Skip to content
  • Models
  • Rankings
  • Ori
Sign Up
Sign Up
OpenRouterOpenRouter
© 2026 OpenRouter, Inc

Product

  • Chat
  • Rankings
  • Benchmarks
  • Apps
  • Discover
  • Models
  • Collections
  • Providers
  • Pricing
  • Business
  • Enterprise
  • Labs

Company

  • About
  • Blog
  • Careers
    Hiring
  • Privacy
  • Terms of Service
  • Trust Center
  • Support
  • Works With OR
  • Data
  • Brand

Developer

  • Documentation
  • API Reference
  • Developer Platform
  • Status

Connect

  • Discord
  • GitHub
  • LinkedIn
  • X
  • YouTube
Favicon for qwen

Qwen: Qwen3 32B

qwen/qwen3-32b

Model weights
Compare

Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.

Modalities

In / Out Price

$0.08 / $0.28per 1M

Context

131K

Released

Apr 28, 2025

Knowledge Cutoff

Mar 2025

Compare
ProvidersPricingPerformanceUptimeBenchmarksAppsActivityFAQExplore

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), Floor (cheapest), or Exacto (highest tool-calling accuracy).

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Benchmarks

Scores on standardized evaluations. Higher percentages are better — and rank percentile shows where this model lands among all models on OpenRouter.

Benchmark score summary for Qwen: Qwen3 32B (Artificial Analysis)
SourceBenchmarkScore
Artificial AnalysisQwen3 32B (Reasoning) Intelligence Index7.2
Artificial AnalysisQwen3 32B (Reasoning) Coding Index15.3
Artificial AnalysisQwen3 32B (Reasoning) Agentic Index0.9
Artificial AnalysisQwen3 32B (Reasoning) GPQA Diamond66.8%
Artificial AnalysisQwen3 32B (Reasoning) HLE7.4%
Artificial AnalysisQwen3 32B (Reasoning) IFBench36.3%
Artificial AnalysisQwen3 32B (Reasoning) τ²-Bench Telecom29.8%
Artificial AnalysisQwen3 32B (Reasoning) AA-LCR0.0%
Artificial AnalysisQwen3 32B (Reasoning) GDPval-AA0.0%
Artificial AnalysisQwen3 32B (Reasoning) CritPt0.3%
Artificial AnalysisQwen3 32B (Reasoning) SciCode36.0%
Artificial AnalysisQwen3 32B (Reasoning) Terminal-Bench Hard3.0%
Artificial AnalysisQwen3 32B (Reasoning) AA-Omniscience Accuracy17.4%
Artificial AnalysisQwen3 32B (Reasoning) AA-Omniscience Non-Hallucination Rate17.9%
Artificial AnalysisQwen3 32B (Non-reasoning) GPQA Diamond53.5%
Artificial AnalysisQwen3 32B (Non-reasoning) HLE4.1%
Artificial AnalysisQwen3 32B (Non-reasoning) IFBench31.5%
Artificial AnalysisQwen3 32B (Non-reasoning) AA-LCR0.0%

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

Quick Start

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

Explore more models

AI Model RankingsRanking
$0.08$0.280.37s25 tps
99.72%
$0.14$0.5718.27s14 tps
92.33%

Throughput

25tok/s

P50, best across providers

Latency

0.37s

P50, best provider

AutoExacto Benchmarks
GPQA DiamondTAU-BenchGroq63.7%--Nebius61.5%47.8%auto-routing60.8%46.9%DeepInfra54.6%46.0%
Uptime (3d)The model was reachable. Request routed to a provider.

100.00%

Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.

99.51%

Availability over the last 3 days

Last 72 hours
Availability 99.51%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
99.88%
Without Routing
96.86%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.

1.
Favicon for https://www.mydreamcompanion.com/
My Dream Companion
new
22.1Btokens
2.
Favicon for https://github.com/Desearch-ai/subnet-22
Subnet 22 Miner
new
1.43Btokens
3.
Favicon for https://arbis.ai/
Arbis AVS Engine
new
1.08Btokens
4.
Favicon for https://skynet.local/
Skynet Agent
new
657Mtokens
5.
Favicon for https://geo.hybrid.co/
GEO Intelligence
new
608Mtokens

Frequently asked questions

Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation.

Qwen3 32B costs $0.08/M input tokens and $0.28/M output tokens.

Qwen3 32B has a 131,072 token context window. It supports up to 16,384 completion tokens.

Yes. Qwen3 32B accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

Qwen3 32B is served by 2 providers on OpenRouter: DeepInfra and SiliconFlow. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.

