Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Haiku 4.5
56
Mistral Medium 3.5 128B
78
Pick Mistral Medium 3.5 128B if you want the stronger benchmark profile. Claude Haiku 4.5 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
+4.3 difference
Claude Haiku 4.5
Mistral Medium 3.5 128B
$1 / $5
$1.5 / $7.5
N/A
N/A
N/A
N/A
200K
256K
Pick Mistral Medium 3.5 128B if you want the stronger benchmark profile. Claude Haiku 4.5 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 56. 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 73.3. The single biggest benchmark swing on the page is SWE-bench Verified, 73.3% 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 $1.00 input / $5.00 output per 1M tokens for Claude Haiku 4.5. Mistral Medium 3.5 128B is the reasoning model in the pair, while Claude Haiku 4.5 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 gives you the larger context window at 256K, compared with 200K for Claude Haiku 4.5.
Mistral Medium 3.5 128B is ahead on BenchLM's provisional leaderboard, 78 to 56. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.3% and 77.6%.
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 73.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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