Model comparison
GPT-5 Mini vs Step 3.7 Flash
GPT-5 Mini is the stronger model overall, scoring 41.7 to 36.5 on the Noometry Index. Step 3.7 Flash costs 1.7× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
StepFunSummary
- They share 3 benchmarks with published results for both. GPT-5 Mini scores higher in 3 categories and Step 3.7 Flash in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Mini leads 46.3 to 40.9.
- Step 3.7 Flash is cheaper at $0.18 / $1.11 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- GPT-5 Mini accepts more context: 400K tokens versus 256K.
- Step 3.7 Flash has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Mini | Step 3.7 Flash | |
|---|---|---|
| Provider | OpenAI | StepFun |
| Noometry Index | 41.7 | 36.5 |
| Released | 2025-08-07 | 2026-05-29 |
| Weights | Proprietary | Open |
| Context window | 400K | 256K |
| Max output | 128K | 256K |
| Input $ / M tokens | $0.25 | $0.18 |
| Output $ / M tokens | $2 | $1.11 |
| Results tracked | 60 | 5 |
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Category by category
Coding Too close to call
GPT-5 Mini: 40.0 (#134), Step 3.7 Flash: 39.4 (#147)
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| SciCode | 39.2% | 40% |
| ALE-Bench | 799.77 | 694.12 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| SWE-bench Multilingual | 39.7% | — |
| WeirdML | 52.7% | — |
| LMArena Coding | 1406 | — |
| AlgoTune | 1.38 | — |
Agentic & Tool Use Not comparable
GPT-5 Mini: 31.0 (#64), Step 3.7 Flash: —
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| Terminal-Bench | 34.8% | — |
| Berkeley Function Calling Leaderboard | 55.5% | — |
| Vending-Bench 2 | -31.18 | — |
Reasoning GPT-5 Mini leads
GPT-5 Mini: 24.1 (#153), Step 3.7 Flash: 20.9 (#211)
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| CritPt | 0% | 2.3% |
| ARC-AGI-2 | 4.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| NYT Connections (extended) | — | 39.7% |
| ARC-AGI-1 | 54.3% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | 8.2% | — |
| LMArena Hard Prompts | 1380 | — |
| Mystery Game Puzzles | 10% | — |
| DTBench | 80.5% | — |
| LMCA | 34.2% | — |
| Epoch Capabilities Index | 145.51 | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.3 (#59), Step 3.7 Flash: 40.9 (#74)
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| MathArena Final-Answer Competitions | — | 68.5% |
| OTIS Mock AIME 2024-2025 | 86.7% | — |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| LMArena Math | 1377 | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge Not comparable
GPT-5 Mini: 45.5 (#80), Step 3.7 Flash: —
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| GPQA Diamond | 75% | — |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| Vectara Hallucination Rate | 12.9% | — |
| GPQA (HELM) | 75.6% | — |
| LMArena Expert | 1377 | — |
Multimodal Not comparable
GPT-5 Mini: 35.0 (#80), Step 3.7 Flash: —
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual Not comparable
GPT-5 Mini: 49.1 (#131), Step 3.7 Flash: —
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| LMArena Non-English | 1363 | — |
| LMArena Chinese | 1387 | — |
| LMArena French | 1385 | — |
| LMArena German | 1366 | — |
| LMArena Japanese | 1342 | — |
| LMArena Korean | 1306 | — |
| LMArena Russian | 1362 | — |
| LMArena Spanish | 1355 | — |
Instruction Following Not comparable
GPT-5 Mini: 76.4 (#43), Step 3.7 Flash: —
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| IFEval | 92.7% | — |
| LMArena Instruction Following | 1357 | — |
Long Context Not comparable
GPT-5 Mini: 41.9 (#127), Step 3.7 Flash: —
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| Fiction.LiveBench | 69.4% | — |
| LMArena Longer Query | 1355 | — |
Writing & Preference Not comparable
GPT-5 Mini: 55.4 (#143), Step 3.7 Flash: —
| Benchmark | GPT-5 Mini | Step 3.7 Flash |
|---|---|---|
| LMArena Text | 1373 | — |
| LMArena Creative Writing | 1325 | — |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
| LMArena Multi-Turn | 1363 | — |
Frequently asked questions
Is GPT-5 Mini better than Step 3.7 Flash?
GPT-5 Mini is the stronger model overall, scoring 41.7 to 36.5 on the Noometry Index. Step 3.7 Flash costs 1.7× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Mini 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-5 Mini lists at $0.25 and $2.
Is GPT-5 Mini or Step 3.7 Flash better for coding?
They score almost the same on coding (40.0 vs 39.4); test both on your own repository before choosing.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 256K.
How many benchmarks do GPT-5 Mini and Step 3.7 Flash share?
3 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Step 3.7 Flash has 5.