Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
35
Step 3.7 Flash
72
Pick Step 3.7 Flash if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+17.1 difference
DeepSeek V3
Step 3.7 Flash
$0.27 / $1.1
$0.2 / $1.15
N/A
N/A
N/A
N/A
128K
256K
Pick Step 3.7 Flash if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Step 3.7 Flash is clearly ahead on the provisional aggregate, 72 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Step 3.7 Flash's sharpest advantage is in coding, where it averages 56.3 against 39.2.
Step 3.7 Flash is also the more expensive model on tokens at $0.20 input / $1.15 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. Step 3.7 Flash is the reasoning model in the pair, while DeepSeek V3 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.
Step 3.7 Flash is ahead on BenchLM's provisional leaderboard, 72 to 35.
Step 3.7 Flash has the edge for coding in this comparison, averaging 56.3 versus 39.2. DeepSeek V3 stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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