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GPT-4.1 vs Step 3.7 Flash

Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.

GPT-4.1

57

VS

Step 3.7 Flash

72

0 categoriesvs1 categories

Pick Step 3.7 Flash if you want the stronger benchmark profile. GPT-4.1 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.

Category Radar

Head-to-Head by Category

Category Breakdown

Coding

Step 3.7 Flash
54.6vs56.3

+1.7 difference

Operational Comparison

GPT-4.1

Step 3.7 Flash

Price (per 1M tokens)

$2 / $8

$0.2 / $1.15

Speed

108 t/s

N/A

Latency (TTFT)

1.02s

N/A

Context Window

1M

256K

Quick Verdict

Pick Step 3.7 Flash if you want the stronger benchmark profile. GPT-4.1 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 57. 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 54.6.

GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.20 input / $1.15 output per 1M tokens for Step 3.7 Flash. That is roughly 7.0x on output cost alone. Step 3.7 Flash is the reasoning model in the pair, while GPT-4.1 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 gives you the larger context window at 1M, compared with 256K for Step 3.7 Flash.

Benchmark Deep Dive

Frequently Asked Questions (2)

Which is better, GPT-4.1 or Step 3.7 Flash?

Step 3.7 Flash is ahead on BenchLM's provisional leaderboard, 72 to 57.

Which is better for coding, GPT-4.1 or Step 3.7 Flash?

Step 3.7 Flash has the edge for coding in this comparison, averaging 56.3 versus 54.6. GPT-4.1 stays close enough that the answer can still flip depending on your workload.

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Last updated: June 1, 2026

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