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 49. 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 mathematics, where it averages 75.9 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 74 to 9.8.
Seed 1.6 is also the more expensive model on tokens at $0.25 input / $2.00 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 5.0x on output cost alone. Seed 1.6 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 Seed 1.6.
Pick Seed 1.6 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Seed 1.6
62.3
GPT-4.1 nano
47.4
Seed 1.6
42.4
GPT-4.1 nano
18
Seed 1.6
79.6
GPT-4.1 nano
59.3
Seed 1.6
74.5
GPT-4.1 nano
74.1
Seed 1.6
56.4
GPT-4.1 nano
50.7
Seed 1.6
87
GPT-4.1 nano
83.2
Seed 1.6
83.4
GPT-4.1 nano
59
Seed 1.6
75.9
GPT-4.1 nano
9.8
Seed 1.6 is ahead overall, 65 to 49. The biggest single separator in this matchup is AIME 2024, where the scores are 74 and 9.8.
Seed 1.6 has the edge for knowledge tasks in this comparison, averaging 56.4 versus 50.7. Inside this category, GPQA 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 18. Inside this category, SWE-bench Pro 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 9.8. Inside this category, AIME 2024 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 74.1. Inside this category, MRCRv2 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 47.4. 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 59.3. 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 83.2. 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 59. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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