MiMo V2.6 Distill 9B vs Qwen3.5 9B
A compact distilled MiMo and a compact Qwen are a useful pair for an application-level evaluation. Their similar nominal size makes them candidates for a controlled test, not proof that their quality, memory, or speed is identical.
By BenchGrid editorial · Reviewed · Performance not yet measured
| SIDE BY SIDESame questions. Different models. | XiaomiMiMo V2.6 Distill 9B | AlibabaQwen3.5 9B |
|---|---|---|
| Weight memory estimate16-bit · weights only · decimal GB | Pending review | Pending review |
| Parameters | ~9B | ~9B |
| Active parameters | ~9B | ~9B |
| Context window | Under review | Under review |
| Architecture | Dense · vision | Dense · vision |
| License | MIT | Apache 2.0 |
| BenchGrid performance test | Not yet measured | Not yet measured |
| Deployment perspective | Use this smaller MiMo checkpoint as a separate deployment target from the much larger Flash and Pro models. | Use the official post-trained checkpoint as the baseline when comparing distilled models and quantization variants. |
| Primary source | Model card | Model card |
Lower weight memory does not mean better quality or faster inference. These calculations exclude serving overhead and do not confirm a working quantized checkpoint. Read the methodology.
Keep this MiMo separate from Flash and Pro
This page concerns XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B. It is a different checkpoint from the much larger MiMo Flash and Pro entries. Before downloading weights or interpreting a result, match the full repository name and revision. A report labeled only MiMo is not sufficient to identify what ran.
Similar names are a starting point, not an equivalence
Both directory entries use a nominal 9B size. Complete checkpoint scope, context settings, and compatible serving configurations still require review here. The comparison therefore shows pending memory estimates. For an initial experiment, choose a context and modality supported by both exact checkpoints, keep the same prompt set, and record any different chat-template requirements.
Evaluate the task before counting tokens
Our suggested comparison uses code tests or objectively scored question sets from the intended application. Set a maximum generation budget and report completion success as well as latency. A model that produces a longer answer may look different under raw tokens per second without completing more useful work. Keep publisher benchmark claims separate from independent measurements, which are not available on this page yet.