Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 E2B
23
Qwen3.5 397B
64
Verified leaderboard positions: Gemma 4 E2B unranked · Qwen3.5 397B #17
Pick Qwen3.5 397B if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Knowledge
+11.1 difference
Gemma 4 E2B
Qwen3.5 397B
$0 / $0
$0.6 / $3.6
N/A
96 t/s
N/A
2.44s
128K
128K
Pick Qwen3.5 397B if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Qwen3.5 397B is clearly ahead on the provisional aggregate, 64 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5 397B's sharpest advantage is in knowledge, where it averages 65.2 against 54.1. The single biggest benchmark swing on the page is GPQA, 43.4% to 88.4%.
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 E2B. That is roughly Infinityx on output cost alone. Gemma 4 E2B 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.
Qwen3.5 397B is ahead on BenchLM's provisional leaderboard, 64 to 23. The biggest single separator in this matchup is GPQA, where the scores are 43.4% and 88.4%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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