MiMo-V2-Flash
MiMo-V2-Flash is a powerful, efficient, and ultra-fast foundation language model that excels in reasoning, coding, and agentic scenarios. It is a Mixture-of-Experts model with 309B total parameters and 15B active parameters, featuring a hybrid attention architecture with sliding-window and full attention (5:1 ratio, 128-token window). Delivers 150 tokens/sec inference with 256k context window.
Benchmark results
| Benchmark | Score | Tags | Source |
|---|---|---|---|
| AIME 2025 | 94.1% | self-reported llm-stats | link → |
| Arena-Hard v2 | 86.2% | self-reported llm-stats | link → |
| BrowseComp | 58.3% | self-reported llm-stats | link → |
| GPQA | 83.7% | self-reported llm-stats | link → |
| HMMT 2025 | 84.4% | self-reported llm-stats | link → |
| Humanity's Last Exam | 22.1% | self-reported llm-stats | link → |
| LiveCodeBench v6 | 80.6% | self-reported llm-stats | link → |
| LongBench v2 | 60.6% | self-reported llm-stats | link → |
| MMLU-Pro | 84.9% | self-reported llm-stats | link → |
| MRCR | 45.7% | self-reported llm-stats | link → |
| SWE-bench Multilingual | 71.7% | self-reported llm-stats | link → |
| SWE-Bench Verified | 73.4% | self-reported llm-stats | link → |
| Tau-bench | 80.3% | self-reported llm-stats | link → |
| Terminal-Bench | 30.5% | self-reported llm-stats | link → |
| Terminal-Bench 2.0 | 38.5% | self-reported llm-stats | link → |