Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
LFM2.5-230M
17
Qwen3.5-27B
61
Verified leaderboard positions: LFM2.5-230M unranked · Qwen3.5-27B #23
Pick Qwen3.5-27B if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+60.3 difference
Inst. Following
+23.3 difference
LFM2.5-230M
Qwen3.5-27B
$0 / $0
$0 / $0
N/A
N/A
N/A
N/A
32K
262K
Pick Qwen3.5-27B if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-27B is clearly ahead on the provisional aggregate, 61 to 17. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in knowledge, where it averages 80.6 against 20.3. The single biggest benchmark swing on the page is MMLU-Pro, 20.3% to 86.1%.
Qwen3.5-27B is the reasoning model in the pair, while LFM2.5-230M 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. Qwen3.5-27B gives you the larger context window at 262K, compared with 32K for LFM2.5-230M.
Qwen3.5-27B is ahead on BenchLM's provisional leaderboard, 61 to 17. The biggest single separator in this matchup is MMLU-Pro, where the scores are 20.3% and 86.1%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 20.3. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 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.