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 61. 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 agentic, where it averages 62.3 against 52.5. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63 to 52. Mistral Large 3 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Mistral Large 3 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $0.25 input / $2.00 output per 1M tokens for Seed 1.6. That is roughly 3.0x on output cost alone. Seed 1.6 is the reasoning model in the pair, while Mistral Large 3 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. Seed 1.6 gives you the larger context window at 256K, compared with 128K for Mistral Large 3.
Pick Seed 1.6 if you want the stronger benchmark profile. Mistral Large 3 only becomes the better choice if mathematics is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Seed 1.6
62.3
Mistral Large 3
52.5
Seed 1.6
42.4
Mistral Large 3
41
Seed 1.6
79.6
Mistral Large 3
75.5
Seed 1.6
74.5
Mistral Large 3
70.6
Seed 1.6
56.4
Mistral Large 3
57.1
Seed 1.6
87
Mistral Large 3
83
Seed 1.6
83.4
Mistral Large 3
78.8
Seed 1.6
75.9
Mistral Large 3
77.3
Seed 1.6 is ahead overall, 65 to 61. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63 and 52.
Mistral Large 3 has the edge for knowledge tasks in this comparison, averaging 57.1 versus 56.4. 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. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for math in this comparison, averaging 77.3 versus 75.9. 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 70.6. 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 52.5. 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 75.5. 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. 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 78.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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