Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1
57
Nemotron 3 Nano Omni 30B A3B
48
Pick GPT-4.1 if you want the stronger benchmark profile. Nemotron 3 Nano Omni 30B A3B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Coding
+1.1 difference
Knowledge
+9.2 difference
Inst. Following
+13.2 difference
GPT-4.1
Nemotron 3 Nano Omni 30B A3B
$2 / $8
$0 / $0
108 t/s
N/A
1.02s
N/A
1M
256K
Pick GPT-4.1 if you want the stronger benchmark profile. Nemotron 3 Nano Omni 30B A3B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-4.1 is clearly ahead on the provisional aggregate, 57 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1's sharpest advantage is in instruction following, where it averages 87.4 against 74.2. The single biggest benchmark swing on the page is GPQA, 66.3% to 72.2%. Nemotron 3 Nano Omni 30B A3B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Nemotron 3 Nano Omni 30B A3B. That is roughly Infinityx on output cost alone. Nemotron 3 Nano Omni 30B A3B is the reasoning model in the pair, while GPT-4.1 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 gives you the larger context window at 1M, compared with 256K for Nemotron 3 Nano Omni 30B A3B.
GPT-4.1 is ahead on BenchLM's provisional leaderboard, 57 to 48. The biggest single separator in this matchup is GPQA, where the scores are 66.3% and 72.2%.
Nemotron 3 Nano Omni 30B A3B has the edge for knowledge tasks in this comparison, averaging 75.5 versus 66.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 54.6 versus 53.5. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 74.2. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
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