Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
74
Qwen3.5 397B
66
Verified leaderboard positions: Gemma 4 31B unranked · Qwen3.5 397B #8
Pick Gemma 4 31B if you want the stronger benchmark profile. Qwen3.5 397B 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
+3.9 difference
Multimodal
+2.1 difference
Gemma 4 31B
Qwen3.5 397B
$0 / $0
$0 / $0
N/A
96 t/s
N/A
2.44s
256K
128K
Pick Gemma 4 31B if you want the stronger benchmark profile. Qwen3.5 397B 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 31B is clearly ahead on the provisional aggregate, 74 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 31B is the reasoning model in the pair, while Qwen3.5 397B 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 31B gives you the larger context window at 256K, compared with 128K for Qwen3.5 397B.
Gemma 4 31B is ahead on BenchLM's provisional leaderboard, 74 to 66. The biggest single separator in this matchup is GPQA, where the scores are 84.3% and 88.4%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 61.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 79 versus 76.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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