Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 nano
26
MAI-Thinking-1
65
Verified leaderboard positions: GPT-4.1 nano unranked · MAI-Thinking-1 #23
Pick MAI-Thinking-1 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+19.6 difference
Inst. Following
+1.8 difference
GPT-4.1 nano
MAI-Thinking-1
$0.1 / $0.4
N/A
181 t/s
N/A
0.63s
N/A
1M
256K
Pick MAI-Thinking-1 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or 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 26. 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 knowledge, where it averages 69.9 against 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 84.2%.
MAI-Thinking-1 is the reasoning model in the pair, while GPT-4.1 nano 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-4.1 nano gives you the larger context window at 1M, compared with 256K for MAI-Thinking-1.
MAI-Thinking-1 is ahead on BenchLM's provisional leaderboard, 65 to 26. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 84.2%.
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 69.9 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
MAI-Thinking-1 has the edge for instruction following in this comparison, averaging 85 versus 83.2. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
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