Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
63
Mistral Medium 3.5 128B
78
Verified leaderboard positions: Kimi K2.5 #17 · Mistral Medium 3.5 128B unranked
Pick Mistral Medium 3.5 128B if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+13.4 difference
Kimi K2.5
Mistral Medium 3.5 128B
$0.6 / $3
$1.5 / $7.5
45 t/s
N/A
2.38s
N/A
256K
256K
Pick Mistral Medium 3.5 128B if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Mistral Medium 3.5 128B is clearly ahead on the provisional aggregate, 78 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mistral Medium 3.5 128B's sharpest advantage is in coding, where it averages 77.6 against 64.2. The single biggest benchmark swing on the page is SWE-bench Verified, 76.8% to 77.6%.
Mistral Medium 3.5 128B is also the more expensive model on tokens at $1.50 input / $7.50 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. That is roughly 2.5x on output cost alone. Mistral Medium 3.5 128B is the reasoning model in the pair, while Kimi K2.5 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.
Mistral Medium 3.5 128B is ahead on BenchLM's provisional leaderboard, 78 to 63. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 76.8% and 77.6%.
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 64.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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