Model comparison
GPT-4.1 vs Step 3.7 Flash
Step 3.7 Flash is the stronger model overall, scoring 37.3 to 35.9 on the Noometry Index.
Last verified . 1 shared benchmarks.
StepFunSummary
- They share 1 benchmark with published results for both. GPT-4.1 scores higher in 0 categories and Step 3.7 Flash in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where Step 3.7 Flash leads 42.9 to 22.3.
- Step 3.7 Flash is cheaper at $0.18 / $1.11 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 accepts more context: 1.05M tokens versus 256K.
- Step 3.7 Flash has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 | Step 3.7 Flash | |
|---|---|---|
| Provider | OpenAI | StepFun |
| Noometry Index | 35.9 | 37.3 |
| Released | 2025-04-14 | 2026-05-29 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 256K |
| Max output | 33K | 256K |
| Input $ / M tokens | $2 | $0.18 |
| Output $ / M tokens | $8 | $1.11 |
| Results tracked | 52 | 5 |
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Category by category
Coding Step 3.7 Flash leads
GPT-4.1: 34.4 (#238), Step 3.7 Flash: 40.0 (#150)
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| ALE-Bench | 558.1 | 694.12 |
| SWE-bench Verified | 48.5% | — |
| SWE-bench Verified (bash only) | 39.6% | — |
| Aider Polyglot | 52.4% | — |
| SciCode | — | 40% |
| WeirdML | 39% | — |
| LMArena Coding | 1391 | — |
| CadEval | 42% | — |
Agentic & Tool Use Not comparable
GPT-4.1: 34.7 (#43), Step 3.7 Flash: —
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| Berkeley Function Calling Leaderboard | 54% | — |
Reasoning Step 3.7 Flash leads
GPT-4.1: 11.7 (#339), Step 3.7 Flash: 21.6 (#219)
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| ARC-AGI-2 | 0.4% | — |
| SimpleBench | 27% | — |
| Kagi LLM Benchmark | 52.3% | — |
| NYT Connections (extended) | — | 39.7% |
| ARC-AGI-1 | 5.5% | — |
| CritPt | — | 2.3% |
| Chess Puzzles | 6% | — |
| EnigmaEval | 2.2% | — |
| LMArena Hard Prompts | 1384 | — |
| DTBench | 68.3% | — |
| LMCA | 25.6% | — |
| Epoch Capabilities Index | 136.78 | — |
| ForecastBench | 61.5 | — |
Math Step 3.7 Flash leads
GPT-4.1: 22.3 (#280), Step 3.7 Flash: 42.9 (#82)
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 6% | — |
| MathArena Final-Answer Competitions | — | 68.5% |
| OTIS Mock AIME 2024-2025 | 38.3% | — |
| Omni-MATH | 47.1% | — |
| LMArena Math | 1370 | — |
| MATH Level 5 | 83% | — |
| FrontierMath (Feb 2025 set) | 5.5% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Not comparable
GPT-4.1: 37.1 (#160), Step 3.7 Flash: —
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| GPQA Diamond | 66.9% | — |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | 31.1% | — |
| MMLU-Pro | 81.1% | — |
| Vectara Hallucination Rate | 5.6% | — |
| GPQA (HELM) | 65.9% | — |
| LMArena Expert | 1364 | — |
Multimodal Not comparable
GPT-4.1: 38.2 (#67), Step 3.7 Flash: —
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| LMArena Vision | 1211 | — |
| GeoBench | 72% | — |
Multilingual Not comparable
GPT-4.1: 49.4 (#133), Step 3.7 Flash: —
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| LMArena Non-English | 1370 | — |
| LMArena Chinese | 1382 | — |
| LMArena French | 1382 | — |
| LMArena German | 1381 | — |
| LMArena Japanese | 1319 | — |
| LMArena Korean | 1339 | — |
| LMArena Russian | 1377 | — |
| LMArena Spanish | 1376 | — |
Instruction Following Not comparable
GPT-4.1: 71.3 (#153), Step 3.7 Flash: —
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| IFEval | 83.8% | — |
| LMArena Instruction Following | 1367 | — |
Long Context Not comparable
GPT-4.1: 40.0 (#163), Step 3.7 Flash: —
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| Fiction.LiveBench | 63.9% | — |
| LMArena Longer Query | 1385 | — |
Writing & Preference Not comparable
GPT-4.1: 57.6 (#125), Step 3.7 Flash: —
| Benchmark | GPT-4.1 | Step 3.7 Flash |
|---|---|---|
| LMArena Text | 1383 | — |
| LMArena Creative Writing | 1363 | — |
| EQ-Bench Creative Writing | 1420 | — |
| WildBench | 85.4% | — |
| LMArena Multi-Turn | 1398 | — |
Frequently asked questions
Is GPT-4.1 better than Step 3.7 Flash?
Step 3.7 Flash is the stronger model overall, scoring 37.3 to 35.9 on the Noometry Index.
Which is cheaper, GPT-4.1 or Step 3.7 Flash?
Step 3.7 Flash is cheaper. It lists at $0.18 per million input tokens and $1.11 per million output tokens; GPT-4.1 lists at $2 and $8.
Is GPT-4.1 or Step 3.7 Flash better for coding?
Step 3.7 Flash scores higher on coding benchmarks: 40.0 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 256K.
How many benchmarks do GPT-4.1 and Step 3.7 Flash share?
1 benchmark has published results for both models. GPT-4.1 has 52 scored results on Noometry and Step 3.7 Flash has 5.