Model comparison
GPT-5 Nano vs GPT-6.1 Sol
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 33.5 on the Noometry Index. GPT-5 Nano costs 29× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Summary
- They share 24 benchmarks with published results for both. GPT-5 Nano scores higher in 0 categories and GPT-6.1 Sol in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6.1 Sol leads 81.9 to 16.3.
- The biggest single-benchmark swing is FrontierMath Tier 4: 2.4% for GPT-5 Nano and 100% for GPT-6.1 Sol.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 400K.
Side by side
| GPT-5 Nano | GPT-6.1 Sol | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 33.5 | 65.6 |
| Released | 2025-08-07 | 2026-09-29 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1.05M |
| Max output | 128K | 128K |
| Input $ / M tokens | $0.05 | $2 |
| Output $ / M tokens | $0.40 | $10 |
| Results tracked | 49 | 34 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-6.1 Sol leads
GPT-5 Nano: 33.6 (#254), GPT-6.1 Sol: 63.2 (#8)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| LMArena Coding | 1351 | 1487 |
| DeepSWE | — | 75.2% |
| FrontierCode | — | 50.2% |
| SWE-bench Verified (bash only) | 34.8% | — |
| LMArena WebDev | — | 1755 |
| SciCode | — | 55.8% |
| WeirdML | 38.1% | — |
| ALE-Bench | 718.67 | — |
Agentic & Tool Use GPT-6.1 Sol leads
GPT-5 Nano: 25.8 (#106), GPT-6.1 Sol: 39.6 (#26)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| APEX-Agents | — | 60% |
| Berkeley Function Calling Leaderboard | 51.5% | — |
| GDP.pdf | — | 32% |
Reasoning GPT-6.1 Sol leads
GPT-5 Nano: 16.3 (#306), GPT-6.1 Sol: 81.9 (#2)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| ARC-AGI-2 | 2.6% | 94.2% |
| ARC-AGI-1 | 20.7% | 98.5% |
| Chess Puzzles | 27% | 61% |
| LMArena Hard Prompts | 1328 | 1466 |
| Mystery Game Puzzles | 9% | 80% |
| Epoch Capabilities Index | 139.38 | 166.09 |
| Kagi LLM Benchmark | 62.2% | — |
| NYT Connections (extended) | — | 95.5% |
| CritPt | — | 31.7% |
| EBR-Bench | — | 54.3% |
| DTBench | 62.7% | — |
| LMCA | 7.9% | — |
| ForecastBench | 59.1 | — |
Math GPT-6.1 Sol leads
GPT-5 Nano: 29.4 (#241), GPT-6.1 Sol: 93.7 (#1)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 20% | 93.7% |
| FrontierMath Tier 4 | 2.4% | 100% |
| OTIS Mock AIME 2024-2025 | 81.1% | 100% |
| ProofBench | 12% | 99% |
| LMArena Math | 1317 | 1464 |
| Omni-MATH | 54.6% | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-6.1 Sol leads
GPT-5 Nano: 35.9 (#178), GPT-6.1 Sol: 71.8 (#4)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| GPQA Diamond | 69.4% | 95.4% |
| SimpleQA Verified | 11.7% | 73.9% |
| LMArena Expert | 1321 | 1502 |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
Multimodal GPT-6.1 Sol leads
GPT-5 Nano: 31.3 (#108), GPT-6.1 Sol: 52.7 (#5)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| LMArena Vision | 1159 | 1288 |
| VPCT | 37.2% | — |
| Furniture Assembly | — | 80% |
Multilingual GPT-6.1 Sol leads
GPT-5 Nano: 45.3 (#172), GPT-6.1 Sol: 54.3 (#46)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| LMArena Non-English | 1313 | 1438 |
| LMArena Chinese | 1356 | 1477 |
| LMArena Russian | 1296 | 1455 |
| LMArena German | 1327 | — |
| LMArena Japanese | 1226 | — |
| LMArena Korean | 1269 | — |
| LMArena Spanish | 1360 | — |
Instruction Following GPT-6.1 Sol leads
GPT-5 Nano: 75.0 (#79), GPT-6.1 Sol: 77.0 (#29)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| LMArena Instruction Following | 1306 | 1468 |
| IFEval | 93.2% | — |
Long Context GPT-6.1 Sol leads
GPT-5 Nano: 31.3 (#281), GPT-6.1 Sol: 44.9 (#54)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| LMArena Longer Query | 1312 | 1465 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference GPT-6.1 Sol leads
GPT-5 Nano: 39.1 (#249), GPT-6.1 Sol: 63.6 (#63)
| Benchmark | GPT-5 Nano | GPT-6.1 Sol |
|---|---|---|
| LMArena Text | 1320 | 1447 |
| LMArena Creative Writing | 1249 | 1432 |
| LMArena Multi-Turn | 1311 | 1449 |
| EQ-Bench Creative Writing | 705 | — |
| WildBench | 80.6% | — |
Frequently asked questions
Is GPT-5 Nano better than GPT-6.1 Sol?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 33.5 on the Noometry Index. GPT-5 Nano costs 29× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Nano or GPT-6.1 Sol?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is GPT-5 Nano or GPT-6.1 Sol better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 33.6 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 400K.
How many benchmarks do GPT-5 Nano and GPT-6.1 Sol share?
24 benchmarks have published results for both models. GPT-5 Nano has 49 scored results on Noometry and GPT-6.1 Sol has 34.