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 60. 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 64.6. The single biggest benchmark swing on the page is MMMU-Pro, 80 to 61. Kimi K2.5 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Kimi K2.5 is also the more expensive model on tokens at $0.50 input / $2.80 output per 1M tokens, versus $0.25 input / $2.00 output per 1M tokens for Seed 1.6. Seed 1.6 is the reasoning model in the pair, while Kimi K2.5 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 Kimi K2.5.
Pick Seed 1.6 if you want the stronger benchmark profile. Kimi K2.5 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
Kimi K2.5
52.3
Seed 1.6
42.4
Kimi K2.5
38.9
Seed 1.6
79.6
Kimi K2.5
64.6
Seed 1.6
74.5
Kimi K2.5
71.7
Seed 1.6
56.4
Kimi K2.5
57.2
Seed 1.6
87
Kimi K2.5
85
Seed 1.6
83.4
Kimi K2.5
79.8
Seed 1.6
75.9
Kimi K2.5
78.7
Seed 1.6 is ahead overall, 65 to 60. The biggest single separator in this matchup is MMMU-Pro, where the scores are 80 and 61.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 57.2 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 38.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for math in this comparison, averaging 78.7 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 71.7. Inside this category, LongBench v2 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 52.3. 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 64.6. 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 85. 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 79.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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