Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 mini
46
Step 3.7 Flash
72
Pick Step 3.7 Flash if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+32.7 difference
GPT-4.1 mini
Step 3.7 Flash
$0.4 / $1.6
$0.2 / $1.15
80 t/s
N/A
0.76s
N/A
1M
256K
Pick Step 3.7 Flash if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you need the larger 1M context window 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 46. 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 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 $0.20 input / $1.15 output per 1M tokens for Step 3.7 Flash. Step 3.7 Flash is the reasoning model in the pair, while GPT-4.1 mini 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. GPT-4.1 mini gives you the larger context window at 1M, compared with 256K for Step 3.7 Flash.
Step 3.7 Flash is ahead on BenchLM's provisional leaderboard, 72 to 46.
Step 3.7 Flash has the edge for coding in this comparison, averaging 56.3 versus 23.6. GPT-4.1 mini stays close enough that the answer can still flip depending on your workload.
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