OpenAI, proprietary

# GPT-5.6 Sol

> GPT-5.6 Sol by OpenAI, released July 2026. Ranked #7 of 354 with a Noometry Index of 65.0. API: $4 in / $20 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-5-6-sol
- Last updated: 2026-10-10
- Title: GPT-5.6 Sol Benchmarks, Price & Rank (October 2026)

GPT-5.6 Sol by OpenAI ranks 7th of 354 ranked models on the Noometry Index as of October 2026, with a score of 65.0. Its strongest category is agentic & tool use, where it ranks 7th. API pricing starts at $4 per million input tokens and $20 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #7 of 354
- **Index score:** 65.0
- **Evidence:** Confirmed 65 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** July 9, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1.05M
- **Max output:** 128K
- **Input price:** $4 / M
- **Output price:** $20 / M
- **Blended price:** $8 / M
- **Output speed:** 10 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #193 of 219
- **Knowledge cutoff:** February 2026
- **Input:** text, image, pdf

## Category scores

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

GPT-5.6 Sol category scores

1.  Coding 65.1
2.  Agentic & Tool Use 50.3
3.  Reasoning 74.8
4.  Math 85.6
5.  Knowledge 64.3
6.  Multimodal 48.6
7.  Multilingual 55.3
8.  Instruction Following 77.7
9.  Long Context 45.4
10.  Writing & Preference 73.3
11.  20406080100

GPT-5.6 Sol category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 65.1 | #7 | 10 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 50.3 | #7 | 7 |
| [Reasoning](https://noometry.com/best/reasoning) | 74.8 | #8 | 14 |
| [Math](https://noometry.com/best/math) | 85.6 | #9 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 64.3 | #18 | 4 |
| [Multimodal](https://noometry.com/best/multimodal) | 48.6 | #9 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 55.3 | #32 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.7 | #16 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | #42 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 73.3 | #12 | 5 |

## Strengths and weaknesses

Categories where GPT-5.6 Sol places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-5.6 Sol: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 65.1 | +26.3 | #7 of 340, top 3% |
| [Reasoning](https://noometry.com/best/reasoning) | 74.8 | +51.2 | #8 of 350, top 3% |
| [Math](https://noometry.com/best/math) | 85.6 | +49.0 | #9 of 327, top 3% |

### Weakest categories

GPT-5.6 Sol: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | +4.4 | #42 of 296, top 15% |
| [Multilingual](https://noometry.com/best/multilingual) | 55.3 | +7.9 | #32 of 297, top 11% |
| [Multimodal](https://noometry.com/best/multimodal) | 48.6 | +10.1 | #9 of 128, top 8% |

## Closest competitors

The models ranked just above and below GPT-5.6 Sol. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-5.6 Sol
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [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-5-6-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-5-6-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-5-6-sol) |
| [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | #6 | 65.6 | $4 | — | [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-5-6-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-5-6-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-5-6-sol) |
| [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) | #11 | 61.8 | $1.50 | — | [Compare](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-6-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-5.6 Sol Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 69.4% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 45.4% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 72.7% | #5 of 29, top 18% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 61.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 70.7% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 47.5% | #11 of 37, top 30% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 35.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 24.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 41.7% | #7 of 14, top 50% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 31.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 37.7% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1618 | #19 of 113, top 17% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [FrontierSWE](https://noometry.com/benchmarks/frontierswe) | 32.2% | #8 of 18, top 45% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 56.9% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 55.4% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 57.1% | #16 of 121, top 14% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 56.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 47.1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 56% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 76.5% | #4 of 31, top 13% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 88.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 87% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 89.4% | #5 of 119, top 5% | promax | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1498 | #21 of 294, top 8% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MirrorCode](https://noometry.com/benchmarks/mirrorcode) | 20% | #6 of 9, top 67% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 2,177 | #3 of 105, top 3% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GPT-5.6 Sol Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 51.4% | #24 of 49, top 49% | promax | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 27.3% | #2 of 9, top 23% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 46.9% | #4 of 26, top 16% | xhigh | [τ²-bench](https://taubench.com/) | 2026-08-04 |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 36.2% | #2 of 11, top 19% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 60% | #3 of 35, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GBAEval](https://noometry.com/benchmarks/gbaeval) | 52.6% | #7 of 23, top 31% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 30.7% | #3 of 36, top 9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 30.7% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1257 | Best of 32 | xhigh | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 9,619 | #7 of 60, top 12% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GPT-5.6 Sol Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 85.4% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 42.5% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 92.5% | #4 of 83, top 5% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 67.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 90% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 64.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 71.7% | #10 of 77, top 13% | prounknown | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 71.7% |  | proxhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 64.8% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 67% | #30 of 99, top 31% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 93.8% | #9 of 91, top 10% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 97% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 74.5% |  | 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) | 92.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 97.5% | #9 of 83, top 11% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 25.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 14.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 32.3% | Best of 134 | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 22.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 5.1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 28.6% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 27% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 55% |  | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 7% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 64% | #3 of 129, top 3% | promax | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-10 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 37.1% | #2 of 38, top 6% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 44.8% | #7 of 24, top 30% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-04 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1484 | #26 of 297, top 9% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 26% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 58% | #7 of 74, top 10% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-28 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 33% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 95.5% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 93.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 95.5% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 95.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 83.7% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 96% | #14 of 151, top 10% | promax | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 95.7% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 56.9% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 55.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 58.4% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 56.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 52.8% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 59.2% | #6 of 125, top 5% | promax | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 58.5% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 93.1% | #3 of 25, top 12% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.14 |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.14 | #7 of 10, top 70% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.14 |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 161.66 | #10 of 213, top 5% |  | [Epoch AI](https://epoch.ai/eci) | 2026-07-09 |

