Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
36
Gemini 2.5 Pro
65
Pick Gemini 2.5 Pro if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Coding
+24.6 difference
Knowledge
+29.2 difference
DeepSeek V3
Gemini 2.5 Pro
$0.27 / $1.1
$1.25 / $10
N/A
117 t/s
N/A
21.19s
128K
1M
Pick Gemini 2.5 Pro if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Gemini 2.5 Pro is clearly ahead on the provisional aggregate, 65 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 2.5 Pro's sharpest advantage is in coding, where it averages 63.8 against 39.2. The single biggest benchmark swing on the page is GPQA, 59.1% to 83%. DeepSeek V3 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $10.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. That is roughly 9.1x on output cost alone. Gemini 2.5 Pro gives you the larger context window at 1M, compared with 128K for DeepSeek V3.
Gemini 2.5 Pro is ahead on BenchLM's provisional leaderboard, 65 to 36. The biggest single separator in this matchup is GPQA, where the scores are 59.1% and 83%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 70 versus 40.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemini 2.5 Pro has the edge for coding in this comparison, averaging 63.8 versus 39.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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