Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
36
GPT-4.1 nano
27
Pick DeepSeek V3 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Knowledge
+19.7 difference
Inst. Following
+2.9 difference
DeepSeek V3
GPT-4.1 nano
$0.27 / $1.1
$0.1 / $0.4
N/A
181 t/s
N/A
0.63s
128K
1M
Pick DeepSeek V3 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
DeepSeek V3 is clearly ahead on the provisional aggregate, 36 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3's sharpest advantage is in knowledge, where it averages 70 against 50.3. The single biggest benchmark swing on the page is GPQA, 59.1% to 50.3%.
DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 2.8x on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 128K for DeepSeek V3.
DeepSeek V3 is ahead on BenchLM's provisional leaderboard, 36 to 27. The biggest single separator in this matchup is GPQA, where the scores are 59.1% and 50.3%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 70 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek V3 has the edge for instruction following in this comparison, averaging 86.1 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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