The two are evenly matched.DeepSeek-V3 is better at reasoning; MiMo 7B RL at coding.
Scores updated · 9 tests both models report · How we compare
Where each one wins
Tests won in each of the two areas where both have results. Each piece is one test, so a longer bar means more evidence; grey means the two scored within a point of each other.
Coding and reasoning rest on a single test each.
What it costs
Prices per million tokens, roughly 750,000 words. The bars show the cost of a million tokens read plus a million written.
The biggest differences
The tests each model wins by the widest margin, up to three each. Scores are out of 100.
Where DeepSeek-V3 pulls ahead
- Graduate-level biology, physics and chemistry questionsGPQA Diamond+11.1points ahead
Where MiMo 7B RL pulls ahead
- Recent programming contest problemsLiveCodeBench v6+2.4points ahead
Every test, side by side
All 9 tests both models report. The winning score is in its model's colour; marks a score checked independently.
Other results7 tests, not counted
Tests outside the eight areas. They are not counted above: several are summary scores built from other tests, or the same test under another name.
- AIME 2024MiMo 7B RL by 2939.268.2+29
- IFEvalDeepSeek-V3 by 25.186.161+25.1
- MMLU-ProDeepSeek-V3 by 17.375.958.6+17.3
- SuperGPQADeepSeek-V3 by 13.253.740.5+13.2
- DROP (3-shot F1)DeepSeek-V3 by 12.991.678.7+12.9
- MATH-500 (EM)MiMo 7B RL by 5.690.295.8+5.6
- AIME 2025MiMo 7B RL by 4.151.355.4+4.1
Questions people ask
Which is better, DeepSeek-V3 or MiMo 7B RL?
DeepSeek-V3 and MiMo 7B RL each win one of the two areas where both have results. DeepSeek-V3 wins reasoning; MiMo 7B RL wins coding.
Which is better for coding?
MiMo 7B RL. It wins the one coding test both models report.
How do you compare the two?
We use the 9 benchmark tests both models have published scores on. The verdict counts the 2 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 7 are listed but not counted, because several are summary scores or repeat a test. Each score is the one shown on the model's own page.