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qwen 的网站图标

Qwen: Qwen3.8 27B

qwen/qwen3.8-27b

模型权重
比较

Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled.

模态
输入 / 输出价格
US$0.006 / US$0.304每 1M
上下文
1.0M
发布时间
2026年8月14日
比较
提供商定价性能正常运行时间基准测试应用活动常见问题探索

提供商

不同公司托管同一个模型。OpenRouter 会根据您选择的路由模式,将您的请求路由到其中一家公司:Balanced(价格 + 速度)、Nitro(最快)或 Floor(最便宜)。

定价

客户为此模型实际支付的平均价格,与提供商公布的价格并列展示。由于缓存和折扣,实际支付的价格往往远低于标价。

性能

吞吐量是模型生成内容的速度(每秒 token 数,越高越好)。延迟是完整的往返时间(越低越好)。TTFT 是首个 token 的响应时间,即您看到内容出现前需要等待的时间(越低越好)。

正常运行时间

正常运行时间是指过去 3 天中至少有一家提供商响应请求的时间百分比。可用性是指成功提供推理服务的时间百分比。OpenRouter 会持续监控,并在某家提供商返回错误时使用次优提供商。

基准测试

标准化评测得分,以及此模型针对共用基准测试提示词的实际输出。百分比越高越好,每项排名显示此模型在该来源所评测的模型中的位置。

基准测试得分摘要: Qwen: Qwen3.8 27B (Artificial Analysis 和 Vals AI)
来源基准测试得分
Artificial AnalysisQwen3.8 27B (Non-reasoning) Intelligence Index10.0
Artificial AnalysisQwen3.8 27B (Non-reasoning) Coding Index44.6
Artificial AnalysisQwen3.8 27B (Non-reasoning) Agentic Index22.4
Artificial AnalysisQwen3.8 27B (Non-reasoning) GPQA Diamond81.8%
Artificial AnalysisQwen3.8 27B (Non-reasoning) HLE12.1%
Artificial AnalysisQwen3.8 27B (Non-reasoning) AA-LCR69.3%
Artificial AnalysisQwen3.8 27B (Non-reasoning) τ-Bench Banking20.0%
Artificial AnalysisQwen3.8 27B (Non-reasoning) GDPval-AA29.1%
Artificial AnalysisQwen3.8 27B (Non-reasoning) CritPt0.3%
Artificial AnalysisQwen3.8 27B (Non-reasoning) SciCode36.2%
Artificial AnalysisQwen3.8 27B (Non-reasoning) Terminal-Bench 4.00.0%
Artificial AnalysisQwen3.8 27B (Non-reasoning) Terminal-Bench 2.149.1%
Artificial AnalysisQwen3.8 27B (Non-reasoning) AA-Omniscience Accuracy9.1%
Artificial AnalysisQwen3.8 27B (Non-reasoning) AA-Omniscience Non-Hallucination Rate82.0%
Artificial AnalysisQwen3.8 27B (Xhigh) Intelligence Index20.1
Artificial AnalysisQwen3.8 27B (Xhigh) Coding Index68.1
Artificial AnalysisQwen3.8 27B (Xhigh) Agentic Index45.8
Artificial AnalysisQwen3.8 27B (Xhigh) GPQA Diamond90.5%
Artificial AnalysisQwen3.8 27B (Xhigh) HLE33.9%
Artificial AnalysisQwen3.8 27B (Xhigh) AA-LCR82.0%
Artificial AnalysisQwen3.8 27B (Xhigh) τ-Bench Banking48.0%
Artificial AnalysisQwen3.8 27B (Xhigh) GDPval-AA46.3%
Artificial AnalysisQwen3.8 27B (Xhigh) CritPt5.4%
Artificial AnalysisQwen3.8 27B (Xhigh) SciCode46.6%
Artificial AnalysisQwen3.8 27B (Xhigh) Terminal-Bench 4.05.6%
Artificial AnalysisQwen3.8 27B (Xhigh) Terminal-Bench 2.179.8%
Artificial AnalysisQwen3.8 27B (Xhigh) AA-Omniscience Accuracy17.3%
Artificial AnalysisQwen3.8 27B (Xhigh) AA-Omniscience Non-Hallucination Rate71.4%
Artificial AnalysisQwen3.8 27B (Low) Intelligence Index15.7
