Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
LFM2.5-230M
17
Qwen 3.6 Max (preview)
78
Pick Qwen 3.6 Max (preview) 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
+53.6 difference
LFM2.5-230M
Qwen 3.6 Max (preview)
$0 / $0
N/A
N/A
N/A
N/A
N/A
32K
256K
Pick Qwen 3.6 Max (preview) 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.
Qwen 3.6 Max (preview) is clearly ahead on the provisional aggregate, 78 to 17. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen 3.6 Max (preview)'s sharpest advantage is in knowledge, where it averages 73.9 against 20.3.
Qwen 3.6 Max (preview) 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. Qwen 3.6 Max (preview) gives you the larger context window at 256K, compared with 32K for LFM2.5-230M.
Qwen 3.6 Max (preview) is ahead on BenchLM's provisional leaderboard, 78 to 17.
Qwen 3.6 Max (preview) has the edge for knowledge tasks in this comparison, averaging 73.9 versus 20.3. LFM2.5-230M stays close enough that the answer can still flip depending on your workload.
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