Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 mini
47
MiMo-V2.5
74
Pick MiMo-V2.5 if you want the stronger benchmark profile. GPT-4.1 mini 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
+32.5 difference
GPT-4.1 mini
MiMo-V2.5
$0.4 / $1.6
$0.4 / $2
80 t/s
N/A
0.76s
N/A
1M
1M
Pick MiMo-V2.5 if you want the stronger benchmark profile. GPT-4.1 mini 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.
MiMo-V2.5 is clearly ahead on the provisional aggregate, 74 to 47. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5's sharpest advantage is in coding, where it averages 56.1 against 23.6.
MiMo-V2.5 is also the more expensive model on tokens at $0.40 input / $2.00 output per 1M tokens, versus $0.40 input / $1.60 output per 1M tokens for GPT-4.1 mini. MiMo-V2.5 is the reasoning model in the pair, while GPT-4.1 mini 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.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 74 to 47.
MiMo-V2.5 has the edge for coding in this comparison, averaging 56.1 versus 23.6. GPT-4.1 mini stays close enough that the answer can still flip depending on your workload.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.