Model comparison
GPT-6.1 Sol vs gpt-oss-120b
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 36.3 on the Noometry Index. gpt-oss-120b costs 57× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GPT-6.1 Sol scores higher in 9 categories and gpt-oss-120b in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6.1 Sol leads 81.9 to 20.0.
- The biggest single-benchmark swing is Mystery Game Puzzles: 80% for GPT-6.1 Sol and 2% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| GPT-6.1 Sol | gpt-oss-120b | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 65.6 | 36.3 |
| Released | 2026-09-29 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 41K |
| Input $ / M tokens | $2 | $0.037 |
| Output $ / M tokens | $10 | $0.17 |
| Results tracked | 34 | 48 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 63.2 (#8), gpt-oss-120b: 33.5 (#256)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| SciCode | 55.8% | 36% |
| LMArena Coding | 1487 | 1380 |
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| LMArena WebDev | 1755 | — |
| WeirdML | — | 48.2% |
| ALE-Bench | — | 575.62 |
| AlgoTune | — | 1.41 |
Agentic & Tool Use GPT-6.1 Sol leads
GPT-6.1 Sol: 39.6 (#26), gpt-oss-120b: 12.2 (#153)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| APEX-Agents | 60% | 4.4% |
| Terminal-Bench | — | 18.7% |
| GDP.pdf | 32% | — |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), gpt-oss-120b: 20.0 (#245)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| CritPt | 31.7% | 1.1% |
| Chess Puzzles | 61% | 20% |
| LMArena Hard Prompts | 1466 | 1364 |
| Mystery Game Puzzles | 80% | 2% |
| Epoch Capabilities Index | 166.09 | 139.93 |
| ARC-AGI-2 | 94.2% | — |
| SimpleBench | — | 22.1% |
| Kagi LLM Benchmark | — | 58.6% |
| NYT Connections (extended) | 95.5% | — |
| ARC-AGI-1 | 98.5% | — |
| EBR-Bench | 54.3% | — |
| DTBench | — | 76.3% |
| LMCA | — | 22.1% |
| Surface Evolver Bench | — | 25% |
Math GPT-6.1 Sol leads
GPT-6.1 Sol: 93.7 (#1), gpt-oss-120b: 52.5 (#50)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 88.9% |
| LMArena Math | 1464 | 1389 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 100% | — |
| ProofBench | 99% | — |
| Omni-MATH | — | 68.8% |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), gpt-oss-120b: 42.4 (#96)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 95.4% | 75.8% |
| LMArena Expert | 1502 | 1356 |
| SimpleQA Verified | 73.9% | — |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| Vectara Hallucination Rate | — | 14.2% |
| GPQA (HELM) | — | 68.4% |
Multimodal Not comparable
GPT-6.1 Sol: 52.7 (#5), gpt-oss-120b: —
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1288 | — |
| Furniture Assembly | 80% | — |
Multilingual GPT-6.1 Sol leads
GPT-6.1 Sol: 54.3 (#46), gpt-oss-120b: 48.0 (#147)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1438 | 1351 |
| LMArena Chinese | 1477 | 1385 |
| LMArena Russian | 1455 | 1343 |
| LMArena French | — | 1369 |
| LMArena German | — | 1353 |
| LMArena Japanese | — | 1331 |
| LMArena Korean | — | 1282 |
| LMArena Spanish | — | 1389 |
Instruction Following GPT-6.1 Sol leads
GPT-6.1 Sol: 77.0 (#29), gpt-oss-120b: 69.3 (#173)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1468 | 1318 |
| IFEval | — | 83.6% |
Long Context GPT-6.1 Sol leads
GPT-6.1 Sol: 44.9 (#54), gpt-oss-120b: 31.4 (#278)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1465 | 1319 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference GPT-6.1 Sol leads
GPT-6.1 Sol: 63.6 (#63), gpt-oss-120b: 46.5 (#217)
| Benchmark | GPT-6.1 Sol | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1447 | 1365 |
| LMArena Creative Writing | 1432 | 1275 |
| LMArena Multi-Turn | 1449 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| EQ-Bench Creative Writing | — | 961 |
| WildBench | — | 84.5% |
Frequently asked questions
Is GPT-6.1 Sol better than gpt-oss-120b?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 36.3 on the Noometry Index. gpt-oss-120b costs 57× 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 gpt-oss-120b?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is GPT-6.1 Sol or gpt-oss-120b better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6.1 Sol and gpt-oss-120b share?
20 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and gpt-oss-120b has 48.