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Favicon for qwen

Qwen: Qwen3.5 397B A17B

qwen/qwen3.5-397b-a17b

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The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers state-of-the-art performance comparable to leading-edge models across a wide range of tasks, including language understanding, logical reasoning, code generation, agent-based tasks, image understanding, video understanding, and graphical user interface (GUI) interactions. With its robust code-generation and agent capabilities, the model exhibits strong generalization across diverse agent.

Modalities

In / Out Price

$0.39 / $2.34per 1M

Context

262K

Released

Feb 16, 2026

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: Qwen3.5 397B A17B (Artificial Analysis and Design Arena)
SourceBenchmarkScore
Artificial AnalysisQwen3.5 397B A17B (Reasoning) Intelligence Index19.1
Artificial AnalysisQwen3.5 397B A17B (Reasoning) Coding Index48.2
Artificial AnalysisQwen3.5 397B A17B (Reasoning) Agentic Index10.6
Artificial AnalysisQwen3.5 397B A17B (Reasoning) GPQA Diamond89.3%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) HLE29.0%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) IFBench78.8%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) τ²-Bench Telecom95.6%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) AA-LCR77.3%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) GDPval-AA20.3%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) CritPt1.7%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) SciCode44.8%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) Terminal-Bench Hard40.9%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) AA-Omniscience Accuracy30.8%
Artificial AnalysisQwen3.5 397B A17B (Reasoning) AA-Omniscience Non-Hallucination Rate11.1%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) GPQA Diamond86.1%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) HLE19.8%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) IFBench51.6%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) τ²-Bench Telecom83.9%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) AA-LCR64.3%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) CritPt0.9%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) Terminal-Bench Hard35.6%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) AA-Omniscience Accuracy24.5%
Artificial AnalysisQwen3.5 397B A17B (Non-reasoning) AA-Omniscience Non-Hallucination Rate17.3%
Design ArenaQwen3.5 397B A17B Models Arena 3D Elo1189
Design ArenaQwen3.5 397B A17B Models Arena Code Categories Elo1195
Design ArenaQwen3.5 397B A17B Models Arena Data Visualization Elo1190
Design ArenaQwen3.5 397B A17B Models Arena Game Development Elo1163
Design ArenaQwen3.5 397B A17B Models Arena SVG Elo1162
Design ArenaQwen3.5 397B A17B Models Arena UI Component Elo1179
Design ArenaQwen3.5 397B A17B Models Arena Website Elo1203

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.

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91.12%

Throughput

55tok/s

P50, best across providers

Latency

0.69s

P50, best provider

AutoExacto Benchmarks
GPQA DiamondTAU-BenchNovitaAI85.8%--StreamLake84.4%--Parasail87.8%78.4%Alibaba Cloud Int.88.2%77.0%GMICloud87.7%77.3%
+6 more providers
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.

98.81%

Availability over the last 3 days

Last 72 hours
Availability 98.81%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
98.64%
Without Routing
86.15%

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.

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Perplexity AI
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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.
801Mtokens
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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.
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Frequently asked questions

The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency.

Qwen3.5 397B A17B costs $0.39/M input tokens and $2.34/M output tokens.

Qwen3.5 397B A17B has a 262,144 token context window. It supports up to 65,536 completion tokens.

Yes. Qwen3.5 397B A17B accepts tools and tool_choice for function calling on 9 of the 10 providers serving it, and requests that send tools are routed to those providers. It also supports structured outputs via a JSON schema in response_format.

Qwen3.5 397B A17B accepts text, images, and video as input and returns text.

Qwen3.5 397B A17B is served by 10 providers on OpenRouter: Alibaba Cloud Int., DeepInfra, Parasail, DigitalOcean, Phala, AtlasCloud, StreamLake, GMICloud and 2 more. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.

Qwen3.5 397B A17B was released on February 16, 2026.

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.

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Qwen3.8 Flash

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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.

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Qwen3.8 27B

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Qwen3.8 27B

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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 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.

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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.

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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.

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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
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Qwen3.8 Max

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Qwen3.7 Flash

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Qwen-Audio-3.0-TTS Flash

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Qwen-Audio-3.0-TTS Plus

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Qwen3.7 Plus

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