Head-to-head comparison across 5benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.2
83
Kimi K2.5
68
Verified leaderboard positions: GPT-5.2 unranked · Kimi K2.5 #9
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
+1.0 difference
GPT-5.2
Kimi K2.5
$2 / $8
$0.5 / $2.8
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, 83 to 68. 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 $2.00 input / $8.00 output per 1M tokens, versus $0.50 input / $2.80 output per 1M tokens for Kimi K2.5. That is roughly 2.9x 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, 83 to 68. 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, GPQA 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, SWE-bench Pro 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. GPT-5.2 stays close enough that the answer can still flip depending on your workload.
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 55.2 versus 54.6. Inside this category, BrowseComp 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 79.5 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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