Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1
57
MiniMax M3
76
Verified leaderboard positions: GPT-4.1 unranked · MiniMax M3 #12
Pick MiniMax M3 if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Coding
+12.4 difference
GPT-4.1
MiniMax M3
$2 / $8
$0.3 / $1.2
108 t/s
N/A
1.02s
N/A
1M
1M
Pick MiniMax M3 if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
MiniMax M3 is clearly ahead on the provisional aggregate, 76 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M3's sharpest advantage is in coding, where it averages 67 against 54.6. The single biggest benchmark swing on the page is SWE-bench Verified, 54.6% to 80.5%.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M3. That is roughly 6.7x on output cost alone.
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 76 to 57. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 54.6% and 80.5%.
MiniMax M3 has the edge for coding in this comparison, averaging 67 versus 54.6. Inside this category, SWE-bench Verified 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.