Model comparison
GPT-6.1 Sol vs o4-mini
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 41.6 on the Noometry Index. o4-mini costs 2.1× 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-6.1 Sol scores higher in 9 categories and o4-mini in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6.1 Sol leads 81.9 to 24.6.
- The biggest single-benchmark swing is FrontierMath Tier 4: 100% for GPT-6.1 Sol and 4.9% for o4-mini.
- o4-mini is cheaper at $1.10 / $4.40 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 200K.
Side by side
| GPT-6.1 Sol | o4-mini | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 65.6 | 41.6 |
| Released | 2026-09-29 | 2025-04-16 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $2 | $1.10 |
| Output $ / M tokens | $10 | $4.40 |
| Results tracked | 34 | 60 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 63.2 (#8), o4-mini: 40.9 (#127)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| LMArena Coding | 1487 | 1368 |
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| LMArena WebDev | 1755 | — |
| SciCode | 55.8% | — |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
Agentic & Tool Use GPT-6.1 Sol leads
GPT-6.1 Sol: 39.6 (#26), o4-mini: 32.6 (#61)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| APEX-Agents | 60% | — |
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| GDP.pdf | 32% | — |
| METR Time Horizons | — | 63.9% |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), o4-mini: 24.6 (#162)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| ARC-AGI-2 | 94.2% | 6.1% |
| ARC-AGI-1 | 98.5% | 58.7% |
| CritPt | 31.7% | 0.6% |
| Chess Puzzles | 61% | 26% |
| LMArena Hard Prompts | 1466 | 1351 |
| Mystery Game Puzzles | 80% | 5% |
| Epoch Capabilities Index | 166.09 | 145.64 |
| SimpleBench | — | 38.7% |
| Kagi LLM Benchmark | — | 67.6% |
| NYT Connections (extended) | 95.5% | — |
| EnigmaEval | — | 9.2% |
| EBR-Bench | 54.3% | — |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| ForecastBench | — | 61.8 |
Math GPT-6.1 Sol leads
GPT-6.1 Sol: 93.7 (#1), o4-mini: 40.8 (#89)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 93.7% | 36.1% |
| FrontierMath Tier 4 | 100% | 4.9% |
| OTIS Mock AIME 2024-2025 | 100% | 81.7% |
| LMArena Math | 1464 | 1389 |
| ProofBench | 99% | — |
| Omni-MATH | — | 72% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), o4-mini: 43.6 (#91)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| GPQA Diamond | 95.4% | 79.6% |
| SimpleQA Verified | 73.9% | 19.6% |
| LMArena Expert | 1502 | 1343 |
| Humanity's Last Exam | — | 18.1% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| Vectara Hallucination Rate | — | 18.6% |
| GPQA (HELM) | — | 73.5% |
Multimodal GPT-6.1 Sol leads
GPT-6.1 Sol: 52.7 (#5), o4-mini: 40.2 (#49)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| LMArena Vision | 1288 | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
| Furniture Assembly | 80% | — |
Multilingual GPT-6.1 Sol leads
GPT-6.1 Sol: 54.3 (#46), o4-mini: 47.0 (#154)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| LMArena Non-English | 1438 | 1337 |
| LMArena Chinese | 1477 | 1354 |
| LMArena Russian | 1455 | 1334 |
| LMArena French | — | 1364 |
| LMArena German | — | 1336 |
| LMArena Japanese | — | 1308 |
| LMArena Korean | — | 1312 |
| LMArena Spanish | — | 1347 |
Instruction Following GPT-6.1 Sol leads
GPT-6.1 Sol: 77.0 (#29), o4-mini: 75.2 (#68)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1468 | 1321 |
| IFEval | — | 92.8% |
Long Context Too close to call
GPT-6.1 Sol: 44.9 (#54), o4-mini: 45.5 (#33)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| LMArena Longer Query | 1465 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference GPT-6.1 Sol leads
GPT-6.1 Sol: 63.6 (#63), o4-mini: 54.0 (#152)
| Benchmark | GPT-6.1 Sol | o4-mini |
|---|---|---|
| LMArena Text | 1447 | 1353 |
| LMArena Creative Writing | 1432 | 1294 |
| LMArena Multi-Turn | 1449 | 1350 |
| Short-Story Creative Writing | — | 75% |
| WildBench | — | 85.4% |
Frequently asked questions
Is GPT-6.1 Sol better than o4-mini?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 41.6 on the Noometry Index. o4-mini costs 2.1× 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-6.1 Sol or o4-mini?
o4-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is GPT-6.1 Sol or o4-mini better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 200K.
How many benchmarks do GPT-6.1 Sol and o4-mini share?
24 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and o4-mini has 60.