Xiaomi
partialShows if the model has enough results for an index.MiMo-V2.6-Flash
MiMo-V2.6-Flash is a reasoning model from Xiaomi in the MiMo-V2.6 family. 10 benchmarks count toward its score, in 2 categories.
IndexOverall score out of 100.Unranked
CoverageShare of the index weight with results.40%
SpeedOutput tokens per second.40/s
Input / 1MUS dollars per 1M input tokens.$0.14
Output / 1MUS dollars per 1M output tokens.$0.28
ContextMaximum tokens in one request.1.05M
EloLMArena rating and rank.N/A
The index is a score out of 100. The ± range shows how much it can change.
CapabilitiesScore per category, out of 100.
Out of 100Results
10 counted| BenchmarkThe test name. | CategoryThe capability that the test measures. | ResultThe score from the publisher. | IndexThis result as a score out of 100. | RunThe settings of the run. | DateDate of the result. | Published byThe source of the result. |
|---|---|---|---|---|---|---|
| CyberGym | Agentic | 95.1% | 81.2 | — | — | Zhun Wang et al. |
| Terminal-Bench 2.1 (provider run) | Agentic | 87.6% | 75.8 | — | — | DeepSeek-AI |
| Terminal-Bench 2.1 (provider run) | Agentic | 87.6% | 75.8 | — | — | DeepSeek-AI |
| OSWorld-Verified | Agentic | 80.8% | 69.0 | — | — | Tianbao Xie et al. |
| Toolathlon-Verified | Agentic | 73.6% | 71.9 | — | — | Moonshot AI |
| DeepSWE | Agentic | 67.9% | 73.1 | — | — | Datacurve AI |
| JobBench | Agentic | 61.2% | 76.2 | — | — | Yuetai Li et al. |
| AutomationBench | Agentic | 52.3% | 95.0 | — | — | Moonshot AI |
| Agents' Last Exam | Agentic | 27.6% | 67.2 | — | — | DeepSeek-AI |
| ProgramBench: Can Language Models Rebuild Programs From Scratch? | Coding | 26.0% | 39.4 | — | — | John Yang et al. |
| ExploitGym | Agentic | 6.0% | 65.6 | — | — | Zhun Wang et al. |
10 benchmarks count, from 11 of 11 results. A grey row does not count. Too few models took that benchmark.