Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2.5 (Reasoning) is clearly ahead on the aggregate, 76 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in coding, where it averages 64.1 against 18. The single biggest benchmark swing on the page is SWE-bench Pro, 70 to 19.
Kimi K2.5 (Reasoning) is the reasoning model in the pair, while LFM2-24B-A2B 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. Kimi K2.5 (Reasoning) gives you the larger context window at 128K, compared with 32K for LFM2-24B-A2B.
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Kimi K2.5 (Reasoning)
73.1
LFM2-24B-A2B
33.4
Kimi K2.5 (Reasoning)
64.1
LFM2-24B-A2B
18
Kimi K2.5 (Reasoning)
74.3
LFM2-24B-A2B
41.7
Kimi K2.5 (Reasoning)
84.9
LFM2-24B-A2B
46.6
Kimi K2.5 (Reasoning)
69.7
LFM2-24B-A2B
35.6
Kimi K2.5 (Reasoning)
91
LFM2-24B-A2B
68
Kimi K2.5 (Reasoning)
86.7
LFM2-24B-A2B
61.4
Kimi K2.5 (Reasoning)
92.6
LFM2-24B-A2B
50.4
Kimi K2.5 (Reasoning) is ahead overall, 76 to 38. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 70 and 19.
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 69.7 versus 35.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 64.1 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for math in this comparison, averaging 92.6 versus 50.4. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for reasoning in this comparison, averaging 84.9 versus 46.6. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for agentic tasks in this comparison, averaging 73.1 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 74.3 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for instruction following in this comparison, averaging 91 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 86.7 versus 61.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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