Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
58
DeepSeek V4 Flash (Max)
77
Verified leaderboard positions: DeepSeek V3.2 unranked · DeepSeek V4 Flash (Max) #12
Pick DeepSeek V4 Flash (Max) if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+12.8 difference
DeepSeek V3.2
DeepSeek V4 Flash (Max)
$0.28 / $0.42
$0.14 / $0.28
35 t/s
N/A
3.75s
N/A
128K
1M
Pick DeepSeek V4 Flash (Max) if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek V4 Flash (Max) is clearly ahead on the provisional aggregate, 77 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V4 Flash (Max)'s sharpest advantage is in coding, where it averages 73.7 against 60.9.
DeepSeek V3.2 is also the more expensive model on tokens at $0.28 input / $0.42 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (Max). DeepSeek V4 Flash (Max) is the reasoning model in the pair, while DeepSeek V3.2 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. DeepSeek V4 Flash (Max) gives you the larger context window at 1M, compared with 128K for DeepSeek V3.2.
DeepSeek V4 Flash (Max) is ahead on BenchLM's provisional leaderboard, 77 to 58.
DeepSeek V4 Flash (Max) has the edge for coding in this comparison, averaging 73.7 versus 60.9. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
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