Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Sonnet 4.5
65
MiniMax M3
76
Verified leaderboard positions: Claude Sonnet 4.5 unranked · MiniMax M3 #12
Pick MiniMax M3 if you want the stronger benchmark profile. Claude Sonnet 4.5 only becomes the better choice if coding is the priority.
Agentic
+16.6 difference
Coding
+10.2 difference
Claude Sonnet 4.5
MiniMax M3
$3 / $15
$0.3 / $1.2
N/A
N/A
N/A
N/A
200K
1M
Pick MiniMax M3 if you want the stronger benchmark profile. Claude Sonnet 4.5 only becomes the better choice if coding is the priority.
MiniMax M3 is clearly ahead on the provisional aggregate, 76 to 65. 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 agentic, where it averages 71.9 against 55.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50% to 66%. Claude Sonnet 4.5 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Claude Sonnet 4.5 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M3. That is roughly 12.5x on output cost alone. MiniMax M3 gives you the larger context window at 1M, compared with 200K for Claude Sonnet 4.5.
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 76 to 65. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50% and 66%.
Claude Sonnet 4.5 has the edge for coding in this comparison, averaging 77.2 versus 67. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
MiniMax M3 has the edge for agentic tasks in this comparison, averaging 71.9 versus 55.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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