Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 3.5 Flash
88
Gemma 4 31B
65
Verified leaderboard positions: Gemini 3.5 Flash #7 · Gemma 4 31B unranked
Pick Gemini 3.5 Flash if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Coding
+12.9 difference
Knowledge
+3.3 difference
Multimodal
+6.9 difference
Gemini 3.5 Flash
Gemma 4 31B
$1.5 / $9
$0 / $0
284.2 t/s
N/A
18.55s
N/A
1M
256K
Pick Gemini 3.5 Flash if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Gemini 3.5 Flash is clearly ahead on the provisional aggregate, 88 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.5 Flash's sharpest advantage is in coding, where it averages 54.5 against 41.6. The single biggest benchmark swing on the page is HLE, 40.2% to 26.5%. Gemma 4 31B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Gemini 3.5 Flash is also the more expensive model on tokens at $1.50 input / $9.00 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. Gemini 3.5 Flash gives you the larger context window at 1M, compared with 256K for Gemma 4 31B.
Gemini 3.5 Flash is ahead on BenchLM's provisional leaderboard, 88 to 65. The biggest single separator in this matchup is HLE, where the scores are 40.2% and 26.5%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 58. Inside this category, HLE is the benchmark that creates the most daylight between them.
Gemini 3.5 Flash has the edge for coding in this comparison, averaging 54.5 versus 41.6. Gemma 4 31B stays close enough that the answer can still flip depending on your workload.
Gemini 3.5 Flash has the edge for multimodal and grounded tasks in this comparison, averaging 83.8 versus 76.9. Inside this category, MMMU-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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