Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
o3-mini
56
Qwen3 235B 2507
34
Pick o3-mini if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+1.0 difference
o3-mini
Qwen3 235B 2507
$1.1 / $4.4
$0 / $0
160 t/s
N/A
7.12s
N/A
200K
128K
Pick o3-mini if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
o3-mini is clearly ahead on the provisional aggregate, 56 to 34. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini's sharpest advantage is in knowledge, where it averages 77.2 against 76.2. The single biggest benchmark swing on the page is GPQA, 77.2% to 77.5%.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3 235B 2507. That is roughly Infinityx on output cost alone. o3-mini 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. o3-mini gives you the larger context window at 200K, compared with 128K for Qwen3 235B 2507.
o3-mini is ahead on BenchLM's provisional leaderboard, 56 to 34. The biggest single separator in this matchup is GPQA, where the scores are 77.2% and 77.5%.
o3-mini has the edge for knowledge tasks in this comparison, averaging 77.2 versus 76.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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