Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
65
Qwen3.5 397B
64
Verified leaderboard positions: Gemma 4 31B unranked · Qwen3.5 397B #17
Pick Gemma 4 31B if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+18.7 difference
Knowledge
+3.9 difference
Multimodal
+2.7 difference
Gemma 4 31B
Qwen3.5 397B
$0 / $0
$0.6 / $3.6
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 coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 31B finishes one point ahead on BenchLM's provisional leaderboard, 65 to 64. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Qwen3.5 397B is also the more expensive model on tokens at $0.60 input / $3.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 31B. That is roughly Infinityx on output cost alone. 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, 65 to 64. 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 coding in this comparison, averaging 60.3 versus 41.6. Gemma 4 31B stays close enough that the answer can still flip depending on your workload.
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 76.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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