Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
62
Kimi K2.5
63
Verified leaderboard positions: Gemma 4 31B unranked · Kimi K2.5 #22
Pick Kimi K2.5 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Coding
+22.6 difference
Knowledge
+3.8 difference
Multimodal
+1.6 difference
Gemma 4 31B
Kimi K2.5
$0 / $0
$0.6 / $3
N/A
45 t/s
N/A
2.38s
256K
256K
Pick Kimi K2.5 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Kimi K2.5 finishes one point ahead on BenchLM's provisional leaderboard, 63 to 62. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Kimi K2.5's sharpest advantage is in coding, where it averages 64.2 against 41.6. The single biggest benchmark swing on the page is SWE-Rebench, 41.6% to 58.5%.
Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 31B. That is roughly Infinityx on output cost alone. Gemma 4 31B 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.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 63 to 62. The biggest single separator in this matchup is SWE-Rebench, where the scores are 41.6% and 58.5%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 61.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for coding in this comparison, averaging 64.2 versus 41.6. Inside this category, SWE-Rebench 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 76.9. Inside this category, AA-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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