Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash (Max)
74
Mistral Medium 3.5 128B
78
Verified leaderboard positions: DeepSeek V4 Flash (Max) #19 · Mistral Medium 3.5 128B unranked
Pick Mistral Medium 3.5 128B if you want the stronger benchmark profile. DeepSeek V4 Flash (Max) only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Coding
+3.9 difference
DeepSeek V4 Flash (Max)
Mistral Medium 3.5 128B
$0.14 / $0.28
$1.5 / $7.5
N/A
N/A
N/A
N/A
1M
256K
Pick Mistral Medium 3.5 128B if you want the stronger benchmark profile. DeepSeek V4 Flash (Max) only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Mistral Medium 3.5 128B is clearly ahead on the provisional aggregate, 78 to 74. 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 73.7. The single biggest benchmark swing on the page is SWE-bench Verified, 79% 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 $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (Max). That is roughly 26.8x on output cost alone. DeepSeek V4 Flash (Max) gives you the larger context window at 1M, compared with 256K for Mistral Medium 3.5 128B.
Mistral Medium 3.5 128B is ahead on BenchLM's provisional leaderboard, 78 to 74. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 79% and 77.6%.
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 73.7. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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