Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
LFM2.5-VL-450M
0
Qwen3.6-35B-A3B
64
Verified leaderboard positions: LFM2.5-VL-450M unranked · Qwen3.6-35B-A3B #13
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+38.9 difference
LFM2.5-VL-450M
Qwen3.6-35B-A3B
$0 / $0
N/A
N/A
N/A
N/A
N/A
128K
262K
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6-35B-A3B is clearly ahead on the provisional aggregate, 64 to 0. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-35B-A3B's sharpest advantage is in knowledge, where it averages 60.5 against 21.6. The single biggest benchmark swing on the page is MMLU-Pro, 19.3% to 85.2%.
Qwen3.6-35B-A3B is the reasoning model in the pair, while LFM2.5-VL-450M 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. Qwen3.6-35B-A3B gives you the larger context window at 262K, compared with 128K for LFM2.5-VL-450M.
Qwen3.6-35B-A3B is ahead on BenchLM's provisional leaderboard, 64 to 0. The biggest single separator in this matchup is MMLU-Pro, where the scores are 19.3% and 85.2%.
Qwen3.6-35B-A3B has the edge for knowledge tasks in this comparison, averaging 60.5 versus 21.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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