Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2.5 is clearly ahead on the aggregate, 60 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5's sharpest advantage is in mathematics, where it averages 78.7 against 67.1. The single biggest benchmark swing on the page is SWE-bench Verified, 42 to 24. Seed 1.6 Flash does hit back in multimodal & grounded, 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 $0.08 input / $0.30 output per 1M tokens for Seed 1.6 Flash. That is roughly 9.3x on output cost alone. Seed 1.6 Flash 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 Flash gives you the larger context window at 256K, compared with 128K for Kimi K2.5.
Pick Kimi K2.5 if you want the stronger benchmark profile. Seed 1.6 Flash only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
Kimi K2.5
52.3
Seed 1.6 Flash
54.5
Kimi K2.5
38.9
Seed 1.6 Flash
27.6
Kimi K2.5
64.6
Seed 1.6 Flash
73.1
Kimi K2.5
71.7
Seed 1.6 Flash
66.8
Kimi K2.5
57.2
Seed 1.6 Flash
47.3
Kimi K2.5
85
Seed 1.6 Flash
81
Kimi K2.5
79.8
Seed 1.6 Flash
72.8
Kimi K2.5
78.7
Seed 1.6 Flash
67.1
Kimi K2.5 is ahead overall, 60 to 56. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 42 and 24.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 47.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for coding in this comparison, averaging 38.9 versus 27.6. Inside this category, SWE-bench Verified 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 67.1. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 71.7 versus 66.8. Inside this category, SimpleQA 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 52.3. Inside this category, BrowseComp 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 64.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 85 versus 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for multilingual tasks in this comparison, averaging 79.8 versus 72.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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