Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
58
GPT-4.1 mini
46
Pick DeepSeek V3.2 if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you need the larger 1M context window.
Coding
+37.3 difference
DeepSeek V3.2
GPT-4.1 mini
$0.28 / $0.42
$0.4 / $1.6
35 t/s
80 t/s
3.75s
0.76s
128K
1M
Pick DeepSeek V3.2 if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you need the larger 1M context window.
DeepSeek V3.2 is clearly ahead on the provisional aggregate, 58 to 46. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2's sharpest advantage is in coding, where it averages 60.9 against 23.6.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 3.8x on output cost alone. GPT-4.1 mini gives you the larger context window at 1M, compared with 128K for DeepSeek V3.2.
DeepSeek V3.2 is ahead on BenchLM's provisional leaderboard, 58 to 46.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 60.9 versus 23.6. GPT-4.1 mini stays close enough that the answer can still flip depending on your workload.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.