Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 nano
60
MiniMax M2.7
62
Pick MiniMax M2.7 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window or you want the stronger reasoning-first profile.
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 MiniMax M2.7 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window or you want the stronger reasoning-first profile.
MiniMax M2.7 has the cleaner provisional overall profile here, landing at 62 versus 60. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
MiniMax M2.7's sharpest advantage is in agentic, where it averages 57 against 42.9. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 46.3% to 57%.
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.
MiniMax M2.7 is ahead on BenchLM's provisional leaderboard, 62 to 60. 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, Toolathlon is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.