Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
58
GPT-4.1
58
Treat this as a split decision. DeepSeek V3.2 makes more sense if coding is the priority or you want the cheaper token bill; GPT-4.1 is the better fit if you need the larger 1M context window.
Coding
+6.3 difference
DeepSeek V3.2
GPT-4.1
$0.28 / $0.42
$2 / $8
35 t/s
108 t/s
3.75s
1.02s
128K
1M
Treat this as a split decision. DeepSeek V3.2 makes more sense if coding is the priority or you want the cheaper token bill; GPT-4.1 is the better fit if you need the larger 1M context window.
DeepSeek V3.2 and GPT-4.1 finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 19.0x on output cost alone. GPT-4.1 gives you the larger context window at 1M, compared with 128K for DeepSeek V3.2.
DeepSeek V3.2 and GPT-4.1 are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 60.9 versus 54.6. GPT-4.1 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.