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 details →Qwen: Qwen3.5-Flash vs Xiaomi: MiMo-V2.5-Pro
Qwen: Qwen3.5-Flash wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
MiMo-V2.5-Pro is Xiaomi’s flagship model, delivering strong performance in general agentic capabilities, complex software engineering, and long-horizon tasks, with top rankings on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro....
Model details →Side-by-side comparison
| Capability | Qwen: Qwen3.5-Flash | Xiaomi: MiMo-V2.5-Pro | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 1M | 1.1M | 🏆 Xiaomi: MiMo-V2.5-Pro |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.07 | $0.43 | 🏆 Qwen: Qwen3.5-Flash |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.26 | $0.87 | 🏆 Qwen: Qwen3.5-Flash |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | Yes | — | 🏆 Qwen: Qwen3.5-Flash |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | Yes | Yes | Tie |
Frequently asked questions
Is Qwen: Qwen3.5-Flash better than Xiaomi: MiMo-V2.5-Pro?
Qwen: Qwen3.5-Flash wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between Qwen: Qwen3.5-Flash and Xiaomi: MiMo-V2.5-Pro?
Input: $0.07 vs $0.43 per 1M tokens. Output: $0.26 vs $0.87 per 1M tokens.
What context windows do Qwen: Qwen3.5-Flash and Xiaomi: MiMo-V2.5-Pro support?
Qwen: Qwen3.5-Flash supports up to 1M tokens. Xiaomi: MiMo-V2.5-Pro supports up to 1.1M tokens.
Do both Qwen: Qwen3.5-Flash and Xiaomi: MiMo-V2.5-Pro support tool calling?
Qwen: Qwen3.5-Flash: yes. Xiaomi: MiMo-V2.5-Pro: yes.
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