Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Seed 1.6 Flash is clearly ahead on the aggregate, 56 to 49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Seed 1.6 Flash's sharpest advantage is in mathematics, where it averages 67.1 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 66 to 9.8. GPT-4.1 nano does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 output per 1M tokens, versus $0.08 input / $0.30 output per 1M tokens for Seed 1.6 Flash. Seed 1.6 Flash 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 Flash.
Pick Seed 1.6 Flash if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if reasoning is the priority or you need the larger 1M context window.
Seed 1.6 Flash
54.5
GPT-4.1 nano
47.4
Seed 1.6 Flash
27.6
GPT-4.1 nano
18
Seed 1.6 Flash
73.1
GPT-4.1 nano
59.3
Seed 1.6 Flash
66.8
GPT-4.1 nano
74.1
Seed 1.6 Flash
47.3
GPT-4.1 nano
50.7
Seed 1.6 Flash
81
GPT-4.1 nano
83.2
Seed 1.6 Flash
72.8
GPT-4.1 nano
59
Seed 1.6 Flash
67.1
GPT-4.1 nano
9.8
Seed 1.6 Flash is ahead overall, 56 to 49. The biggest single separator in this matchup is AIME 2024, where the scores are 66 and 9.8.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 47.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Seed 1.6 Flash has the edge for coding in this comparison, averaging 27.6 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Seed 1.6 Flash has the edge for math in this comparison, averaging 67.1 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for reasoning in this comparison, averaging 74.1 versus 66.8. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Seed 1.6 Flash has the edge for agentic tasks in this comparison, averaging 54.5 versus 47.4. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
Seed 1.6 Flash has the edge for multimodal and grounded tasks in this comparison, averaging 73.1 versus 59.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Seed 1.6 Flash has the edge for multilingual tasks in this comparison, averaging 72.8 versus 59. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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