Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.2
81
MiniMax M2.7
62
Pick GPT-5.2 if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Agentic
+1.8 difference
Coding
+11.0 difference
GPT-5.2
MiniMax M2.7
$1.75 / $14
$0.3 / $1.2
73 t/s
45 t/s
130.34s
2.53s
400K
200K
Pick GPT-5.2 if you want the stronger benchmark profile. MiniMax M2.7 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, 81 to 62. 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 53.7. The single biggest benchmark swing on the page is SWE-bench Pro, 55.6% to 56.2%. MiniMax M2.7 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 $1.75 input / $14.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 11.7x on output cost alone. GPT-5.2 is the reasoning model in the pair, while MiniMax M2.7 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 200K for MiniMax M2.7.
GPT-5.2 is ahead on BenchLM's provisional leaderboard, 81 to 62. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 55.6% and 56.2%.
GPT-5.2 has the edge for coding in this comparison, averaging 64.7 versus 53.7. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 55.2. GPT-5.2 stays close enough that the answer can still flip depending on your workload.
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