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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

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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), 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 Elo1157
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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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.