Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 2.5 Pro
66
MiMo-V2.5
74
Pick MiMo-V2.5 if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+7.7 difference
Gemini 2.5 Pro
MiMo-V2.5
$1.25 / $5
$0.4 / $2
117 t/s
N/A
21.19s
N/A
1M
1M
Pick MiMo-V2.5 if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if coding is the priority 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 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $5.00 output per 1M tokens, versus $0.40 input / $2.00 output per 1M tokens for MiMo-V2.5. That is roughly 2.5x on output cost alone. MiMo-V2.5 is the reasoning model in the pair, while Gemini 2.5 Pro 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 66.
Gemini 2.5 Pro has the edge for coding in this comparison, averaging 63.8 versus 56.1. MiMo-V2.5 stays close enough that the answer can still flip depending on your workload.
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