Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
57
MAI-Thinking-1
65
Verified leaderboard positions: DeepSeek V3.2 unranked · MAI-Thinking-1 #23
Pick MAI-Thinking-1 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+10.1 difference
DeepSeek V3.2
MAI-Thinking-1
$0.28 / $0.42
N/A
35 t/s
N/A
3.75s
N/A
128K
256K
Pick MAI-Thinking-1 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
MAI-Thinking-1 is clearly ahead on the provisional aggregate, 65 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MAI-Thinking-1's sharpest advantage is in coding, where it averages 71 against 60.9.
MAI-Thinking-1 is the reasoning model in the pair, while DeepSeek V3.2 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. MAI-Thinking-1 gives you the larger context window at 256K, compared with 128K for DeepSeek V3.2.
MAI-Thinking-1 is ahead on BenchLM's provisional leaderboard, 65 to 57.
MAI-Thinking-1 has the edge for coding in this comparison, averaging 71 versus 60.9. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
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