OpenAI, proprietary

# GPT-6.1 Sol

> GPT-6.1 Sol by OpenAI, released September 2026. Ranked #6 of 354 with a Noometry Index of 65.6. API: $2 in / $10 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-6-1-sol
- Last updated: 2026-10-10
- Title: GPT-6.1 Sol Benchmarks, Price & Rank (October 2026)

GPT-6.1 Sol by OpenAI ranks 6th of 354 ranked models on the Noometry Index as of October 2026, with a score of 65.6. Its strongest category is math, where it ranks 1st. API pricing starts at $2 per million input tokens and $10 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #6 of 354
- **Index score:** 65.6
- **Evidence:** Confirmed 34 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** September 29, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1.05M
- **Max output:** 128K
- **Input price:** $2 / M
- **Output price:** $10 / M
- **Blended price:** $4 / M
- **Output speed:** Not measured
- **Value:** #160 of 219
- **Knowledge cutoff:** April 2026
- **Input:** text, image, pdf

## Category scores

Each category score combines every public result we have in that category.

GPT-6.1 Sol category scores

1.  Coding 63.2
2.  Agentic & Tool Use 39.6
3.  Reasoning 81.9
4.  Math 93.7
5.  Knowledge 71.8
6.  Multimodal 52.7
7.  Multilingual 54.3
8.  Instruction Following 77.0
9.  Long Context 44.9
10.  Writing & Preference 63.6
11.  050100

GPT-6.1 Sol category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 63.2 | #8 | 5 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 39.6 | #26 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 81.9 | #2 | 8 |
| [Math](https://noometry.com/best/math) | 93.7 | #1 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 71.8 | #4 | 3 |
| [Multimodal](https://noometry.com/best/multimodal) | 52.7 | #5 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 54.3 | #46 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.0 | #29 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 44.9 | #54 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 63.6 | #63 | 3 |

## Strengths and weaknesses

Categories where GPT-6.1 Sol places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-6.1 Sol: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Math](https://noometry.com/best/math) | 93.7 | +57.2 | #1 of 327, top 1% |
| [Reasoning](https://noometry.com/best/reasoning) | 81.9 | +58.3 | #2 of 350, top 1% |
| [Knowledge](https://noometry.com/best/knowledge) | 71.8 | +34.4 | #4 of 314, top 2% |

### Weakest categories

GPT-6.1 Sol: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Writing & Preference](https://noometry.com/best/writing) | 63.6 | +9.8 | #63 of 312, top 21% |
| [Long Context](https://noometry.com/best/long-context) | 44.9 | +3.9 | #54 of 296, top 19% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 39.6 | +9.3 | #26 of 154, top 17% |

## Closest competitors

The models ranked just above and below GPT-6.1 Sol. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-6.1 Sol
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | #2 | 69.0 | $20 | — | [Compare](https://noometry.com/compare/claude-fable-5-1-vs-gpt-6-1-sol) |
| [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | #3 | 68.6 | $8 | — | [Compare](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-1-sol) |
| [Claude Opus 5](https://noometry.com/models/claude-opus-5) | #4 | 67.8 | $10 | — | [Compare](https://noometry.com/compare/claude-opus-5-vs-gpt-6-1-sol) |
| [Claude Fable 5](https://noometry.com/models/claude-fable-5) | #5 | 66.8 | $20 | 25 | [Compare](https://noometry.com/compare/claude-fable-5-vs-gpt-6-1-sol) |
| [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | #7 | 65.0 | $8 | 10 | [Compare](https://noometry.com/compare/gpt-5-6-sol-vs-gpt-6-1-sol) |
| [GPT-5.5 Pro](https://noometry.com/models/gpt-5-5-pro) | #8 | 64.3 | $67.50 | — | [Compare](https://noometry.com/compare/gpt-5-5-pro-vs-gpt-6-1-sol) |
| [GPT-5.5](https://noometry.com/models/gpt-5-5) | #9 | 63.4 | $11.25 | 25 | [Compare](https://noometry.com/compare/gpt-5-5-vs-gpt-6-1-sol) |
| [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5) | #10 | 61.9 | $4 | — | [Compare](https://noometry.com/compare/claude-sonnet-5-5-vs-gpt-6-1-sol) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

GPT-6.1 Sol Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 75.2% | Best of 29 | high | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 64.4% |  | low | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 71.9% |  | max | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 73% |  | medium | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 71.9% |  | xhigh | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 50.2% | #7 of 37, top 19% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1755 | #4 of 113, top 4% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 55.8% | #24 of 121, top 20% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 53.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 54.2% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 53.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 55.7% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1487 | #36 of 294, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Agentic & Tool Use

GPT-6.1 Sol Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 60% | #12 of 49, top 25% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 32% | #2 of 36, top 6% | high | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 27% |  | low | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 31% |  | max | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 30% |  | medium | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 31.8% |  | xhigh | [Model card](https://openai.com/index/introducing-gpt-6-1-sol/) (self-reported) | 2026-09-29 |

