Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 3.1 Pro
93
Kimi K2.5
68
Verified leaderboard positions: Gemini 3.1 Pro unranked · Kimi K2.5 #9
Pick Gemini 3.1 Pro if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if you want the cheaper token bill.
Reasoning
+16.1 difference
Multimodal
+5.4 difference
Gemini 3.1 Pro
Kimi K2.5
$1.25 / $5
$0.5 / $2.8
109 t/s
45 t/s
29.71s
2.38s
1M
256K
Pick Gemini 3.1 Pro if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if you want the cheaper token bill.
Gemini 3.1 Pro is clearly ahead on the provisional aggregate, 93 to 68. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.1 Pro's sharpest advantage is in reasoning, where it averages 77.1 against 61. The single biggest benchmark swing on the page is MMMU-Pro, 83.9% to 78.5%.
Gemini 3.1 Pro is also the more expensive model on tokens at $1.25 input / $5.00 output per 1M tokens, versus $0.50 input / $2.80 output per 1M tokens for Kimi K2.5. Gemini 3.1 Pro gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
Gemini 3.1 Pro is ahead on BenchLM's provisional leaderboard, 93 to 68. The biggest single separator in this matchup is MMMU-Pro, where the scores are 83.9% and 78.5%.
Gemini 3.1 Pro has the edge for reasoning in this comparison, averaging 77.1 versus 61. Kimi K2.5 stays close enough that the answer can still flip depending on your workload.
Gemini 3.1 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 83.9 versus 78.5. 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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