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@@ -0,0 +1,9227 @@
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+{
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+ "version": 50,
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+ "models": [
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+ "model_name": "cosyvoice-clone-v1",
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+ "description": "声音复刻Cosyvoice大模型，依托先进的大模型技术进行特征提取，从而完成声音的复刻，且无需训练过程。仅需提供时长较短的音频，即可迅速生成高度相似且听感自然的定制声音。",
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+ "display_tags": [
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+ "Text"
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+ "model_name": "cosyvoice-v3-flash",
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+ "prices": {
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+ "currency": "CNY"
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+ "model_code": "cosyvoice-v3-flash",
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+ "description": "合成能力：CosyVoice-v3-Flash是通义实验室CosyVoice系列最新版高性能的语音合成大模型，较之前版本在自然度、音质、韵律、情感表现力上有更好的表现。该模型支持文本至语音的实时流式合成。克隆能力：CosyVoice-v3-Flash也是通义实验室CosyVoice系列最新版的语音克隆大模型，较之前版本提升了发音准确性、音色相似度，并且增加了更多小语种支持（德、西、法、意、俄）。仅需提供5-20s的参考音频，即可迅速生成高度相似且听感自然的定制声音。",
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+ "display_tags": [
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+ "语音合成"
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+ "prices": {
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+ "currency": "CNY"
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+ "model_code": "cosyvoice-v3-plus",
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+ "description": "克隆能力：CosyVoice-v3-plus是通义实验室CosyVoice系列最新版的语音克隆大模型，具有更好的音质和复刻相似度，适用于更专业的场景。仅需提供5-20s的参考音频，即可迅速生成高度相似且听感自然的定制声音。合成能力：CosyVoice-v3-plus是通义实验室CosyVoice系列最新版的语音合成大模型，具有更好的音质和表现力，适用于更专业的场景。该模型支持文本至语音的实时流式合成。",
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+ "display_tags": [
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+ "description": "基于Qwen3的代码生成模型，具有强大的Coding Agent能力，擅长工具调用和环境交互，能够实现自主编程、代码能力卓越的同时兼具通用能力。",
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+ "display_tags": [
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+ "group_name": "阿里系列模型"
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+ "currency": "CNY",
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+ "unit": "元/每百万tokens",
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+ "price": 1.25,
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+ "unit": "元/每百万tokens",
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+ "currency": "CNY",
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+ "price_original": 10.0
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+ "raw": "0.5",
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+ "unit": "元/每百万tokens",
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+ }
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+ },
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+ "32k<input<=128k": {
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+ "raw": "4",
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+ "unit": "元/每百万tokens",
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+ "price": 4.0,
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+ "currency": "CNY"
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+ },
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+ "输出": {
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+ "raw": "16",
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+ "price": 16.0,
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+ "currency": "CNY"
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+ },
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+ "显式缓存创建": {
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+ "raw": "5",
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+ "unit": "元/每百万tokens",
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+ "currency": "CNY"
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+ },
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+ "显式缓存命中": {
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+ "raw": "0.4",
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+ "note": "原价显示",
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+ "price_original": 16.0
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+ }
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+ },
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+ "currency": "CNY"
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+ },
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+ "输出": {
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+ "raw": "28",
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+ "unit": "元/每百万tokens",
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+ "price": 28.0,
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+ "currency": "CNY"
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+ },
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+ "显式缓存创建": {
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+ "raw": "8.75",
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+ "unit": "元/每百万tokens",
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+ "price": 8.75,
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+ "currency": "CNY"
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+ },
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+ "显式缓存命中": {
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+ "raw": "0.7",
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+ "note": "原价显示",
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+ "unit": "元/每百万tokens",
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+ "price": 0.7,
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+ "currency": "CNY",
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+ "price_original": 7.0
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+ "输入（Batch File）": {
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+ "raw": "3.5",
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+ "price": 3.5,
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+ "输出（Batch File）": {
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+ "raw": "14",
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+ "note": "原价显示",
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+ "unit": "元/每百万tokens",
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+ "price": 14.0,
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+ "currency": "CNY",
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+ "price_original": 28.0
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+ },
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+ "输入（缓存命中）": {
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+ "raw": "1.4",
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+ "unit": "元/每百万tokens",
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+ "price": 1.4,
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+ "currency": "CNY"
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+ }
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+ }
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+ },
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+ "model_info": {
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+ "function calling": true
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+ "description": "千问3系列Max模型，相较preview版本在智能体编程与工具调用方向进行了专项升级。本次发布的正式版模型达到领域SOTA水平，适配场景更加复杂的智能体需求。",
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+ "display_tags": [
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+ "文本生成",
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+ "深度思考"
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+ ],
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+ ]
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+ "rate_limits": {
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+ "TPM": "5000000",
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+ "上下文长度": "256K",
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+ "最大输入长度": "252K",
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+ "最大输出长度": "64K",
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+ "最大思维链长度": "80K"
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+ },