Artificial AnalysisQwen3.8 27B (Low) Coding Index58.2
Artificial AnalysisQwen3.8 27B (Low) Agentic Index38.9
Artificial AnalysisQwen3.8 27B (Low) GPQA Diamond84.5%
Artificial AnalysisQwen3.8 27B (Low) HLE14.0%
Artificial AnalysisQwen3.8 27B (Low) AA-LCR77.3%
Artificial AnalysisQwen3.8 27B (Low) τ-Bench Banking32.2%
Artificial AnalysisQwen3.8 27B (Low) GDPval-AA43.5%
Artificial AnalysisQwen3.8 27B (Low) CritPt0.0%
Artificial AnalysisQwen3.8 27B (Low) SciCode40.0%
Artificial AnalysisQwen3.8 27B (Low) Terminal-Bench 4.02.5%
Artificial AnalysisQwen3.8 27B (Low) Terminal-Bench 2.167.4%
Artificial AnalysisQwen3.8 27B (Low) AA-Omniscience Accuracy18.3%
Artificial AnalysisQwen3.8 27B (Low) AA-Omniscience Non-Hallucination Rate51.3%
Artificial AnalysisQwen3.8 27B (Medium) Intelligence Index17.5
Artificial AnalysisQwen3.8 27B (Medium) Coding Index56.1
Artificial AnalysisQwen3.8 27B (Medium) Agentic Index44.4
Artificial AnalysisQwen3.8 27B (Medium) GPQA Diamond84.5%
Artificial AnalysisQwen3.8 27B (Medium) HLE14.1%
Artificial AnalysisQwen3.8 27B (Medium) AA-LCR79.7%
Artificial AnalysisQwen3.8 27B (Medium) τ-Bench Banking47.4%
Artificial AnalysisQwen3.8 27B (Medium) GDPval-AA44.4%
Artificial AnalysisQwen3.8 27B (Medium) CritPt0.0%
Artificial AnalysisQwen3.8 27B (Medium) SciCode39.0%
Artificial AnalysisQwen3.8 27B (Medium) Terminal-Bench 4.05.1%
Artificial AnalysisQwen3.8 27B (Medium) Terminal-Bench 2.165.2%
Artificial AnalysisQwen3.8 27B (Medium) AA-Omniscience Accuracy17.9%
Artificial AnalysisQwen3.8 27B (Medium) AA-Omniscience Non-Hallucination Rate34.6%
Vals AIQwen 3.8 27B Code Migration14.2%
Vals AIQwen 3.8 27B CyberBench v1.167.8%
Vals AIQwen 3.8 27B EMB59.7%
Vals AIQwen 3.8 27B Finance Agent (v2)48.6%
Vals AIQwen 3.8 27B GPQA Diamond88.9%
Vals AIQwen 3.8 27B Harvey's Legal Agent Benchmark v111.3%
Vals AIQwen 3.8 27B IOI39.1%
Vals AIQwen 3.8 27B LiveCodeBench84.0%
Vals AIQwen 3.8 27B LegalBench82.4%
Vals AIQwen 3.8 27B Legal Research Bench36.1%
Vals AIQwen 3.8 27B MedCode28.7%
Vals AIQwen 3.8 27B MedScribe83.8%
Vals AIQwen 3.8 27B MMLU Pro84.3%
Vals AIQwen 3.8 27B MMMU Pro83.9%
Vals AIQwen 3.8 27B MortgageTax64.9%
Vals AIQwen 3.8 27B ProgramBench0.0%
Vals AIQwen 3.8 27B ProofBench v1.116.0%
Vals AIQwen 3.8 27B SAGE v152.4%
Vals AIQwen 3.8 27B SkillsBench38.1%
Vals AIQwen 3.8 27B SWE-bench86.0%
Vals AIQwen 3.8 27B Tax Agent Bench30.9%
Vals AIQwen 3.8 27B TaxEval v270.8%
Vals AIQwen 3.8 27B Terminal-Bench 2.158.4%
Vals AIQwen 3.8 27B Terminal-Bench-Science0.0%
Vals AIQwen 3.8 27B Vals Index48.5%
Vals AIQwen 3.8 27B Vibe Code Bench v1.164.8%

