Model comparison
GPT-4.1 vs Step 3.7 Flash
Head-to-head evidence from 15 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
BenchAlign evidence: GPT-4.1 supported; Step 3.7 Flash estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4.1 and Step 3.7 Flash share 15 comparable benchmark results. 1 of 8 categories are comparable. 6 results are unique to GPT-4.1; 15 to Step 3.7 Flash.
Updated July 16, 2026- Shared results
- 15
- GPT-4.1 only
- 6
- Step 3.7 Flash only
- 15
- Comparable categories
- 1 / 8
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.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Step 3.7 Flash finishes one point ahead on BenchLM's provisional leaderboard, 57 to 56. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
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.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GPT-4.1 | Δ | Step 3.7 Flash |
|---|---|---|---|
| Coding | GPT-4.154.6 | Margin→ 1.7 | Step 3.7 Flash56.3 |
| Agentic | GPT-4.1Not measured | MarginNo overlap | Step 3.7 Flash66.4 |
| Knowledge | GPT-4.166.3 | MarginNo overlap | Step 3.7 FlashNot measured |
| Math | GPT-4.14.1 | MarginNo overlap | Step 3.7 FlashNot measured |
| Inst. Following | GPT-4.187.4 | MarginNo overlap | Step 3.7 FlashNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4.1 | Step 3.7 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1$2 input / $8 output | Step 3.7 Flash$0.2 input / $1.15 output | Step 3.7 Flash has the lower combined listed price. |
| Generation speedtokens per second | GPT-4.1108 tok/s | Step 3.7 FlashNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4.11.02 s | Step 3.7 FlashNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4.11M | Step 3.7 Flash256K | GPT-4.1 lists the larger context window. |
Benchmark Deep Dive
Agentic12 benchmarks
| Benchmark | GPT-4.1 | Step 3.7 Flash | Result |
|---|---|---|---|
| τ²-bench resultsSource | 47.1% | 98.5% | Step 3.7 Flash leads |
| Gert LabsSource | 25.65% | 51.57% | Step 3.7 Flash leads |
| Terminal-Bench 2.0Source | — | 59.5% | Not comparable |
| BrowseCompSource | — | 75.8% | Not comparable |
| DeepSearchQASource | — | 92.8% | Not comparable |
| GDPval-AASource | — | 25.9% | Not comparable |
| ToolathlonSource | — | 49.5% | Not comparable |
| Claw-EvalSource | — | 67.1% | Not comparable |
| HLE w/ toolsSource | — | 47.2% | Not comparable |
| AA Agentic IndexSource | — | 21.5% | Not comparable |
| GDPval-AASource | — | 1017 | Not comparable |
| APEX-Agents-AASource | — | 14.8% | Not comparable |
CodingStep 3.7 Flash wins6 benchmarks
| Benchmark | GPT-4.1 | Step 3.7 Flash | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 54.6% | — | Not comparable |
| Terminal-Bench HardSource | 13.6% | 35.6% | Step 3.7 Flash leads |
| AA-SciCodeSource | 38.1% | 40.0% | Step 3.7 Flash leads |
| SWE-bench ProSource | — | 56.3% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.5% | Not comparable |
| AA Coding IndexSource | — | 39.6% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GPT-4.1 | Step 3.7 Flash | Result |
|---|---|---|---|
| MMLUSource | 90.2% | — | Not comparable |
| GPQASource | 66.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 19.4% | 30.3% | Step 3.7 Flash leads |
| AA-GPQA DiamondSource | 66.6% | 80.9% | Step 3.7 Flash leads |
| AA-HLESource | 4.6% | 19.9% | Step 3.7 Flash leads |
| AA-Omniscience IndexSource | -36.2% | -37.5% | GPT-4.1 leads |
| AA-Omniscience AccuracySource | 24.2% | 25.4% | Step 3.7 Flash leads |
| AA-Omniscience Hallucination RateSource | 79.6% | 84.4% | GPT-4.1 leads |
Math2 benchmarks
Multimodal4 benchmarks
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, 57 to 56.
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. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
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