Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 nano
59
MiniMax M2.7
53
Pick GPT-5.4 nano 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
+14.1 difference
GPT-5.4 nano
MiniMax M2.7
$0.2 / $1.25
$0.3 / $1.2
191 t/s
45 t/s
3.64s
2.53s
400K
200K
Pick GPT-5.4 nano 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-5.4 nano is clearly ahead on the provisional aggregate, 59 to 53. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. GPT-5.4 nano 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-5.4 nano gives you the larger context window at 400K, compared with 200K for MiniMax M2.7.
GPT-5.4 nano is ahead on BenchLM's provisional leaderboard, 59 to 53. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 46.3% and 57%.
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 42.9. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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