应用

向此模型发送最多流量的公开应用。这能很好地反映实际生产工作负载的情况,也能提示此模型最适合哪些用例。

活动

此模型的 token 用量和请求流量随时间的变化。

API

可直接用于调用此模型的代码。OpenRouter 的 API 与 OpenAI 兼容——大多数 SDK 只需替换基础 URL 即可使用。切换模型时,唯一需要更改的是下方的模型标识符。

目前无法通过 EU 区域内路由使用。 区域内路由文档

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延迟 / 吞吐量
US$0.040US$1.350US$0.0180----1.26s75 tps
99.93%
US$0.035US$1.500US$0.0175----0.52s85 tps
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US$0.20US$0.150US$2.50US$1.875US$0.05US$0.0375----0.28s36 tps
98.47%
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99.23%
US$0.420US$3.000US$0.0850----3.29s45 tps
100.00%
US$0.039US$1.250US$0.0039----2.54s24 tps
100.00%
US$0.450US$3.200------0.63s65 tps
99.75%
US$0.400US$3.000US$0.1500----1.30s46 tps
99.92%
US$0.040US$1.950US$0.0200----2.86s14 tps
98.79%
US$0.500US$3.000US$0.1000----5.94s60 tps
100.00%
US$0.450US$3.200US$0.0500----11.69s20 tps
96.76%
US$0.150US$2.000US$0.0500----0.86s52 tps
99.95%
US$0.425US$2.550US$0.0850US$0.53125US$0.04251.94s43 tps
96.59%
US$0.040US$2.300US$0.0200----2.12s45 tps
99.95%
US$0.990US$1.490US$0.9900----0.36s403 tps
99.98%
US$0.200US$2.500US$0.0500----1.38s53 tps
87.45%
吞吐量
403token/秒

P50,所有提供商中最佳

延迟
0.28秒

P50,最佳提供商

AutoExacto 基准测试
GPQA DiamondTAU-BenchStreamLake84.9%--DekaLLM83.4%77.9%Prime Intellect83.2%78.0%Parasail83.4%77.1%Mancer82.2%78.1%
+19 家其他提供商
正常运行时间(3 天)模型可访问。请求已路由至提供商。
100.00%
可用性(3 天)模型通过任意提供商返回了推理结果。错误和空响应均计为失败。
99.64%

过去 3 天的可用性

最近 72 小时
可用性 99.64%
3 天前2 天前昨天现在

过去 24 小时的可用性

OpenRouter Availability
99.68%
Without Routing
96.37%

当上游提供商发生错误时,如果您的请求筛选条件允许,我们可以通过路由到另一家健康的提供商来恢复服务。您可以通过以下接口以编程方式访问各提供商的正常运行时间数据: 端点 API。了解更多有关我们的 负载均衡和提供商路由选项。

1.
https://nousresearch.com 的网站图标
Hermes Agent
Hermes Agent is an open-source, self-improving AI agent by Nous Research that runs persistently with memory across sessions, and builds reusable skills from experience. It comes with 40+ built-in tools, including web search, browser automation, and vision, plus scheduled automations and subagents.
121Btoken
2.
https://claude.ai/apple-touch-icon.png 的网站图标
Claude Code
Claude Code is Anthropic's agentic coding tool that reads your entire codebase, plans and executes changes across files, runs tests, and iterates on failures, all from natural language prompts.
103Btoken
3.
https://openai.com/codex 的网站图标
Codex
A coding agent that helps you build and ship with AI
79.3Btoken
4.
https://portkey.ai/ 的网站图标
Portkey AI
Control panel for AI apps
66.6Btoken
5.
https://pi.dev/ 的网站图标
pi
There are many coding agents, but this one is yours.
47.6Btoken

常见问题

Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled.

