Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash Base
31
Gemma 4 E2B
27
Pick DeepSeek V4 Flash Base if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Knowledge
+1.9 difference
DeepSeek V4 Flash Base
Gemma 4 E2B
$null / $null
$0 / $0
N/A
N/A
N/A
N/A
1M
128K
Pick DeepSeek V4 Flash Base if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
DeepSeek V4 Flash Base is clearly ahead on the provisional aggregate, 31 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 E2B is the reasoning model in the pair, while DeepSeek V4 Flash Base 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. DeepSeek V4 Flash Base gives you the larger context window at 1M, compared with 128K for Gemma 4 E2B.
DeepSeek V4 Flash Base is ahead on BenchLM's provisional leaderboard, 31 to 27. The biggest single separator in this matchup is MMLU-Pro, where the scores are 68.3% and 60%.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 versus 52.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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