Head-to-head comparison across 5benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.2
79
Kimi K2.5
64
Verified leaderboard positions: GPT-5.2 unranked · Kimi K2.5 #15
Pick GPT-5.2 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if reasoning is the priority or you want the cheaper token bill.
Agentic
+0.6 difference
Coding
+0.5 difference
Reasoning
+8.1 difference
Knowledge
+27.3 difference
Multimodal
+1.8 difference
GPT-5.2
Kimi K2.5
$1.75 / $14
$0.6 / $3
73 t/s
45 t/s
130.34s
2.38s
400K
256K
Pick GPT-5.2 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if reasoning is the priority or you want the cheaper token bill.
GPT-5.2 is clearly ahead on the provisional aggregate, 79 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in knowledge, where it averages 92.4 against 65.1. The single biggest benchmark swing on the page is BrowseComp, 65.8% to 60.6%. Kimi K2.5 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
GPT-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. That is roughly 4.7x on output cost alone. GPT-5.2 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-5.2 gives you the larger context window at 400K, compared with 256K for Kimi K2.5.
GPT-5.2 is ahead on BenchLM's provisional leaderboard, 79 to 64. The biggest single separator in this matchup is BrowseComp, where the scores are 65.8% and 60.6%.
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 92.4 versus 65.1. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for coding in this comparison, averaging 64.7 versus 64.2. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 61 versus 52.9. Inside this category, CritPt is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 55.2 versus 54.6. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 80.3 versus 78.5. 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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