Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
35
Gemma 4 E4B
36
Pick Gemma 4 E4B if you want the stronger benchmark profile. DeepSeek V3 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
+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 Gemma 4 E4B if you want the stronger benchmark profile. DeepSeek V3 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 E4B finishes one point ahead on BenchLM's provisional leaderboard, 36 to 35. 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.
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.
Gemma 4 E4B is ahead on BenchLM's provisional leaderboard, 36 to 35. 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, MMLU-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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