Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
63
LFM2.5-230M
17
Verified leaderboard positions: Kimi K2.5 #18 · LFM2.5-230M unranked
Pick Kimi K2.5 if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill.
Knowledge
+44.8 difference
Inst. Following
+22.2 difference
Kimi K2.5
LFM2.5-230M
$0.6 / $3
$0 / $0
45 t/s
N/A
2.38s
N/A
256K
32K
Pick Kimi K2.5 if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill.
Kimi K2.5 is clearly ahead on the provisional aggregate, 63 to 17. 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 knowledge, where it averages 65.1 against 20.3. The single biggest benchmark swing on the page is MMLU-Pro, 87.1% to 20.3%.
Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-230M. That is roughly Infinityx on output cost alone. Kimi K2.5 gives you the larger context window at 256K, compared with 32K for LFM2.5-230M.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 63 to 17. The biggest single separator in this matchup is MMLU-Pro, where the scores are 87.1% and 20.3%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 20.3. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 93.9 versus 71.7. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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