Qwen3 32B was released on April 28, 2025. Its knowledge cutoff is March 31, 2025.

More models from Qwen

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.

Text1M context$2 / $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.

Text1M context$0.15 / $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.

Rerank
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.

Rerank
Qwen3.8 27B

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.

Text1M context$0.15 / $1.875
Qwen3.8 27B

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.

Text262K contextFree
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.

Rerank$0.20/M tokens
Qwen3 ASR 1.7B

Qwen3 ASR 1.7B is an automatic speech recognition model from Qwen. It supports multilingual language identification and transcription across 30 languages and 22 Chinese dialects, with streaming and offline inference plus segment-level and word-level timestamps.

Transcription$0.000008/second
Qwen3 ASR 0.6B

Qwen3 ASR 0.6B is a compact automatic speech recognition model from Qwen. It supports multilingual language identification and transcription across 30 languages and 22 Chinese dialects, with streaming and offline inference plus segment-level and word-level timestamps.

Transcription$0.000003/second
Qwen3.8 2.4T A95B

Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of Qwen3.8 Max, with 95 billion active parameters out of 2.4 trillion total. It is suited for coding, research, complex reasoning, and agentic workflows.

Text1.0M context$2 / $6
Qwen3.8 2.4T A95B

Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of Qwen3.8 Max, with 95 billion active parameters out of 2.4 trillion total. It is suited for coding, research, complex reasoning, and agentic workflows.

Text1.0M context$2 / $6
Qwen Image 3 Pro

Qwen Image 3 Pro is an image generation and editing model from Qwen. It supports precise rendering of text and details as small as 10px, along with richer world knowledge compared to previous generations.

Imagefrom $0.04/image
Qwen Image 3

Qwen Image 3 is a unified image generation and editing model from Qwen. It supports precise rendering of text and details as small as 10px, along with a richer world knowledge base than previous generations.

Imagefrom $0.03/image
Qwen3.8 Max

Qwen3.8 Max (0803) is the August 3, 2026 checkpoint of Qwen3.8 Max, the flagship model in Alibaba's Qwen3.8 series and the general-availability successor to the Qwen3.8 Max Preview. It is a multimodal reasoning model intended for complex reasoning, visual understanding, coding, and agentic workflows. This checkpoint was superseded by Qwen3.8 Max (0902) on September 5, 2026.

Text1M context
Qwen3.7 Flash

Qwen3.7 Flash is a vision-language reasoning model from Alibaba. It is suited for multimodal agents, visual coding, search, and computer interaction, with strengths in object recognition, spatial understanding, and real-world visual perception.

Text1M context$0.03 / $0.13
Qwen-Audio-3.0-TTS Flash

Qwen-Audio-3.0-TTS Flash is Alibaba's fast, cost-efficient text-to-speech model, generating spoken audio from text via the DashScope Speech Synthesizer API.

Speech$15/M characters
Qwen-Audio-3.0-TTS Plus

Qwen-Audio-3.0-TTS Plus is Alibaba's higher-quality text-to-speech model, generating spoken audio from text via the DashScope Speech Synthesizer API.

Speech$20/M characters
Qwen3.7 Plus

Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its vision-language abilities while retaining full-stack, agent-level intelligence for coding, tool use, and productivity workflows. Its distinguishing trait is multi-modal interactive hybrid agent capability: it can perceive real-world scenes, read screens and interact with GUIs, generate code from visual references, and perform end-to-end navigation within mobile apps.

Text1M context$0.32 / $1.28
Qwen3.7 Max

Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks, and long-horizon autonomous execution. The model offers notable gains in coding and agentic performance over prior Qwen generations and supports explicit prompt caching for efficient repeated context use.

Text1M context$1.475 / $4.425
Qwen3 ASR Flash

Qwen3-ASR-Flash is Alibaba's automatic speech recognition service, built on the Qwen3-Omni foundation and trained on tens of millions of hours of multimodal speech data. The model handles 11 languages — including Chinese (with Cantonese, Sichuanese, Minnan, and Wu dialects), English, Arabic, French, German, Spanish, Italian, Portuguese, Russian, Japanese, and Korean — with automatic language detection so no manual configuration is needed for mixed-language audio.

The model is designed for difficult acoustic conditions: it transcribes lyrics over background music, handles noisy and far-field recordings, filters silence and non-speech audio, and accepts arbitrary context text (names, jargon, domain terminology) to bias recognition toward specific vocabulary.

Transcription$0.000035/second