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Favicon for qwen

Qwen: Qwen3.5-9B

qwen/qwen3.5-9b

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Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design with early fusion of multimodal tokens, allowing the model to process and reason across text and images within the same context.

Modalities
In / Out Price
$0.08 / $0.13per 1M
Context
262K
Released
Mar 10, 2026
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ProvidersPricingPerformanceUptimeBenchmarksAppsActivityFAQExplore

Providers

This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.

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 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.

Benchmark score summary for Qwen: Qwen3.5-9B (Artificial Analysis)
SourceBenchmarkScore
Artificial AnalysisQwen3.5 9B (Non-reasoning) Intelligence Index13.3
Artificial AnalysisQwen3.5 9B (Non-reasoning) Coding Index23.5
Artificial AnalysisQwen3.5 9B (Non-reasoning) GPQA Diamond78.6%
Artificial AnalysisQwen3.5 9B (Non-reasoning) HLE9.4%
Artificial AnalysisQwen3.5 9B (Non-reasoning) IFBench37.8%
Artificial AnalysisQwen3.5 9B (Non-reasoning) τ²-Bench Telecom85.1%
Artificial AnalysisQwen3.5 9B (Non-reasoning) AA-LCR46.0%
Artificial AnalysisQwen3.5 9B (Non-reasoning) CritPt0.6%
Artificial AnalysisQwen3.5 9B (Non-reasoning) Terminal-Bench Hard18.2%
Artificial AnalysisQwen3.5 9B (Non-reasoning) Terminal-Bench 2.121.3%
Artificial AnalysisQwen3.5 9B (Non-reasoning) AA-Omniscience Accuracy14.0%
Artificial AnalysisQwen3.5 9B (Non-reasoning) AA-Omniscience Non-Hallucination Rate1.6%
Artificial AnalysisQwen3.5 9B (Reasoning) Intelligence Index11.2
Artificial AnalysisQwen3.5 9B (Reasoning) Coding Index28.7
Artificial AnalysisQwen3.5 9B (Reasoning) Agentic Index1.2
Artificial AnalysisQwen3.5 9B (Reasoning) GPQA Diamond80.6%
Artificial AnalysisQwen3.5 9B (Reasoning) HLE14.9%
Artificial AnalysisQwen3.5 9B (Reasoning) IFBench66.7%
Artificial AnalysisQwen3.5 9B (Reasoning) τ²-Bench Telecom86.8%
Artificial AnalysisQwen3.5 9B (Reasoning) AA-LCR70.0%
Artificial AnalysisQwen3.5 9B (Reasoning) τ-Bench Banking7.0%
Artificial AnalysisQwen3.5 9B (Reasoning) GDPval-AA0.0%
Artificial AnalysisQwen3.5 9B (Reasoning) CritPt0.3%
Artificial AnalysisQwen3.5 9B (Reasoning) SciCode29.5%
Artificial AnalysisQwen3.5 9B (Reasoning) Terminal-Bench Hard24.2%
Artificial AnalysisQwen3.5 9B (Reasoning) Terminal-Bench 4.00.5%
Artificial AnalysisQwen3.5 9B (Reasoning) Terminal-Bench 2.129.2%
Artificial AnalysisQwen3.5 9B (Reasoning) AA-Omniscience Accuracy16.4%
Artificial AnalysisQwen3.5 9B (Reasoning) AA-Omniscience Non-Hallucination Rate16.4%

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.

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Frequently asked questions

Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design with early fusion of multimodal tokens, allowing the model to process and reason across text and images within the same context.

Qwen3.5-9B costs $0.08/M input tokens and $0.13/M output tokens, with separate rates for Cache Read at $0.04/M tokens.

Qwen3.5-9B has a 262,144 token context window. It supports up to 65,536 completion tokens.

Yes. Qwen3.5-9B accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

Qwen3.5-9B accepts text, images, and video as input and returns text.

Qwen3.5-9B was released on March 10, 2026.