Model comparison
GPT-4.1 nano vs GPT-6 Sol
GPT-6 Sol is the stronger model overall, scoring 61.8 to 27.9 on the Noometry Index. GPT-4.1 nano costs 23× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Summary
- They share 26 benchmarks with published results for both. GPT-4.1 nano scores higher in 0 categories and GPT-6 Sol in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 74.0 to 8.5.
- The biggest single-benchmark swing is ARC-AGI-1: 0% for GPT-4.1 nano and 95.5% for GPT-6 Sol.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 1.05M.
Side by side
| GPT-4.1 nano | GPT-6 Sol | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 27.9 | 61.8 |
| Released | 2025-04-14 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 33K | 128K |
| Input $ / M tokens | $0.10 | $2 |
| Output $ / M tokens | $0.40 | $10 |
| Results tracked | 38 | 45 |
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Category by category
Coding GPT-6 Sol leads
GPT-4.1 nano: 24.1 (#330), GPT-6 Sol: 60.1 (#11)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| SciCode | 25.9% | 57.6% |
| LMArena Coding | 1306 | 1447 |
| DeepSWE | — | 68.8% |
| FrontierCode | — | 49.3% |
| Aider Polyglot | 8.9% | — |
| LMArena WebDev | — | 1688 |
| WeirdML | 19% | — |
| ALE-Bench | — | 2,462 |
Agentic & Tool Use GPT-6 Sol leads
GPT-4.1 nano: 26.5 (#104), GPT-6 Sol: 37.2 (#36)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| APEX-Agents | — | 54.3% |
| Berkeley Function Calling Leaderboard | 33% | — |
| GDP.pdf | — | 26.4% |
| Vending-Bench 2 | — | 14,428 |
Reasoning GPT-6 Sol leads
GPT-4.1 nano: 8.5 (#349), GPT-6 Sol: 74.0 (#9)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| ARC-AGI-2 | 0% | 89.6% |
| ARC-AGI-1 | 0% | 95.5% |
| CritPt | 0% | 30.9% |
| LMArena Hard Prompts | 1286 | 1418 |
| DTBench | 52.5% | 97.3% |
| LMCA | 5.5% | 59.1% |
| Epoch Capabilities Index | 129.62 | 162.72 |
| Kagi LLM Benchmark | 33.3% | — |
| NYT Connections (extended) | — | 90.1% |
| EBR-Bench | — | 53.3% |
| Mystery Game Puzzles | — | 56% |
Math GPT-6 Sol leads
GPT-4.1 nano: 26.9 (#252), GPT-6 Sol: 87.2 (#7)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 28.9% | 100% |
| LMArena Math | 1274 | 1402 |
| FrontierMath (Tiers 1-3) | — | 89.8% |
| FrontierMath Tier 4 | — | 90% |
| ProofBench | — | 83% |
| Omni-MATH | 36.7% | — |
| MATH Level 5 | 70% | — |
| FrontierMath (Feb 2025 set) | 1% | — |
Knowledge GPT-6 Sol leads
GPT-4.1 nano: 21.8 (#273), GPT-6 Sol: 64.8 (#15)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| GPQA Diamond | 48.9% | 94.3% |
| SimpleQA Verified | 6% | 60.7% |
| LMArena Expert | 1272 | 1439 |
| MMLU-Pro | 55% | — |
| Vectara Hallucination Rate | — | 6.5% |
| GPQA (HELM) | 50.7% | — |
Multimodal GPT-6 Sol leads
GPT-4.1 nano: 29.2 (#113), GPT-6 Sol: 47.6 (#10)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| LMArena Vision | 1063 | 1245 |
| Blueprint-Bench 2 | — | 36.9% |
| Furniture Assembly | — | 58.3% |
Multilingual GPT-6 Sol leads
GPT-4.1 nano: 41.6 (#205), GPT-6 Sol: 50.5 (#118)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| LMArena Non-English | 1260 | 1385 |
| LMArena Chinese | 1270 | 1405 |
| LMArena German | 1288 | 1390 |
| LMArena Japanese | 1198 | 1385 |
| LMArena Russian | 1261 | 1401 |
| LMArena French | — | 1410 |
| LMArena Korean | — | 1341 |
| LMArena Spanish | — | 1384 |
Instruction Following GPT-6 Sol leads
GPT-4.1 nano: 67.8 (#193), GPT-6 Sol: 74.5 (#94)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| LMArena Instruction Following | 1267 | 1412 |
| IFEval | 84.3% | — |
Long Context GPT-6 Sol leads
GPT-4.1 nano: 23.7 (#296), GPT-6 Sol: 43.1 (#108)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| LMArena Longer Query | 1283 | 1411 |
| Fiction.LiveBench | 25% | — |
Writing & Preference GPT-6 Sol leads
GPT-4.1 nano: 40.5 (#243), GPT-6 Sol: 71.9 (#18)
| Benchmark | GPT-4.1 nano | GPT-6 Sol |
|---|---|---|
| LMArena Text | 1285 | 1395 |
| LMArena Creative Writing | 1260 | 1378 |
| EQ-Bench Creative Writing | 946 | 2125 |
| LMArena Multi-Turn | 1277 | 1412 |
| WildBench | 81.2% | — |
Frequently asked questions
Is GPT-4.1 nano better than GPT-6 Sol?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 27.9 on the Noometry Index. GPT-4.1 nano costs 23× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 nano or GPT-6 Sol?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-4.1 nano or GPT-6 Sol better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 1.05M.
How many benchmarks do GPT-4.1 nano and GPT-6 Sol share?
26 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and GPT-6 Sol has 45.