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Merge pull request #71 from modelstudioai/feat/bailian-docs-update
chore: update bailian-docs-llm-wiki (2026-08-07)
2 parents 828a0e7 + 70a73fa commit 5420f72

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Lines changed: 8829 additions & 4164 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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@@ -0,0 +1,96 @@
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{
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"name": "Qwen-Coder-Plus",
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"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。",
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"items": [
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{
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"inferenceMetadata": {
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"response_modality": [
8+
"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": "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。",
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"offlineAt": "2026-07-13T15:59:59.000+00:00",
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"features": [
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"model-experience"
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],
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"provider": "qwen-domain-model",
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"model": "qwen-coder-plus",
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"prices": [
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{
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"priceUnit": "每百万tokens",
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"price": "3.5",
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"timeBand": "standard",
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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": "7",
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"timeBand": "standard",
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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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"qpmInfo": {
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"model-default-actual": {
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"count_limit_period": 1,
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"usage_limit": 100000,
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"usage_limit_field": "total_tokens",
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"count_limit": 20,
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"usage_limit_period": 6,
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"type": "model-default"
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},
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"model-default": {
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"count_limit_period": 1,
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"usage_limit": 100000,
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"usage_limit_field": "total_tokens",
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"count_limit": 20,
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"usage_limit_period": 6,
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"type": "model-default"
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}
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},
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"priceTimeBands": [
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"standard"
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],
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"capabilities": [
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"TG"
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],
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"versionTag": "MAJOR",
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"maxOutputTokens": 8192,
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"latestOnlineAt": "2024-11-11T16:00:00.000+00:00",
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"contextWindow": 131072,
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"maxInputTokens": 129024,
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"offlineInfo": {
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"inference": {
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"announceUrl": "https://www.aliyun.com/notice/118177",
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"offlineTime": "2026-10-10 00:00:00"
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}
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},
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"inferenceProvider": "aliyun-bailian",
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"name": "Qwen-Coder-Plus",
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"docUrl": "https://help.aliyun.com/document_detail/2850166.html",
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"category": "Older",
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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\": \"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}'",
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"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)",
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"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}",
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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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"completionsAPI": {
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"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}'",
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"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)",
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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.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}",
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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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}

‎skills/bailian-docs-llm-wiki/models/groups/qwen-image-3.0-pro.json‎

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"Text"
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]
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},
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"description": "内容丰实:支持最大 4.5k token 输入,支持图中图密集信息排版,让报纸、分镜、菜单、试卷等复杂版面一次生成。\n细节真实:支持 10px 小字精准渲染,微表情、毛孔、发丝等细节生动还原,逼近真实摄影的质感。\n知识厚实:支持 12 国语言、20+ 字体原生渲染,主流网页、游戏、直播等界面仿真,外部知识全纳入。\nQwen-Image-3.0-Pro 不只是在追求\"好看\",更在追求“好用”——让图像生成真正成为可落地的生产力工具。",
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"description": "内容丰实:支持最大 4.5k token 输入,支持图中图密集信息排版,让报纸、分镜、菜单、试卷等复杂版面一次生成。\n细节真实:支持 10px 小字精准渲染,微表情、毛孔、发丝等细节生动还原,逼近真实摄影的质感。\n知识厚实:支持 12 国语言、多种字体原生渲染,主流网页、游戏、直播等界面仿真,外部知识全纳入。\nQwen-Image-3.0-Pro 不只是在追求\"好看\",更在追求“好用”——让图像生成真正成为可落地的生产力工具。",
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"features": [
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"model-experience"
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],
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{
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"name": "Qwen-Math-Turbo",
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"description": "Qwen-Math-Turbo模型是专门用于数学解题的语言模型,推理速度快,成本低。",
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"items": [
5+
{
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"inferenceMetadata": {
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"response_modality": [
8+
"Text"
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],
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"request_modality": [
11+
"Text"
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]
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},
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"description": "千问系列数学模型是专门用于数学解题的语言模型,推理速度快,成本低。",
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"offlineAt": "2026-07-13T15:59:59.000+00:00",
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"features": [
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"model-experience"
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],
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"provider": "qwen-domain-model",
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"model": "qwen-math-turbo",
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"prices": [
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{
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"priceUnit": "每百万tokens",
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"price": "2",
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"timeBand": "standard",
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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": "6",
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"timeBand": "standard",
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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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"qpmInfo": {
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"model-default-actual": {
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"count_limit_period": 1,
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"usage_limit": 100000,
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"usage_limit_field": "total_tokens",
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"count_limit": 20,
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"usage_limit_period": 6,
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"type": "model-default"
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},
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"model-default": {
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"count_limit_period": 1,
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"usage_limit": 100000,
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"usage_limit_field": "total_tokens",
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"count_limit": 20,
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"usage_limit_period": 6,
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"type": "model-default"
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}
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},
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"priceTimeBands": [
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"standard"
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],
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"capabilities": [
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"TG"
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],
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"versionTag": "MAJOR",
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"maxOutputTokens": 3072,
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"latestOnlineAt": "2024-09-18T16:00:00.000+00:00",
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"contextWindow": 4096,
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"maxInputTokens": 3072,
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"offlineInfo": {
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"inference": {
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"announceUrl": "https://www.aliyun.com/notice/118177",
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"offlineTime": "2026-07-13 23:59:59"
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}
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},
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"inferenceProvider": "aliyun-bailian",
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"name": "Qwen-Math-Turbo",
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"docUrl": "https://help.aliyun.com/document_detail/2849934.html",
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"category": "Older",
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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\": \"qwen-math-turbo\",\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}'",
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"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-math-turbo\",\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)",
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"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-math-turbo\",\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}",
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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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"completionsAPI": {
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"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-math-turbo\",\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}'",
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"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-math-turbo\",\n messages=messages,\n result_format=\"message\",\n)\n\nprint(response.output.choices[0].message.content)",
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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.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-math-turbo\")\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}",
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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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}

