Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
LFM2.5-230M
17
Qwen3.5 397B
62
Verified leaderboard positions: LFM2.5-230M unranked · Qwen3.5 397B #22
Pick Qwen3.5 397B if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill.
Knowledge
+44.9 difference
Inst. Following
+20.9 difference
LFM2.5-230M
Qwen3.5 397B
$0 / $0
$0.6 / $3.6
N/A
96 t/s
N/A
2.44s
32K
128K
Pick Qwen3.5 397B if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you want the cheaper token bill.
Qwen3.5 397B is clearly ahead on the provisional aggregate, 62 to 17. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5 397B's sharpest advantage is in knowledge, where it averages 65.2 against 20.3. The single biggest benchmark swing on the page is MMLU-Pro, 20.3% to 87.8%.
Qwen3.5 397B is also the more expensive model on tokens at $0.60 input / $3.60 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. Qwen3.5 397B gives you the larger context window at 128K, compared with 32K for LFM2.5-230M.
Qwen3.5 397B is ahead on BenchLM's provisional leaderboard, 62 to 17. The biggest single separator in this matchup is MMLU-Pro, where the scores are 20.3% and 87.8%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 20.3. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for instruction following in this comparison, averaging 92.6 versus 71.7. Inside this category, IFEval is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.