"priceName": "输出"
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},
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{
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"priceUnit": "每百万tokens",
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"price": "0.2",
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"type": "input_token_cache",
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"priceName": "输入（缓存命中）"
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}
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],
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"qpmInfo": {
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"model-default-actual": {
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"count_limit_period": 6,
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"usage_limit": 1200000,
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"usage_limit_field": "total_tokens",
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"count_limit": 1500,
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"usage_limit_period": 60,
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"type": "model-default"
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},
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"model-default": {
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"count_limit_period": 6,
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"usage_limit": 1200000,
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"usage_limit_field": "total_tokens",
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"count_limit": 1500,
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"usage_limit_period": 60,
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"type": "model-default"
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}
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},
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"priceTimeBands": [],
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"capabilities": [
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"Reasoning",
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"TG"
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],
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"modelAlias": "",
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"versionTag": "SNAPSHOT",
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"maxOutputTokens": 393216,
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"latestOnlineAt": "2026-07-31T16:09:35.000+00:00",
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"contextWindow": 1000000,
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"maxInputTokens": 1000000,
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"inferenceProvider": "aliyun-bailian",
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"name": "DeepSeek-V4-Flash-0731",
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"docUrl": "https://help.aliyun.com/document_detail/2868565.html",
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"category": "Third-party",
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"samples": {
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"openai": {
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"completionsAPI": {
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"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\": \"deepseek-v4-flash-0731\",\n \"messages\": [\n {\n \"role\": \"user\", \n \"content\": \"你是谁\"\n }\n ],\n \"stream\": true,\n \"enable_thinking\": true\n}'",
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"python": "from openai import OpenAI\nimport os\n\nclient = OpenAI(\n # 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\"\n api_key=os.getenv(\"DASHSCOPE_API_KEY\"),\n base_url=\"https://[workspace-id].cn-beijing.maas.aliyuncs.com/compatible-mode/v1\",\n)\n\nmessages = [{\"role\": \"user\", \"content\": \"你是谁\"}]\ncompletion = client.chat.completions.create(\n model=\"deepseek-v4-flash-0731\", # 您可以按需更换为其它深度思考模型\n messages=messages,\n extra_body={\"enable_thinking\": True},\n stream=True\n)\nis_answering = False # 是否进入回复阶段\nprint(\"\\n\" + \"=\" * 20 + \"思考过程\" + \"=\" * 20)\nfor chunk in completion:\n if not chunk.choices:\n continue\n delta = chunk.choices[0].delta\n if hasattr(delta, \"reasoning_content\") and delta.reasoning_content is not None:\n if not is_answering:\n print(delta.reasoning_content, end=\"\", flush=True)\n if hasattr(delta, \"content\") and delta.content:\n if not is_answering:\n print(\"\\n\" + \"=\" * 20 + \"完整回复\" + \"=\" * 20)\n is_answering = True\n print(delta.content, end=\"\", flush=True)",
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"nodejs": "import OpenAI from \"openai\";\nimport process from 'process';\n\n// 初始化 openai 客户端\nconst openai = new OpenAI({\n apiKey: process.env.DASHSCOPE_API_KEY, // 从环境变量读取\n baseURL: 'https://[workspace-id].cn-beijing.maas.aliyuncs.com/compatible-mode/v1'\n});\nlet isAnswering = false;\nasync function main() {\n try {\n const messages = [{ role: 'user', content: '你是谁' }];\n const stream = await openai.chat.completions.create({\n model: 'deepseek-v4-flash-0731',\n messages,\n stream: true,\n enable_thinking: true\n });\n console.log('\\n' + '='.repeat(20) + '思考过程' + '='.repeat(20));\n for await (const chunk of stream) {\n if (!chunk.choices.length) continue;\n const delta = chunk.choices[0].delta;\n if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {\n if (!isAnswering) {\n process.stdout.write(delta.reasoning_content);\n }\n }\n if (delta.content !== undefined && delta.content) {\n if (!isAnswering) {\n console.log('\\n' + '='.repeat(20) + '完整回复' + '='.repeat(20));\n isAnswering = true;\n }\n process.stdout.write(delta.content);\n }\n }\n } catch (error) {\n console.error('Error:', error);\n }\n}\nmain();",
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"docUrl": "https://help.aliyun.com/document_detail/3016807.html"
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}
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},
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"dashscope": {
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"default": {
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"curl": "curl -X POST \"https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation\" \\\n-H \"Authorization: Bearer $DASHSCOPE_API_KEY\" \\\n-H \"Content-Type: application/json\" \\\n-d '{\n \"model\": \"deepseek-v4-flash-0731\",\n \"input\":{\n \"messages\":[\n {\n \"role\": \"user\",\n \"content\": \"你是谁？\"\n }\n ]\n },\n \"parameters\":{\n \"enable_thinking\": true,\n \"result_format\": \"message\"\n }\n}'",
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"python": "import os\nfrom dashscope import Generation\nimport dashscope\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=\"deepseek-v4-flash-0731\",\n messages=messages,\n result_format=\"message\",\n # 开启深度思考\n enable_thinking=True,\n)\n\nif response.status_code == 200:\n # 打印思考过程\n print(\"=\" * 20 + \"思考过程\" + \"=\" * 20)\n print(response.output.choices[0].message.reasoning_content)\n \n # 打印回复\n print(\"=\" * 20 + \"完整回复\" + \"=\" * 20)\n print(response.output.choices[0].message.content)\nelse:\n print(f\"HTTP返回码：{response.status_code}\")\n print(f\"错误码：{response.code}\")\n print(f\"错误信息：{response.message}\")",
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"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.utils.Constants;\n\npublic class Main {\n static {\n Constants.baseHttpApiUrl=\"https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1\";\n }\n public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException {\n Generation gen = new Generation();\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(\"deepseek-v4-flash-0731\")\n .messages(Arrays.asList(systemMsg, userMsg))\n .resultFormat(GenerationParam.ResultFormat.MESSAGE)\n .enableThinking(true)\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(\"====================思考过程====================\");\n System.out.println(result.getOutput().getChoices().get(0).getMessage().getReasoningContent());\n System.out.println(\"\\n====================完整回复====================\");\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}",
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"docUrl": "https://help.aliyun.com/document_detail/3016809.html"
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}
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}
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}
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},
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{
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"inferenceMetadata": {
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"response_modality": [

