Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.2
83
MiMo-V2.5
74
Pick GPT-5.2 if you want the stronger benchmark profile. MiMo-V2.5 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Agentic
+10.6 difference
Coding
+8.6 difference
Multimodal
+1.6 difference
GPT-5.2
MiMo-V2.5
$2 / $8
$0.4 / $2
73 t/s
N/A
130.34s
N/A
400K
1M
Pick GPT-5.2 if you want the stronger benchmark profile. MiMo-V2.5 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
GPT-5.2 is clearly ahead on the provisional aggregate, 83 to 74. 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 coding, where it averages 64.7 against 56.1. The single biggest benchmark swing on the page is MMMU-Pro, 79.5% to 77.9%. MiMo-V2.5 does hit back in agentic, 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 $0.40 input / $2.00 output per 1M tokens for MiMo-V2.5. That is roughly 4.0x on output cost alone. MiMo-V2.5 gives you the larger context window at 1M, compared with 400K for GPT-5.2.
GPT-5.2 is ahead on BenchLM's provisional leaderboard, 83 to 74. The biggest single separator in this matchup is MMMU-Pro, where the scores are 79.5% and 77.9%.
GPT-5.2 has the edge for coding in this comparison, averaging 64.7 versus 56.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 55.2. GPT-5.2 stays close enough that the answer can still flip depending on your workload.
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 79.5 versus 77.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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