Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
56
MiniMax M2.7
53
Pick DeepSeek V3.2 if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you need the larger 200K context window.
Coding
+7.2 difference
DeepSeek V3.2
MiniMax M2.7
$0.28 / $0.42
$0.3 / $1.2
35 t/s
45 t/s
3.75s
2.53s
128K
200K
Pick DeepSeek V3.2 if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you need the larger 200K context window.
DeepSeek V3.2 has the cleaner provisional overall profile here, landing at 56 versus 53. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
DeepSeek V3.2's sharpest advantage is in coding, where it averages 60.9 against 53.7. The single biggest benchmark swing on the page is SWE-Rebench, 60.9% to 51.9%.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 2.9x on output cost alone. MiniMax M2.7 gives you the larger context window at 200K, compared with 128K for DeepSeek V3.2.
DeepSeek V3.2 is ahead on BenchLM's provisional leaderboard, 56 to 53. The biggest single separator in this matchup is SWE-Rebench, where the scores are 60.9% and 51.9%.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 60.9 versus 53.7. Inside this category, SWE-Rebench is the benchmark that creates the most daylight between them.
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