Official model documentation
Fields: model identity
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…
- Context
- 1M
- Max output
- 66K
- Input
- $0.065
- Output
- $0.26
Recommended use cases
Strengths in this dataset
- 1,000,000-token context window
- text, image, video input
- 14 supported API parameters listed
Limits and caveats
- Provider behavior and pricing can change; verify the linked sources before production use.
Capability
Model-native facts
Model- Reasoning
- Supported
- Open weights
- Unknown
Provider endpoint facts
Provider endpoint- Tool calling
- Supported
- Structured output
- Supported
- Streaming
- Unknown
- Prompt cache
- Unknown
- Batch
- Unknown
- Fine-tuning
- Unknown
Qwen3.5-Flash Provider pricing
Provider endpoint: qwen/qwen3.5-flash-02-23
- Input
- $0.065 per 1M tokens
- Output
- $0.26 per 1M tokens
- Cached input
- Unknown per 1M tokens
- Image output
- Unknown per 1M tokens
Sources and verification
OpenRouter Models API
Fields: identity, description, modalities, context window, maximum output, pricing, supported parameters
Frequently asked questions
Answers are generated from the same sourced model and provider facts shown above.
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…
Model specifications and prices may vary by provider and change over time. AIToolly displays sources and verification dates so users can confirm critical details before production use.