Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
57
Gemma 4 31B
65
Pick Gemma 4 31B if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+19.3 difference
DeepSeek V3.2
Gemma 4 31B
$0.28 / $0.42
$0 / $0
35 t/s
N/A
3.75s
N/A
128K
256K
Pick Gemma 4 31B if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 31B is clearly ahead on the provisional aggregate, 65 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2 is also the more expensive model on tokens at $0.28 input / $0.42 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 31B. That is roughly Infinityx on output cost alone. Gemma 4 31B is the reasoning model in the pair, while DeepSeek V3.2 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 31B gives you the larger context window at 256K, compared with 128K for DeepSeek V3.2.
Gemma 4 31B is ahead on BenchLM's provisional leaderboard, 65 to 57. The biggest single separator in this matchup is SWE-Rebench, where the scores are 60.9% and 41.6%.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 60.9 versus 41.6. Inside this category, SWE-Rebench is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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