Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi 2.6
83
MiMo-V2.5
74
Verified leaderboard positions: Kimi 2.6 #5 · MiMo-V2.5 unranked
Pick Kimi 2.6 if you want the stronger benchmark profile. MiMo-V2.5 only becomes the better choice if you need the larger 1M context window.
Agentic
+7.3 difference
Coding
+15.9 difference
Multimodal
+1.5 difference
Kimi 2.6
MiMo-V2.5
$0 / $0
$0.4 / $2
N/A
N/A
N/A
N/A
256K
1M
Pick Kimi 2.6 if you want the stronger benchmark profile. MiMo-V2.5 only becomes the better choice if you need the larger 1M context window.
Kimi 2.6 is clearly ahead on the provisional aggregate, 83 to 74. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi 2.6's sharpest advantage is in coding, where it averages 72 against 56.1. The single biggest benchmark swing on the page is SWE-bench Pro, 58.6% to 56.1%.
MiMo-V2.5 is also the more expensive model on tokens at $0.40 input / $2.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Kimi 2.6. That is roughly Infinityx on output cost alone. MiMo-V2.5 gives you the larger context window at 1M, compared with 256K for Kimi 2.6.
Kimi 2.6 is ahead on BenchLM's provisional leaderboard, 83 to 74. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 58.6% and 56.1%.
Kimi 2.6 has the edge for coding in this comparison, averaging 72 versus 56.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Kimi 2.6 has the edge for agentic tasks in this comparison, averaging 73.1 versus 65.8. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Kimi 2.6 has the edge for multimodal and grounded tasks in this comparison, averaging 79.4 versus 77.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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