Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 E4B
36
Kimi K2.5
64
Verified leaderboard positions: Gemma 4 E4B unranked · Kimi K2.5 #13
Pick Kimi K2.5 if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Knowledge
+0.5 difference
Gemma 4 E4B
Kimi K2.5
$0 / $0
$0.6 / $3
N/A
45 t/s
N/A
2.38s
128K
256K
Pick Kimi K2.5 if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi K2.5 is clearly ahead on the provisional aggregate, 64 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
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 E4B. That is roughly Infinityx on output cost alone. Gemma 4 E4B 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 gives you the larger context window at 256K, compared with 128K for Gemma 4 E4B.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 64 to 36. The biggest single separator in this matchup is GPQA, where the scores are 58.6% and 87.6%.
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 65.6 versus 65.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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