Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
35
Gemma 4 12B
53
Pick Gemma 4 12B if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+32.8 difference
Knowledge
+7.8 difference
DeepSeek V3
Gemma 4 12B
$0.27 / $1.1
N/A
N/A
N/A
N/A
N/A
128K
256K
Pick Gemma 4 12B if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 12B is clearly ahead on the provisional aggregate, 53 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 12B's sharpest advantage is in coding, where it averages 72 against 39.2. The single biggest benchmark swing on the page is LiveCodeBench, 37.6% to 72%.
Gemma 4 12B 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 12B gives you the larger context window at 256K, compared with 128K for DeepSeek V3.
Gemma 4 12B is ahead on BenchLM's provisional leaderboard, 53 to 35. The biggest single separator in this matchup is LiveCodeBench, where the scores are 37.6% and 72%.
Gemma 4 12B has the edge for knowledge tasks in this comparison, averaging 77.8 versus 70. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 12B has the edge for coding in this comparison, averaging 72 versus 39.2. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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