Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 nano
27
Kimi K2.5
64
Verified leaderboard positions: GPT-4.1 nano unranked · Kimi K2.5 #11
Pick Kimi K2.5 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Knowledge
+14.8 difference
Inst. Following
+10.7 difference
GPT-4.1 nano
Kimi K2.5
$0.1 / $0.4
$0.6 / $3
181 t/s
45 t/s
0.63s
2.38s
1M
256K
Pick Kimi K2.5 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Kimi K2.5 is clearly ahead on the provisional aggregate, 64 to 27. 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 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 87.6%.
Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 7.5x on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 64 to 27. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 87.6%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 93.9 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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