Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
57
Step 3.7 Flash
72
Pick Step 3.7 Flash if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Coding
+4.6 difference
DeepSeek V3.2
Step 3.7 Flash
$0.28 / $0.42
$0.2 / $1.15
35 t/s
N/A
3.75s
N/A
128K
256K
Pick Step 3.7 Flash if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Step 3.7 Flash is clearly ahead on the provisional aggregate, 72 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Step 3.7 Flash is also the more expensive model on tokens at $0.20 input / $1.15 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 2.7x on output cost alone. Step 3.7 Flash 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. Step 3.7 Flash gives you the larger context window at 256K, compared with 128K for DeepSeek V3.2.
Step 3.7 Flash is ahead on BenchLM's provisional leaderboard, 72 to 57.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 60.9 versus 56.3. Step 3.7 Flash stays close enough that the answer can still flip depending on your workload.
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