Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 mini
71
Kimi K2.5
64
Verified leaderboard positions: GPT-5.4 mini unranked · Kimi K2.5 #11
Pick GPT-5.4 mini if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+11.0 difference
Knowledge
+7.7 difference
Multimodal
+1.9 difference
GPT-5.4 mini
Kimi K2.5
$0.75 / $4.5
$0.6 / $3
201 t/s
45 t/s
3.85s
2.38s
400K
256K
Pick GPT-5.4 mini if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-5.4 mini is clearly ahead on the provisional aggregate, 71 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 mini's sharpest advantage is in agentic, where it averages 65.6 against 54.6. The single biggest benchmark swing on the page is HLE, 41.5% to 30.1%. Kimi K2.5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-5.4 mini is also the more expensive model on tokens at $0.75 input / $4.50 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. GPT-5.4 mini 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 mini gives you the larger context window at 400K, compared with 256K for Kimi K2.5.
GPT-5.4 mini is ahead on BenchLM's provisional leaderboard, 71 to 64. The biggest single separator in this matchup is HLE, where the scores are 41.5% and 30.1%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 57.4. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.4 mini has the edge for agentic tasks in this comparison, averaging 65.6 versus 54.6. 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 76.6. 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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