Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 26B A4B
55
Kimi K2.5
64
Verified leaderboard positions: Gemma 4 26B A4B unranked · Kimi K2.5 #13
Pick Kimi K2.5 if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Knowledge
+15.9 difference
Multimodal
+4.7 difference
Gemma 4 26B A4B
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 26B A4B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Kimi K2.5 is clearly ahead on the provisional aggregate, 64 to 55. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5's sharpest advantage is in knowledge, where it averages 65.1 against 49.2. The single biggest benchmark swing on the page is HLE, 17.2% to 30.1%.
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 26B A4B. That is roughly Infinityx on output cost alone. Gemma 4 26B A4B 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, 64 to 55. The biggest single separator in this matchup is HLE, where the scores are 17.2% and 30.1%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 49.2. Inside this category, HLE 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 73.8. 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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