Model comparison
GPT-4o vs Step 3.5 Flash
Step 3.5 Flash is the stronger model overall, scoring 42.3 to 28.6 on the Noometry Index.
Last verified . 17 shared benchmarks.
StepFunSummary
- They share 17 benchmarks with published results for both. GPT-4o scores higher in 0 categories and Step 3.5 Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Step 3.5 Flash leads 42.6 to 10.6.
- Step 3.5 Flash is cheaper at $0.10 / $0.30 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- Step 3.5 Flash accepts more context: 256K tokens versus 128K.
- Step 3.5 Flash has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Step 3.5 Flash | |
|---|---|---|
| Provider | OpenAI | StepFun |
| Noometry Index | 28.6 | 42.3 |
| Released | 2024-05-13 | 2026-01-29 |
| Weights | Proprietary | Open |
| Context window | 128K | 256K |
| Max output | 16K | 256K |
| Input $ / M tokens | $2.50 | $0.10 |
| Output $ / M tokens | $10 | $0.30 |
| Results tracked | 72 | 19 |
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Category by category
Coding Step 3.5 Flash leads
GPT-4o: 24.8 (#328), Step 3.5 Flash: 42.4 (#105)
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Coding | 1297 | 1436 |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o: 21.0 (#141), Step 3.5 Flash: —
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning Step 3.5 Flash leads
GPT-4o: 9.4 (#343), Step 3.5 Flash: 22.2 (#202)
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Hard Prompts | 1281 | 1411 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| NYT Connections (extended) | — | 28.4% |
| ARC-AGI-1 | 4.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | 13% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| DTBench | 64.5% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 16.6% | — |
| Epoch Capabilities Index | 128.97 | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math Step 3.5 Flash leads
GPT-4o: 10.6 (#312), Step 3.5 Flash: 42.6 (#84)
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Math | 1285 | 1408 |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| MathArena Final-Answer Competitions | — | 66.8% |
| OTIS Mock AIME 2024-2025 | 6.4% | — |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Step 3.5 Flash leads
GPT-4o: 28.8 (#242), Step 3.5 Flash: 39.6 (#132)
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Expert | 1250 | 1421 |
| GPQA Diamond | 49.2% | — |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| Confabulations | 15.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| GPQA (HELM) | 52% | — |
| MMLU | 88.1% | — |
Multimodal Not comparable
GPT-4o: 34.5 (#91), Step 3.5 Flash: —
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual Step 3.5 Flash leads
GPT-4o: 43.2 (#186), Step 3.5 Flash: 50.5 (#119)
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Non-English | 1283 | 1385 |
| LMArena Chinese | 1277 | 1447 |
| LMArena French | 1304 | 1421 |
| LMArena German | 1282 | 1405 |
| LMArena Japanese | 1257 | 1354 |
| LMArena Korean | 1234 | 1352 |
| LMArena Russian | 1286 | 1385 |
| LMArena Spanish | 1292 | 1419 |
Instruction Following Step 3.5 Flash leads
GPT-4o: 66.6 (#207), Step 3.5 Flash: 73.1 (#124)
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Instruction Following | 1278 | 1385 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
Long Context Step 3.5 Flash leads
GPT-4o: 39.4 (#179), Step 3.5 Flash: 42.8 (#117)
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Longer Query | 1289 | 1402 |
| Fiction.LiveBench | 66.7% | — |
Writing & Preference Step 3.5 Flash leads
GPT-4o: 52.6 (#166), Step 3.5 Flash: 58.8 (#113)
| Benchmark | GPT-4o | Step 3.5 Flash |
|---|---|---|
| LMArena Text | 1300 | 1403 |
| LMArena Creative Writing | 1292 | 1357 |
| LMArena Multi-Turn | 1302 | 1405 |
| Short-Story Creative Writing | 81.8% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Step 3.5 Flash?
Step 3.5 Flash is the stronger model overall, scoring 42.3 to 28.6 on the Noometry Index.
Which is cheaper, GPT-4o or Step 3.5 Flash?
Step 3.5 Flash is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; GPT-4o lists at $2.50 and $10.
Is GPT-4o or Step 3.5 Flash better for coding?
Step 3.5 Flash scores higher on coding benchmarks: 42.4 versus 24.8 in the Noometry coding category.
Which has the bigger context window?
Step 3.5 Flash does, with 256K tokens against 128K.
How many benchmarks do GPT-4o and Step 3.5 Flash share?
17 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Step 3.5 Flash has 19.