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+ "tool_prices": [
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+ {
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+ "unit": "元/千次调用",
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+ "label": "search_strategy:agent",
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+ "currency": "CNY"
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+ },
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+ {
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+ "note": "限时优惠",
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+ "unit": "元/千次调用",
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+ "label": "search_strategy:agent_max",
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+ "currency": "CNY"
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+ },
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+ {
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+ "unit": "元/千次调用",
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+ "label": "web_search",
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+ "price": 4.0,
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+ "currency": "CNY"
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+ },
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+ {
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+ "note": "限时免费",
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+ "unit": "元/千次调用",
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+ "label": "code_interpreter",
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+ "currency": "CNY"
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+ },
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+ {
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+ "note": "限时免费",
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+ "unit": "元/千次调用",
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+ "label": "web_extractor",
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+ "price": 0,
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+ "currency": "CNY"
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+ ],
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+ "scraped_at": "2026-04-28T03:54:54.797834Z",
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+ "api_key": "LuWDTrBgiu2uIse5_7r0r3nGgyKcr9lZs7gOuP0ePbPukww",
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+ "group_name": "阿里系列模型"
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+ },
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+ {
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+ "url": "https://bailian.console.aliyun.com/cn-beijing?spm=5176.29619931.J_XNqYbJaEnpB5_cCJf7e6D.1.770b10d7fVCktz&tab=model#/model-market/detail/qwen3-omni-flash-realtime?serviceSite=asia-pacific-china",
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+ "model_name": "qwen3-omni-flash-realtime",
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+ "prices": {
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+ "输入：文本": {
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+ "raw": "2.2",
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+ "unit": "元/每百万tokens",
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+ "price": 2.2,
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+ "currency": "CNY"
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+ },
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+ "raw": "18.9",
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+ "price": 18.9,
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+ "currency": "CNY"
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+ },
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+ "输入：图片/视频": {
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+ "raw": "3.9",
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+ "unit": "元/每百万tokens",
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+ "price": 3.9,
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+ "currency": "CNY"
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+ },
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+ "输出：文本（输入仅包含文本时）": {
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+ "raw": "8.3",
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+ "unit": "元/每百万tokens",
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+ "price": 8.3,
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+ "currency": "CNY"
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+ },
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+ "输出：文本+音频（输出的文本不计费）": {
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+ "raw": "75.1",
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|
+ "unit": "元/每百万tokens",
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+ "price": 75.1,
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+ "currency": "CNY"
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+ },
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+ "输出：文本（输入包含图片/音频/视频时）": {
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+ "raw": "15.2",
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+ "unit": "元/每百万tokens",
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+ "price": 15.2,
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+ "currency": "CNY"
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+ }
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+ },
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+ "联网搜索": false,
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+ "结构化输出": false,
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+ "function calling": false
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+ },
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+ "model_code": "qwen3-omni-flash-realtime",
|
|
|
+ "description": "Qwen3-Omni-Flash-Realtime多模态大模型的实时版，基于Thinker–Talker混合专家（MoE）架构，支持文本、图像、音频、视频的高效理解与语音生成能力，可进行119种语言文本交互和20种语言语音交互，生成类人语音实现跨语言精准沟通。模型具备强大指令跟随与系统提示定制功能，灵活适配对话风格与角色设定，广泛应用于文本创作、语音助手、多媒体分析等场景，提供自然流畅的多模态交互体验。",
|
|
|
+ "display_tags": [
|
|
|
+ "Qwen3",
|
|
|
+ "Realtime-Omni"
|
|
|
+ ],
|
|
|
+ "input_modalities": [
|
|
|
+ "Text",
|
|
|
+ "Image",
|
|
|
+ "Video",
|
|
|
+ "Audio"
|
|
|
+ ],
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|
|
+ "output_modalities": [
|
|
|
+ "Text",
|
|
|
+ "Audio"
|
|
|
+ ]
|
|
|
+ },
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|
|
+ "rate_limits": {
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+ "RPM": "60",
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|
|
+ "TPM": "100000",
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|
|
+ "上下文长度": "64K",
|
|
|
+ "最大输入长度": "48K",
|
|
|
+ "最大输出长度": "16K"
|
|
|
+ },
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|
|
+ "tool_prices": [],
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|
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+ "icon": "https://img.alicdn.com/imgextra/i3/O1CN01Kmx9dR1wcHOaMMXAk_!!6000000006328-55-tps-28-28.svg",
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+ "scraped_at": "2026-04-28T04:02:34.058005Z",
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+ "currency": "CNY",
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+ "description": "Qwen3-VL-Rerank重排模型，它能够深入理解文本、图片、视频的丰富多模态信息。在初步检索获得结果后，Qwen3-VL-Rerank 能够运用其先进的跨模态关联能力，对候选项目进行智能化的二次排序，将最相关的结果置于显要位置。通用用于提升跨模态搜索的准确率、优化图搜和视频检索的精准度、辅助图像聚类的分组质量、以及实现复杂多模态信息的高效检索和精确打标。",
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|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.5-i2i-preview",
|
|
|
+ "type": [
|
|
|
+ "图像生成"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.5-i2v-preview",
|
|
|
+ "type": [
|
|
|
+ "视频生成"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.5-t2i-preview",
|
|
|
+ "type": [
|
|
|
+ "图像生成"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.5-t2v-preview",
|
|
|
+ "type": [
|
|
|
+ "视频生成"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.6-i2v",
|
|
|
+ "type": [
|
|
|
+ "视频生成"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.6-image",
|
|
|
+ "type": [
|
|
|
+ "图像生成"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.6-r2v",
|
|
|
+ "type": [
|
|
|
+ "视频生成"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.6-t2i",
|
|
|
+ "type": [
|
|
|
+ "图像生成"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "model_name": "wan2.6-t2v",
|
|
|
+ "type": [
|
|
|
+ "视频生成"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ]
|
|
|
+}
|
