Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 nano
60
Kimi K2.5
64
Verified leaderboard positions: GPT-5.4 nano unranked · Kimi K2.5 #11
Pick Kimi K2.5 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you want the cheaper token bill or you need the larger 400K context window.
Agentic
+11.7 difference
Knowledge
+11.9 difference
Multimodal
+12.4 difference
GPT-5.4 nano
Kimi K2.5
$0.2 / $1.25
$0.6 / $3
191 t/s
45 t/s
3.64s
2.38s
400K
256K
Pick Kimi K2.5 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you want the cheaper token bill or you need the larger 400K context window.
Kimi K2.5 is clearly ahead on the provisional aggregate, 64 to 60. 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 multimodal & grounded, where it averages 78.5 against 66.1. The single biggest benchmark swing on the page is MMMU-Pro, 66.1% to 78.5%.
Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.20 input / $1.25 output per 1M tokens for GPT-5.4 nano. That is roughly 2.4x on output cost alone. GPT-5.4 nano 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. GPT-5.4 nano gives you the larger context window at 400K, compared with 256K for Kimi K2.5.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 64 to 60. The biggest single separator in this matchup is MMMU-Pro, where the scores are 66.1% and 78.5%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 53.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for agentic tasks in this comparison, averaging 54.6 versus 42.9. Inside this category, MCP Atlas 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 66.1. 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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