Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
68
MiMo-V2.5
74
Verified leaderboard positions: Kimi K2.5 #9 · MiMo-V2.5 unranked
Pick MiMo-V2.5 if you want the stronger benchmark profile. Kimi K2.5 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.
Agentic
+11.2 difference
Coding
+8.1 difference
Multimodal
+0.6 difference
Kimi K2.5
MiMo-V2.5
$0.5 / $2.8
$0.4 / $2
45 t/s
N/A
2.38s
N/A
256K
1M
Pick MiMo-V2.5 if you want the stronger benchmark profile. Kimi K2.5 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 68. 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 agentic, where it averages 65.8 against 54.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50.8% to 65.8%. Kimi K2.5 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Kimi K2.5 is also the more expensive model on tokens at $0.50 input / $2.80 output per 1M tokens, versus $0.40 input / $2.00 output per 1M tokens for MiMo-V2.5. MiMo-V2.5 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. MiMo-V2.5 gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 74 to 68. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50.8% and 65.8%.
Kimi K2.5 has the edge for coding in this comparison, averaging 64.2 versus 56.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 54.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for multimodal and grounded tasks in this comparison, averaging 78.5 versus 77.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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