Model comparison
GPT-4.1 nano vs GPT-5
GPT-5 is the stronger model overall, scoring 50.9 to 27.7 on the Noometry Index. GPT-4.1 nano costs 20× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Last verified . 37 shared benchmarks.
Summary
- They share 37 benchmarks with published results for both. GPT-4.1 nano scores higher in 0 categories and GPT-5 in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 23.7.
- The biggest single-benchmark swing is Aider Polyglot: 8.9% for GPT-4.1 nano and 88% for GPT-5.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 400K.
Side by side
| GPT-4.1 nano | GPT-5 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 27.7 | 50.9 |
| Released | 2025-04-14 | 2025-08-07 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 400K |
| Max output | 33K | 128K |
| Input $ / M tokens | $0.10 | $1.25 |
| Output $ / M tokens | $0.40 | $10 |
| Results tracked | 38 | 69 |
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Category by category
Coding GPT-5 leads
GPT-4.1 nano: 23.8 (#325), GPT-5: 50.7 (#39)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| Aider Polyglot | 8.9% | 88% |
| SciCode | 25.9% | 42.9% |
| WeirdML | 19% | 60.7% |
| LMArena Coding | 1306 | 1436 |
| SWE-bench Verified | — | 73.6% |
| SWE-bench Verified (bash only) | — | 65% |
| LMArena WebDev | — | 1417 |
| GSO | — | 6.9% |
| ALE-Bench | — | 1,162 |
| AlgoTune | — | 1.67 |
Agentic & Tool Use GPT-5 leads
GPT-4.1 nano: 26.3 (#98), GPT-5: 33.0 (#53)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| Terminal-Bench | — | 49.6% |
| Berkeley Function Calling Leaderboard | 33% | — |
| GDPval | — | 34.8% |
| Remote Labor Index | — | 1.7% |
| DeepResearch Bench | — | 49.6% |
| BALROG | — | 32.8% |
| LMArena Search | — | 1133 |
| METR Time Horizons | — | 69.6% |
Reasoning GPT-5 leads
GPT-4.1 nano: 8.6 (#344), GPT-5: 38.3 (#55)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| ARC-AGI-2 | 0% | 9.9% |
| Kagi LLM Benchmark | 33.3% | 72.7% |
| ARC-AGI-1 | 0% | 65.7% |
| CritPt | 0% | 12.6% |
| LMArena Hard Prompts | 1286 | 1415 |
| DTBench | 52.5% | 90.7% |
| LMCA | 5.5% | 40% |
| Epoch Capabilities Index | 129.62 | 150 |
| SimpleBench | — | 56.7% |
| Chess Puzzles | — | 37% |
| EnigmaEval | — | 10.5% |
| EBR-Bench | — | 12.7% |
| Mystery Game Puzzles | — | 23% |
| ForecastBench | — | 61.4 |
Math GPT-5 leads
GPT-4.1 nano: 25.5 (#246), GPT-5: 54.6 (#36)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 28.9% | 91.4% |
| Omni-MATH | 36.7% | 64.7% |
| LMArena Math | 1274 | 1406 |
| MATH Level 5 | 70% | 98.1% |
| FrontierMath (Feb 2025 set) | 1% | 32.4% |
| FrontierMath (Tiers 1-3) | — | 55.4% |
| FrontierMath Tier 4 | — | 22% |
| ProofBench | — | 18% |
| FrontierMath Tier 4 (v1) | — | 12.5% |
Knowledge GPT-5 leads
GPT-4.1 nano: 21.8 (#268), GPT-5: 56.5 (#37)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| GPQA Diamond | 48.9% | 86.2% |
| SimpleQA Verified | 6% | 50.1% |
| MMLU-Pro | 55% | 86.3% |
| GPQA (HELM) | 50.7% | 79.2% |
| LMArena Expert | 1272 | 1420 |
| Humanity's Last Exam | — | 25.3% |
| Confabulations | — | 10.3% |
| Vectara Hallucination Rate | — | 14.7% |
Multimodal GPT-5 leads
GPT-4.1 nano: 28.6 (#108), GPT-5: 46.1 (#12)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| LMArena Vision | 1063 | 1232 |
| GeoBench | — | 81% |
| VPCT | — | 66% |
Multilingual GPT-5 leads
GPT-4.1 nano: 41.8 (#200), GPT-5: 51.5 (#105)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| LMArena Non-English | 1260 | 1397 |
| LMArena Chinese | 1270 | 1423 |
| LMArena German | 1288 | 1415 |
| LMArena Japanese | 1199 | 1411 |
| LMArena Russian | 1261 | 1406 |
| LMArena French | — | 1409 |
| LMArena Korean | — | 1361 |
| LMArena Spanish | — | 1398 |
Instruction Following GPT-5 leads
GPT-4.1 nano: 68.0 (#188), GPT-5: 74.0 (#107)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| IFEval | 84.3% | 87.5% |
| LMArena Instruction Following | 1267 | 1388 |
Long Context GPT-5 leads
GPT-4.1 nano: 23.7 (#291), GPT-5: 69.5 (#2)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| Fiction.LiveBench | 25% | 97.2% |
| LMArena Longer Query | 1283 | 1399 |
Writing & Preference GPT-5 leads
GPT-4.1 nano: 40.7 (#238), GPT-5: 63.5 (#63)
| Benchmark | GPT-4.1 nano | GPT-5 |
|---|---|---|
| LMArena Text | 1285 | 1406 |
| LMArena Creative Writing | 1260 | 1365 |
| EQ-Bench Creative Writing | 946 | 1627 |
| WildBench | 81.2% | 85.7% |
| LMArena Multi-Turn | 1277 | 1426 |
| Short-Story Creative Writing | — | 86% |
Frequently asked questions
Is GPT-4.1 nano better than GPT-5?
GPT-5 is the stronger model overall, scoring 50.9 to 27.7 on the Noometry Index. GPT-4.1 nano costs 20× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 nano or GPT-5?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GPT-5 lists at $1.25 and $10.
Is GPT-4.1 nano or GPT-5 better for coding?
GPT-5 scores higher on coding benchmarks: 50.7 versus 23.8 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 400K.
How many benchmarks do GPT-4.1 nano and GPT-5 share?
37 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and GPT-5 has 69.