Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Seed 1.6 is clearly ahead on the aggregate, 65 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Seed 1.6's sharpest advantage is in multimodal & grounded, where it averages 79.6 against 60.4. The single biggest benchmark swing on the page is MMMU-Pro, 80 to 55.
Seed 1.6 is the reasoning model in the pair, while Nemotron 3 Super 100B 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. Nemotron 3 Super 100B gives you the larger context window at 1M, compared with 256K for Seed 1.6.
Pick Seed 1.6 if you want the stronger benchmark profile. Nemotron 3 Super 100B 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.
Seed 1.6
62.3
Nemotron 3 Super 100B
56.6
Seed 1.6
42.4
Nemotron 3 Super 100B
41.3
Seed 1.6
79.6
Nemotron 3 Super 100B
60.4
Seed 1.6
74.5
Nemotron 3 Super 100B
69.5
Seed 1.6
56.4
Nemotron 3 Super 100B
52.8
Seed 1.6
87
Nemotron 3 Super 100B
84
Seed 1.6
83.4
Nemotron 3 Super 100B
79.5
Seed 1.6
75.9
Nemotron 3 Super 100B
72.6
Seed 1.6 is ahead overall, 65 to 59. The biggest single separator in this matchup is MMMU-Pro, where the scores are 80 and 55.
Seed 1.6 has the edge for knowledge tasks in this comparison, averaging 56.4 versus 52.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for coding in this comparison, averaging 42.4 versus 41.3. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for math in this comparison, averaging 75.9 versus 72.6. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for reasoning in this comparison, averaging 74.5 versus 69.5. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for agentic tasks in this comparison, averaging 62.3 versus 56.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 60.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for instruction following in this comparison, averaging 87 versus 84. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for multilingual tasks in this comparison, averaging 83.4 versus 79.5. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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