The Qwen3.5 native vision-language Flash series models are designed with a hybrid architecture that integrates linear attention mechanisms and sparse mixture-of-experts models, achieving higher inference efficiency. Compared with the 3 series, the models deliver leapfrog improvements in both pure-text and multimodal performance; they respond quickly and combine inference speed with high performance.
Pricing
Input Modalities
- Text
- Vision
- Video
Output Modalities
- Text
Context length
- 1M tokens
Max output
- 65.5K tokens
Capabilities
- Thinking
- Streaming
- Tool calling
- Web search
- URL context
- Code interpreter
- Computer use
- File search
- Memory tool
- Structured outputs
- Citations
- Prompt caching
- Background mode
- Server-side sessions
Providers
Alibaba Cloud alicloud-qwen3.5-flash
Pricing$0.0282$0.282
Web Search$0.00055/request
Cache Write$0.0352/M tokens
Cache Read$0.0028/M tokens
Pricing$0.1126$1.126
Web Search$0.00055/request
Cache Write$0.1407/M tokens
Cache Read$0.0113/M tokens
Pricing$0.169$1.69
Web Search$0.00055/request
Cache Write$0.2112/M tokens
Cache Read$0.0169/M tokens
Context991K
Max output64K
Latency1.4S
Throughput71.5TPS
Uptime
100.00% uptime 2 days ago
100.00% uptime yesterday
100.00% uptime today
Performance for qwen3.5-flash
Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).
Uptime
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Latency
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Throughput
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Try this model
Python