### Math

GPT-5.6 Sol Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 89.1% | #6 of 81, top 8% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 82.9% | #7 of 63, top 12% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 80.5% |  | promax | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 95.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 100% | #7 of 173, top 5% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 68.9% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 83% | #10 of 77, top 13% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1474 | #35 of 285, top 13% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath Erdős](https://noometry.com/benchmarks/frontiermath-erdos) | 0% | #7 of 7, top 100% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |

### Knowledge

GPT-5.6 Sol Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 89.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 93.5% | #14 of 186, top 8% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 82.8% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 69.7% | #8 of 77, top 11% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 12.4% | #78 of 96, top 82% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1516 | #12 of 273, top 5% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-5.6 Sol Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1281 | #28 of 122, top 23% | xhigh | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 33.6% | #10 of 31, top 33% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 56.7% | #8 of 31, top 26% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1483 | #7 of 38, top 19% | xhigh | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

GPT-5.6 Sol Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1452 | #31 of 297, top 11% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1527 | #21 of 285, top 8% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1477 | #29 of 223, top 14% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1476 | #22 of 231, top 10% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1471 | #18 of 211, top 9% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1442 | #24 of 213, top 12% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1468 | #28 of 283, top 10% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1441 | #64 of 226, top 29% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-5.6 Sol Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1482 | #13 of 298, top 5% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-5.6 Sol Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1480 | #23 of 291, top 8% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-5.6 Sol Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1457 | #37 of 297, top 13% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1448 | #30 of 295, top 11% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1972 | #9 of 115, top 8% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1250 | #9 of 28, top 33% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1460 | #38 of 295, top 13% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-5.6 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) | $4 | $20 | $0.50 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $4 | $20 | $0.40 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $4 | $20 | $0.40 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-5.6-sol) | $2 | $10 | $0.20 | 2026-10-10 |

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

## Compare GPT-5.6 Sol

-   [GPT-5.6 Sol vs GPT-6.1 Sol](https://noometry.com/compare/gpt-5-6-sol-vs-gpt-6-1-sol)
-   [GPT-5.6 Sol vs GPT-5.5 Pro](https://noometry.com/compare/gpt-5-5-pro-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs Claude Fable 5](https://noometry.com/compare/claude-fable-5-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs GPT-5.5](https://noometry.com/compare/gpt-5-5-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs Claude Opus 5](https://noometry.com/compare/claude-opus-5-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs Claude Sonnet 5.5](https://noometry.com/compare/claude-sonnet-5-5-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs Kimi K3](https://noometry.com/compare/gpt-5-6-sol-vs-kimi-k3)
-   [GPT-5.6 Sol vs Grok 4.6](https://noometry.com/compare/gpt-5-6-sol-vs-grok-4-6)
-   [GPT-5.6 Sol vs Qwen3.8 Max](https://noometry.com/compare/gpt-5-6-sol-vs-qwen3-8-max)
-   [GPT-5.6 Sol vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs Muse Spark 1.3](https://noometry.com/compare/gpt-5-6-sol-vs-muse-spark-1-3)
-   [GPT-5.6 Sol vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gpt-5-6-sol)

## Other OpenAI models

-   [GPT-6 Astra](https://noometry.com/models/gpt-6-astra)70.8
-   [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol)65.6
-   [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-5.6 Sol?

GPT-5.6 Sol by OpenAI ranks 7th of 354 ranked models on the Noometry Index as of October 2026, with a score of 65.0. Its strongest category is agentic & tool use, where it ranks 7th. API pricing starts at $4 per million input tokens and $20 per million output tokens, with a 1.05M-token context window.

### How much does GPT-5.6 Sol cost?

GPT-5.6 Sol costs $4 per million input tokens and $20 per million output tokens on OpenAI's own API, with cached input at $0.40.

### What is GPT-5.6 Sol's context window?

GPT-5.6 Sol accepts up to 1.05M tokens of input and can write up to 128K tokens in one response.

### Is GPT-5.6 Sol open source?

No. GPT-5.6 Sol is proprietary and available only through OpenAI's API and partner platforms.

### How fast is GPT-5.6 Sol?

GPT-5.6 Sol generated about 10 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are GPT-5.6 Sol's strengths and weaknesses?

Relative to other ranked models, GPT-5.6 Sol places best in coding, reasoning, math and lowest in long context, multilingual, multimodal.

### What is GPT-5.6 Sol best at?

Its best category is agentic & tool use, where it ranks 7th on Noometry.

### Cite this page

Noometry. (2026). GPT-5.6 Sol benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-5-6-sol

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gpt-5-6-sol.md).