Qwen3.8 27B 的费用为 US$0.006/百万输入 token 和 US$0.304/百万输出 token,Cache Read,价格为 US$0.005/M tokens 单独计费。

Qwen3.8 27B 的上下文窗口为 1,024,000 个 token。它最多支持 32,768 个补全 token。

是的。Qwen3.8 27B 接受 tools 和 tool_choice,以进行函数调用。 它还支持通过 response_format 中的 JSON schema 实现结构化输出。

Qwen3.8 27B 接受 文本、图像和视频 作为输入,并返回 文本。

Qwen3.8 27B 在 OpenRouter 上由 23 家提供商提供服务:Prime Intellect, Darkbloom, Chutes, NEAR AI, Ionstream, Modal, DekaLLM, Wafer 及另外 15 个。请求会路由至当前可用的最佳提供商,并在故障时自动切换至其他提供商,您也可以通过提供商路由固定或排除提供商。

Qwen3.8 27B 于 2026年8月14日 发布。

更多模型,来自 Qwen

Qwen Image 2.1 Turbo

Qwen Image 2.1 Turbo is the fast, lower-cost tier of the Qwen Image 2.1 series from Qwen, for image generation and editing where throughput and price matter more than peak fidelity. See Qwen Image 2.1 Pro for the higher-quality tier.

图像from US$0.0184/image
Qwen Image 2.1 Pro

Qwen Image 2.1 Pro is an image generation and editing model from Qwen, the higher-quality tier of the Qwen Image 2.1 series. Choose it over Qwen Image 2.1 Turbo when output fidelity matters more than cost or speed.

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Qwen3.8 Max Prime

Qwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point. It accepts text, image, and video input and returns text, with a 1M-token context window and reasoning enabled by default. Tool calling, structured outputs, and configurable reasoning effort are supported, matching Qwen3.8 Max.

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Qwen3.8 Omni Flash

Qwen3.8 Omni Flash is an omni-modal reasoning model from Alibaba, the first Qwen model built around agentic capabilities with native audio-video understanding. It is suited for audio-video analysis and summarization, video editing and production workflows, audio-video dialogue, coding, knowledge work, and GUI interaction, and it is particularly strong at long-form multimedia tasks that combine speech, sound, and visual context. It also supports two-channel and four-channel spatial audio understanding.

文本1M 上下文US$0.15 / US$0.47
Qwen3.8 Max

Qwen3.8 Max 0902 is an updated snapshot of Qwen3.8 Max from Alibaba's Qwen team. It is a 2.4-trillion-parameter mixture-of-experts model that accepts text, image, and video input and returns text, with a 1M-token context window and reasoning enabled by default.

This snapshot is post-trained for coding and agentic work, including multi-step software projects, multi-tool orchestration, and long-horizon task execution. It also targets chart reasoning, document parsing, and multimodal understanding over long documents and extended video. Tool calling, structured outputs, and configurable reasoning effort are supported.

文本1M 上下文US$2 / US$6
Qwen3.8 Flash

Qwen3.8 Flash is a multimodal reasoning model from Alibaba. It is suited for coding assistance, agentic workflows, visual understanding, document and codebase analysis, desktop interaction, chart analysis, and long-video analysis.

文本1M 上下文US$0.15 / US$0.47
Qwen3 Reranker 0.6B

Qwen3 Reranker 0.6B is the smallest text reranking model from Alibaba Cloud in the Qwen3 Reranker series. Built on the Qwen3 architecture, it evaluates query-document pairs to produce relevance scores for retrieval and RAG pipelines. Supports 100+ languages with instruction-aware reranking. Designed for low-latency, resource-constrained deployments.

重排序
Qwen3 Reranker 4B

Qwen3 Reranker 4B is a text reranking model from Alibaba Cloud built on the Qwen3 architecture. It evaluates query-document pairs to produce relevance scores for use in retrieval and RAG pipelines. Supports 100+ languages and programming languages, with instruction-aware reranking that allows customizing scoring criteria per task. A compact 4B alternative balancing performance and inference cost.

重排序
Qwen3 Reranker 8B

Qwen3 Reranker 8B is a text reranking model from Alibaba Cloud built on the Qwen3 architecture. It evaluates query-document pairs to produce relevance scores for use in retrieval and RAG pipelines. Supports 100+ languages and programming languages, with instruction-aware reranking that allows customizing scoring criteria per task. Offers strong performance on multilingual benchmarks including MTEB, CMTEB, and MMTEB.

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Qwen3 ASR 0.6B

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Qwen3.8 2.4T A95B

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Qwen Image 3 Pro

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