Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash Base
29
Gemma 4 12B
53
Pick Gemma 4 12B if you want the stronger benchmark profile. DeepSeek V4 Flash Base only becomes the better choice if reasoning is the priority or you need the larger 1M context window.
Reasoning
+1.3 difference
Knowledge
+25.6 difference
DeepSeek V4 Flash Base
Gemma 4 12B
$null / $null
N/A
N/A
N/A
N/A
N/A
1M
256K
Pick Gemma 4 12B if you want the stronger benchmark profile. DeepSeek V4 Flash Base only becomes the better choice if reasoning is the priority or you need the larger 1M context window.
Gemma 4 12B is clearly ahead on the provisional aggregate, 53 to 29. 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 knowledge, where it averages 77.8 against 52.2. The single biggest benchmark swing on the page is MMLU-Pro, 68.3% to 77.2%. DeepSeek V4 Flash Base does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Gemma 4 12B 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 256K for Gemma 4 12B.
Gemma 4 12B is ahead on BenchLM's provisional leaderboard, 53 to 29. The biggest single separator in this matchup is MMLU-Pro, where the scores are 68.3% and 77.2%.
Gemma 4 12B has the edge for knowledge tasks in this comparison, averaging 77.8 versus 52.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
DeepSeek V4 Flash Base has the edge for reasoning in this comparison, averaging 44.7 versus 43.4. Inside this category, BBH is the benchmark that creates the most daylight between them.
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