‎skills/bailian-docs-llm-wiki/models/index.json‎

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{
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"updatedAt": "2026-08-01",
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"updatedAt": "2026-08-02",
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"totalFamilies": 175,
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"totalModels": 392,
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"totalModels": 393,
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"profileCoverage": {
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"full": 332,
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"full": 314,
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"partial": 15,
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"structured-only": 45,
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"structured-only": 64,
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},
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"capabilityDistribution": {
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"providers": [
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],
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"itemCount": 12,
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"itemCount": 13,
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"deepseek-v3.2",
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"deepseek-v3.2-exp",
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"deepseek-v4-flash",
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"deepseek-v4-flash-0731",
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"deepseek-v4-pro"
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],
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"maxContextWindow": 1000000

‎skills/bailian-docs-llm-wiki/models/index.md‎

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# 百炼模型市场索引
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> 自动生成 · 共 175 个模型家族 · 392 个主干模型 · 更新于 2026-08-01
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> 自动生成 · 共 175 个模型家族 · 393 个主干模型 · 更新于 2026-08-02
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**机器查询走结构化文件**：
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## 推理 `Reasoning` — 14 个家族
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- [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。
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- 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-pro`
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- 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`
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- [MiniMax](groups/MiniMax-M2.1.json) — MiniMax推出的旗舰级开源大模型，聚焦真实世界复杂任务，包含MiniMax-M2.1、MiniMax-M2.5等开源模型。
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- 模型：`MiniMax-M2.1`, `MiniMax-M2.5`
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- [QVQ-Max](groups/qvq-max.json) — 千问QVQ视觉推理模型，支持视觉输入及思维链输出，在数学、编程、视觉分析、创作以及通用任务上都表现了更强的能力。

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