Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Qwen3 235B 2507
33
Qwen3.5-27B
63
Verified leaderboard positions: Qwen3 235B 2507 unranked · Qwen3.5-27B #16
Pick Qwen3.5-27B 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
+4.4 difference
Multilingual
+2.8 difference
Qwen3 235B 2507
Qwen3.5-27B
$0 / $0
$0 / $0
N/A
N/A
N/A
N/A
128K
262K
Pick Qwen3.5-27B 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.
Qwen3.5-27B is clearly ahead on the provisional aggregate, 63 to 33. 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 76.2. The single biggest benchmark swing on the page is GPQA, 77.5% to 85.5%.
Qwen3.5-27B 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. Qwen3.5-27B gives you the larger context window at 262K, compared with 128K for Qwen3 235B 2507.
Qwen3.5-27B is ahead on BenchLM's provisional leaderboard, 63 to 33. The biggest single separator in this matchup is GPQA, where the scores are 77.5% and 85.5%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 76.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-27B 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.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.