
Qwen: Qwen3.5 397B A17B
qwen/qwen3.5-397b-a17b
Model weightsThe 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.
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 Floor (cheapest).
| $0.50 | $3.60 | $0.30 | 0.87s | 83 tok/s | ||
| $0.60 | $3.60 | $0.12 | 1.34s | 95 tok/s | ||
| $0.39 | $2.34 | -- | 1.02s | 46 tok/s | ||
| $0.45 | $3.00 | $0.22 | 1.11s | 40 tok/s | ||
| $0.55 | $3.50 | $0.55 | 1.50s | 42 tok/s | ||
| $0.60 | $3.60 | -- | 1.60s | 28 tok/s | ||
| $0.75 | $4.50 | -- | 1.32s | 27 tok/s | ||
| $0.60 | $3.60 | -- | 1.34s | 54 tok/s |
- Input $0.50 / Output $3.6083 tok/s91.53% uptime
- Input $0.60 / Output $3.6095 tok/s96.75% uptime
- Input $0.39 / Output $2.3446 tok/s99.37% uptime
Input $0.45 / Output $3.0040 tok/s99.81% uptime- Input $0.55 / Output $3.5042 tok/s97.20% uptime
- Input $0.60 / Output $3.6028 tok/s97.96% uptime
- Input $0.75 / Output $4.5027 tok/s97.04% uptime
- Input $0.60 / Output $3.6054 tok/s92.50% uptime
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.
Weighted average
Qwen3.5 397B A17B — Price history
| $0.387 | $3.00 | 27.2% | 18.8% | ||
| $0.390 | $2.34 | 0.0% | 12.5% | ||
| $0.410 | $3.60 | 45.2% | 1.5% | ||
| $0.550 | $3.50 | 0.0% | 6.4% | ||
| $0.552 | $3.60 | 9.9% | 58.7% | ||
| $0.600 | $3.60 | 0.0% | 0.2% | ||
| $0.600 | $3.60 | 0.0% | 1.4% | ||
| $0.750 | $4.50 | 16.2% | 0.5% |
- Effective in $0.387 / Effective out $3.00Token share18.8%
- Effective in $0.390 / Effective out $2.34Token share12.5%
- Effective in $0.410 / Effective out $3.60Token share1.5%
- Effective in $0.550 / Effective out $3.50Token share6.4%
- Effective in $0.552 / Effective out $3.60Token share58.7%
- Effective in $0.600 / Effective out $3.60Token share0.2%
- Effective in $0.600 / Effective out $3.60Token share1.4%
- Effective in $0.750 / Effective out $4.50Token share0.5%
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).
P50, best across providers
P50, best provider
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.
Availability over the last 3 days
Last 72 hoursAvailability over the last 24 hours
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 provider routing options.
Benchmarks
Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | Qwen3.5 397B A17B (Non-reasoning) GPQA Diamond | 86.1% |
| Artificial Analysis | Qwen3.5 397B A17B (Non-reasoning) HLE | 19.8% |
| Artificial Analysis | Qwen3.5 397B A17B (Non-reasoning) IFBench | 51.6% |
| Artificial Analysis | Qwen3.5 397B A17B (Non-reasoning) τ²-Bench Telecom | 83.9% |
| Artificial Analysis | Qwen3.5 397B A17B (Non-reasoning) AA-LCR | 64.3% |
| Artificial Analysis | Qwen3.5 397B A17B (Non-reasoning) CritPt | 0.9% |
| Artificial Analysis | Qwen3.5 397B A17B (Non-reasoning) Terminal-Bench Hard | 35.6% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) Coding Index | 48.2 |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) Agentic Index | 8.3 |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) GPQA Diamond | 89.3% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) HLE | 29.0% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) IFBench | 78.8% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) τ²-Bench Telecom | 95.6% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) AA-LCR | 77.3% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) τ-Bench Banking | 13.4% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) GDPval-AA | 15.0% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) CritPt | 1.7% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) SciCode | 44.8% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) Terminal-Bench Hard | 40.9% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) Terminal-Bench 4.0 | 0.0% |
| Artificial Analysis | Qwen3.5 397B A17B (Reasoning) Terminal-Bench 2.1 | 51.3% |
| Design Arena | Qwen3.5 397B A17B Models Arena 3D Elo | 1140 |
| Design Arena | Qwen3.5 397B A17B Models Arena Code Categories Elo | 1169 |
| Design Arena | Qwen3.5 397B A17B Models Arena Data Visualization Elo | 1172 |
| Design Arena | Qwen3.5 397B A17B Models Arena Game Development Elo | 1129 |
| Design Arena | Qwen3.5 397B A17B Models Arena SVG Elo | 1129 |
| Design Arena | Qwen3.5 397B A17B Models Arena UI Component Elo | 1154 |
| Design Arena | Qwen3.5 397B A17B Models Arena Website Elo | 1182 |
| Vals AI | Qwen 3.5 397B A17B SAGE | 85.5% |
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.
API
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.
Not currently available through EU in-region routing. In-region routing docs
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 7 of the 8 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 8 providers on OpenRouter: Alibaba Cloud Int., DeepInfra, Parasail, AtlasCloud, StreamLake, GMICloud, NovitaAI, and Venice. 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.