Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash
59
Gemma 4 E4B
40
Verified leaderboard positions: DeepSeek V4 Flash #23 · Gemma 4 E4B unranked
Pick DeepSeek V4 Flash if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Knowledge
+20.4 difference
DeepSeek V4 Flash
Gemma 4 E4B
$0.14 / $0.28
$0 / $0
N/A
N/A
N/A
N/A
1M
128K
Pick DeepSeek V4 Flash if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
DeepSeek V4 Flash is clearly ahead on the provisional aggregate, 59 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V4 Flash is also the more expensive model on tokens at $0.14 input / $0.28 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 V4 Flash 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 gives you the larger context window at 1M, compared with 128K for Gemma 4 E4B.
DeepSeek V4 Flash is ahead on BenchLM's provisional leaderboard, 59 to 40. The biggest single separator in this matchup is MMLU-Pro, where the scores are 83% and 69.4%.
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 65.6 versus 45.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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