Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
41
Gemma 4 E4B
38
Pick DeepSeek V3 if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Knowledge
+4.4 difference
DeepSeek V3
Gemma 4 E4B
$0.27 / $1.1
$0 / $0
N/A
N/A
N/A
N/A
128K
128K
Pick DeepSeek V3 if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
DeepSeek V3 has the cleaner provisional overall profile here, landing at 41 versus 38. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
DeepSeek V3's sharpest advantage is in knowledge, where it averages 70 against 65.6. The single biggest benchmark swing on the page is MMLU-Pro, 75.9% to 69.4%.
DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 E4B. That is roughly Infinityx on output cost alone. Gemma 4 E4B is the reasoning model in the pair, while DeepSeek V3 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 V3 is ahead on BenchLM's provisional leaderboard, 41 to 38. The biggest single separator in this matchup is MMLU-Pro, where the scores are 75.9% and 69.4%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 70 versus 65.6. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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