### Reasoning

GPT-6.1 Sol Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 91.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 76.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 94.2% | #2 of 83, top 3% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 86.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 91.7% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 95.5% | #5 of 91, top 6% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 98.5% | #4 of 83, top 5% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 93.5% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 96.5% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 95.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 98.5% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 30% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 24.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 31.7% | #3 of 134, top 3% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 27.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 31.7% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 61% | #5 of 129, top 4% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 54.3% | #4 of 24, top 17% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1466 | #40 of 297, top 14% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 80% | #2 of 74, top 3% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 166.09 | #3 of 213, top 2% |  | [Epoch AI](https://epoch.ai/eci) | 2026-09-29 |

### Math

GPT-6.1 Sol Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 93.7% | Best of 81 | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 100% | Best of 63 | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 100% | #8 of 173, top 5% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 99% | #6 of 77, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1464 | #49 of 285, top 18% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

GPT-6.1 Sol Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 95.4% | #4 of 186, top 3% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 73.9% | #2 of 77, top 3% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1502 | #24 of 273, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-6.1 Sol Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1288 | #23 of 122, top 19% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 80% | #2 of 31, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 |

### Multilingual

GPT-6.1 Sol Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1438 | #46 of 297, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1477 | #68 of 285, top 24% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1455 | #38 of 283, top 14% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-6.1 Sol Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1468 | #25 of 298, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-6.1 Sol Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1465 | #36 of 291, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-6.1 Sol Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1447 | #50 of 297, top 17% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1432 | #48 of 295, top 17% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1449 | #56 of 295, top 19% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-6.1 Sol API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $2 | $10 | $0.10 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $2 | $10 | $0.10 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $2 | $10 | $0.10 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-6.1-sol) | $2 | $10 | $0.10 | 2026-10-10 |

[All OpenAI API prices →](https://noometry.com/llm-pricing/openai) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare GPT-6.1 Sol

-   [GPT-6.1 Sol vs GPT-6 Sol](https://noometry.com/compare/gpt-6-1-sol-vs-gpt-6-sol)
-   [GPT-6.1 Sol vs Claude Fable 5](https://noometry.com/compare/claude-fable-5-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs GPT-5.6 Sol](https://noometry.com/compare/gpt-5-6-sol-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs Claude Opus 5](https://noometry.com/compare/claude-opus-5-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs GPT-5.5 Pro](https://noometry.com/compare/gpt-5-5-pro-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs Claude Opus 5.5](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs GPT-5.5](https://noometry.com/compare/gpt-5-5-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs Kimi K3](https://noometry.com/compare/gpt-6-1-sol-vs-kimi-k3)
-   [GPT-6.1 Sol vs Grok 4.6](https://noometry.com/compare/gpt-6-1-sol-vs-grok-4-6)
-   [GPT-6.1 Sol vs Qwen3.8 Max](https://noometry.com/compare/gpt-6-1-sol-vs-qwen3-8-max)
-   [GPT-6.1 Sol vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-6-1-sol)
-   [GPT-6.1 Sol vs Muse Spark 1.3](https://noometry.com/compare/gpt-6-1-sol-vs-muse-spark-1-3)

## Other OpenAI models

-   [GPT-6 Astra](https://noometry.com/models/gpt-6-astra)70.8
-   [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol)65.0
-   [GPT-5.5 Pro](https://noometry.com/models/gpt-5-5-pro)64.3
-   [GPT-5.5](https://noometry.com/models/gpt-5-5)63.4
-   [GPT-6 Sol](https://noometry.com/models/gpt-6-sol)61.8
-   [GPT-5.4](https://noometry.com/models/gpt-5-4)59.4
-   [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra)59.2
-   [GPT-5.4 Pro](https://noometry.com/models/gpt-5-4-pro)58.9

## Frequently asked questions

### How good is GPT-6.1 Sol?

GPT-6.1 Sol by OpenAI ranks 6th of 354 ranked models on the Noometry Index as of October 2026, with a score of 65.6. Its strongest category is math, where it ranks 1st. API pricing starts at $2 per million input tokens and $10 per million output tokens, with a 1.05M-token context window.

### How much does GPT-6.1 Sol cost?

GPT-6.1 Sol costs $2 per million input tokens and $10 per million output tokens on OpenAI's own API, with cached input at $0.10.

### What is GPT-6.1 Sol's context window?

GPT-6.1 Sol accepts up to 1.05M tokens of input and can write up to 128K tokens in one response.

### Is GPT-6.1 Sol open source?

No. GPT-6.1 Sol is proprietary and available only through OpenAI's API and partner platforms.

### What are GPT-6.1 Sol's strengths and weaknesses?

Relative to other ranked models, GPT-6.1 Sol places best in math, reasoning, knowledge and lowest in writing & preference, long context, agentic & tool use.

### What is GPT-6.1 Sol best at?

Its best category is math, where it ranks 1st on Noometry.

### Cite this page

Noometry. (2026). GPT-6.1 Sol benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-6-1-sol

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gpt-6-1-sol.md).