Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Mistral 8x7B finishes one point ahead overall, 57 to 56. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
o3-mini is the reasoning model in the pair, while Mistral 8x7B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. o3-mini gives you the larger context window at 200K, compared with 32K for Mistral 8x7B.
Pick Mistral 8x7B if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
Mistral 8x7B
54
o3-mini
82.1
Mistral 8x7B
35.3
o3-mini
49.3
Mistral 8x7B
65.1
o3-mini
87.3
Mistral 8x7B
78
o3-mini
93.9
Mistral 8x7B is ahead overall, 57 to 56. The biggest single separator in this matchup is MMLU, where the scores are 65 and 86.9.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 54. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o3-mini has the edge for coding in this comparison, averaging 49.3 versus 35.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o3-mini has the edge for math in this comparison, averaging 87.3 versus 65.1. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 78. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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