Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Qwen3 235B 2507
33
Qwen3.5-122B-A10B
65
Verified leaderboard positions: Qwen3 235B 2507 unranked · Qwen3.5-122B-A10B #8
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+5.4 difference
Multilingual
+2.8 difference
Qwen3 235B 2507
Qwen3.5-122B-A10B
$0 / $0
$0 / $0
N/A
N/A
N/A
N/A
128K
262K
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-122B-A10B is clearly ahead on the provisional aggregate, 65 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-122B-A10B's sharpest advantage is in knowledge, where it averages 81.6 against 76.2. The single biggest benchmark swing on the page is GPQA, 77.5% to 86.6%.
Qwen3.5-122B-A10B is the reasoning model in the pair, while Qwen3 235B 2507 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. Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 128K for Qwen3 235B 2507.
Qwen3.5-122B-A10B is ahead on BenchLM's provisional leaderboard, 65 to 33. The biggest single separator in this matchup is GPQA, where the scores are 77.5% and 86.6%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 76.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 79.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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