‎skills/bailian-docs-llm-wiki/models/groups/qwen3.8-max.json‎

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@@ -101,7 +101,8 @@
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"model-experience",
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"prefix-completion",
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"structured-outputs",
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"web-search"
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"web-search",
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"batch"
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],
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"provider": "qwen",
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"model": "qwen3.8-max",
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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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"priceUnit": "每百万tokens",
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"price": "6",
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"type": "input_token_batch",
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"priceName": "输入(Batch File)"
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},
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{
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"priceUnit": "每百万tokens",
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"price": "18",
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"type": "output_token_batch",
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"priceName": "输出(Batch File)"
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},
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{
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"priceUnit": "每百万tokens",
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"price": "15",
@@ -135,6 +148,20 @@
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"price": "1",
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"type": "input_token_cache_read",
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"priceName": "显式缓存命中"
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},
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{
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"priceUnit": "每百万tokens",
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"price": "12",
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"discount": 0.5,
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"type": "input_token_batch_chat",
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"priceName": "输入(Batch Chat)"
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},
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{
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"priceUnit": "每百万tokens",
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"price": "36",
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"discount": 0.5,
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"type": "output_token_batch_chat",
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"priceName": "输出(Batch Chat)"
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}
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],
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"qpmInfo": {

‎skills/bailian-docs-llm-wiki/models/groups/tongyi-xiaomi-analysis-flash.json‎

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Original file line numberDiff line numberDiff line change
@@ -1,6 +1,6 @@
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{
2-
"name": "通义晓蜜-对话分析-flash",
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"description": "通义晓蜜-对话分析-flash是专注于日常任务,如对话信息抽取、场景分类等分析类需求的模型,自定义分析标准遵循与对话语义理解能力显著提升,适用于低时延的离线在线分析任务。",
2+
"name": "伶鹊-对话分析-flash",
3+
"description": "伶鹊-对话分析-flash是专注于日常任务,如对话信息抽取、场景分类等分析类需求的模型,自定义分析标准遵循与对话语义理解能力显著提升,适用于低时延的离线在线分析任务。",
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"items": [
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{
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"inferenceMetadata": {
@@ -11,7 +11,7 @@
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"Text"
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]
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},
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"description": "通义晓蜜-对话分析-flash是专注于日常任务,如对话信息抽取、场景分类等分析类需求的模型,自定义分析标准遵循与对话语义理解能力显著提升,适用于低时延的离线在线分析任务。",
14+
"description": "伶鹊-对话分析-flash是专注于日常任务,如对话信息抽取、场景分类等分析类需求的模型,自定义分析标准遵循与对话语义理解能力显著提升,适用于低时延的离线在线分析任务。",
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"features": [],
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"provider": "qwen-domain-model",
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"model": "tongyi-xiaomi-analysis-flash",
@@ -61,7 +61,7 @@
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"contextWindow": 32768,
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"maxInputTokens": 28672,
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"inferenceProvider": "aliyun-bailian",
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"name": "通义晓蜜-对话分析-flash",
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"name": "伶鹊-对话分析-flash",
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"docUrl": "https://help.aliyun.com/document_detail/3015075.html",
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"samples": {
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"openai": {

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