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Qwen: Qwen3.5-Flash

qwen/qwen3.5-flash-02-23

Released Feb 25, 20261,000,000 context$0.065/M input tokens$0.26/M output tokens

The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the 3 series, these models deliver a leap forward in performance for both pure text and multimodal tasks, offering fast response times while balancing inference speed and overall performance.

Overview
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Performance for Qwen3.5-Flash

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Effective Pricing for Qwen3.5-Flash

Actual cost per million tokens across providers over the past hour

Apps using Qwen3.5-Flash

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Recent activity on Qwen3.5-Flash

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Uptime stats for Qwen3.5-Flash

Uptime stats for Qwen3.5-Flash across all providers

Providers for Qwen3.5-Flash

OpenRouter routes requests to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.

Sample code and API for Qwen3.5-Flash

OpenRouter normalizes requests and responses across providers for you.

OpenRouter supports reasoning-enabled models that can show their step-by-step thinking process. Use the reasoning parameter in your request to enable reasoning, and access the reasoning_details array in the response to see the model's internal reasoning before the final answer. When continuing a conversation, preserve the complete reasoning_details when passing messages back to the model so it can continue reasoning from where it left off. Learn more about reasoning tokens.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

See the Request docs for all possible fields, and Parameters for explanations of specific sampling parameters.