Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 26B A4B
55
Qwen3 235B 2507
33
Pick Gemma 4 26B A4B if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+27.0 difference
Gemma 4 26B A4B
Qwen3 235B 2507
$0 / $0
$0 / $0
N/A
N/A
N/A
N/A
256K
128K
Pick Gemma 4 26B A4B if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 26B A4B is clearly ahead on the provisional aggregate, 55 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 26B A4B 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. Gemma 4 26B A4B gives you the larger context window at 256K, compared with 128K for Qwen3 235B 2507.
Gemma 4 26B A4B is ahead on BenchLM's provisional leaderboard, 55 to 33. The biggest single separator in this matchup is MMLU-Pro, where the scores are 82.6% and 83%.
Qwen3 235B 2507 has the edge for knowledge tasks in this comparison, averaging 76.2 versus 49.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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