Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Seed-2.0-Lite has the cleaner overall profile here, landing at 63 versus 60. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Seed-2.0-Lite'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-2.0-Lite. Seed-2.0-Lite gives you the larger context window at 256K, compared with 128K for Kimi K2.5.
Pick Seed-2.0-Lite if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if mathematics is the priority.
Seed-2.0-Lite
55.1
Kimi K2.5
52.3
Seed-2.0-Lite
41.4
Kimi K2.5
38.9
Seed-2.0-Lite
79.6
Kimi K2.5
64.6
Seed-2.0-Lite
73
Kimi K2.5
71.7
Seed-2.0-Lite
53.9
Kimi K2.5
57.2
Seed-2.0-Lite
89
Kimi K2.5
85
Seed-2.0-Lite
82.5
Kimi K2.5
79.8
Seed-2.0-Lite
75
Kimi K2.5
78.7
Seed-2.0-Lite is ahead overall, 63 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 53.9. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Seed-2.0-Lite has the edge for coding in this comparison, averaging 41.4 versus 38.9. Inside this category, HumanEval 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. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Seed-2.0-Lite has the edge for reasoning in this comparison, averaging 73 versus 71.7. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Seed-2.0-Lite has the edge for agentic tasks in this comparison, averaging 55.1 versus 52.3. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Seed-2.0-Lite 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-2.0-Lite has the edge for instruction following in this comparison, averaging 89 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Seed-2.0-Lite has the edge for multilingual tasks in this comparison, averaging 82.5 versus 79.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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