OpenAI, proprietary

# GPT-5.5

> GPT-5.5 by OpenAI, released April 2026. Ranked #9 of 354 with a Noometry Index of 63.4. API: $5 in / $30 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-5-5
- Last updated: 2026-10-10
- Title: GPT-5.5 Benchmarks, Price & Rank (October 2026) | Noometry

GPT-5.5 by OpenAI ranks 9th of 354 ranked models on the Noometry Index as of October 2026, with a score of 63.4. Its strongest category is agentic & tool use, where it ranks 6th. API pricing starts at $5 per million input tokens and $30 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #9 of 354
- **Index score:** 63.4
- **Evidence:** Confirmed 71 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** April 23, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1.05M
- **Max output:** 128K
- **Input price:** $5 / M
- **Output price:** $30 / M
- **Blended price:** $11.25 / M
- **Output speed:** 25 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #204 of 219
- **Knowledge cutoff:** December 2025
- **Input:** text, image, pdf

## Category scores

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

GPT-5.5 category scores

1.  Coding 58.2
2.  Agentic & Tool Use 50.7
3.  Reasoning 72.8
4.  Math 81.7
5.  Knowledge 64.4
6.  Multimodal 46.9
7.  Multilingual 56.4
8.  Instruction Following 77.5
9.  Long Context 48.3
10.  Writing & Preference 72.7
11.  405060708090

GPT-5.5 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 58.2 | #17 | 9 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 50.7 | #6 | 10 |
| [Reasoning](https://noometry.com/best/reasoning) | 72.8 | #11 | 13 |
| [Math](https://noometry.com/best/math) | 81.7 | #11 | 6 |
| [Knowledge](https://noometry.com/best/knowledge) | 64.4 | #17 | 4 |
| [Multimodal](https://noometry.com/best/multimodal) | 46.9 | #12 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 56.4 | #20 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.5 | #18 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 48.3 | #12 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 72.7 | #13 | 5 |

## Strengths and weaknesses

Categories where GPT-5.5 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-5.5: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 72.8 | +49.2 | #11 of 350, top 4% |
| [Math](https://noometry.com/best/math) | 81.7 | +45.1 | #11 of 327, top 4% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 50.7 | +20.3 | #6 of 154, top 4% |

### Weakest categories

GPT-5.5: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 46.9 | +8.4 | #12 of 128, top 10% |
| [Multilingual](https://noometry.com/best/multilingual) | 56.4 | +9.0 | #20 of 297, top 7% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.5 | +6.3 | #18 of 305, top 6% |

## Closest competitors

The models ranked just above and below GPT-5.5. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-5.5
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [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-5-5) |
| [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | #6 | 65.6 | $4 | — | [Compare](https://noometry.com/compare/gpt-5-5-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-5-5-vs-gpt-5-6-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-vs-gpt-5-5-pro) |
| [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-5-5) |
| [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-5-5) |
| [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | #12 | 61.8 | $4 | — | [Compare](https://noometry.com/compare/gpt-5-5-vs-gpt-6-sol) |
| [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | #13 | 60.7 | $10 | 34 | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gpt-5-5) |

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-5.5 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 80.6% | #2 of 32, top 7% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-24 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 64.4% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 27% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 54% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 67% | #13 of 29, top 45% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 43% | #15 of 37, top 41% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1513 | #44 of 113, top 39% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1487 |  |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1456 |  |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 55.9% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 51.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 53.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 47.3% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 56.1% | #23 of 121, top 20% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 40.2% | #8 of 31, top 26% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 83.9% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 67.2% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 84.9% | #6 of 119, top 6% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1494 | #27 of 294, top 10% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MirrorCode](https://noometry.com/benchmarks/mirrorcode) | 10% | #8 of 9, top 89% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,589 |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,128 |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,943 | #9 of 105, top 9% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GPT-5.5 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 84.7% | Best of 41 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 55.1% | #18 of 49, top 37% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 13% | #5 of 9, top 56% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index) | 6.3% | #5 of 14, top 36% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 44.6% | #5 of 26, top 20% | xhigh | [τ²-bench](https://taubench.com/) | 2026-05-05 |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 54% | #4 of 24, top 17% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 48.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 49.6% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 27.2% | #8 of 11, top 73% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ExploitBench](https://noometry.com/benchmarks/exploitbench) | 47.4% | #2 of 9, top 23% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GBAEval](https://noometry.com/benchmarks/gbaeval) | 53.2% | #6 of 23, top 27% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 26% | #9 of 36, top 25% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1242 | #3 of 32, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 7,524 | #13 of 60, top 22% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GPT-5.5 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 83.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 33.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 70.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 85% | #10 of 83, top 13% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 69% | #13 of 77, top 17% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 88.8% | #3 of 99, top 4% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 96.2% | #4 of 91, top 5% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 94.5% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 76.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 92.2% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 95% | #15 of 83, top 19% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 25.4% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 18.6% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 1.4% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 27.1% | #14 of 134, top 11% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 26% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 10% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 54% | #8 of 129, top 7% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-24 |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 34.3% | #9 of 24, top 38% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-27 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1489 | #17 of 297, top 6% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 52% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 28% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 18% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 56% | #8 of 74, top 11% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-24 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 96% | #12 of 151, top 8% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 54.3% | #12 of 125, top 10% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 88.1% | #4 of 25, top 16% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 81.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.14 | #3 of 10, top 30% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 159.1 | #12 of 213, top 6% |  | [Epoch AI](https://epoch.ai/eci) | 2026-04-23 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 60.6 | #25 of 72, top 35% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GPT-5.5 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 85.3% | #12 of 81, top 15% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 72.5% | #12 of 63, top 20% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 94.3% | Best of 29 | xhigh | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 84.4% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 57.8% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 100% | #5 of 173, top 3% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-24 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 50% | #30 of 77, top 39% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1486 | #23 of 285, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 51.7% | #2 of 68, top 3% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-23 |
| [FrontierMath Erdős](https://noometry.com/benchmarks/frontiermath-erdos) | 0% | #6 of 7, top 86% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 35.4% | #4 of 55, top 8% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-23 |

### Knowledge

GPT-5.5 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 90.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-05 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 77.3% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 94% | #10 of 186, top 6% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-24 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 63% | #13 of 77, top 17% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 9.3% | #46 of 96, top 48% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1508 | #17 of 273, top 7% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-5.5 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1297 | #17 of 122, top 14% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 36.2% | #8 of 31, top 26% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 44.2% | #11 of 31, top 36% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1486 | #6 of 38, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

GPT-5.5 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1467 | #20 of 297, top 7% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1533 | #11 of 285, top 4% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1486 | #24 of 223, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1480 | #20 of 231, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1498 | #7 of 211, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1460 | #10 of 213, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1473 | #24 of 283, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1468 | #28 of 226, top 13% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-5.5 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1479 | #15 of 298, top 6% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-5.5 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [CL-bench Life](https://noometry.com/benchmarks/cl-bench-life) | 22.2% | Best of 13 | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1484 | #15 of 291, top 6% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-5.5 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1472 | #22 of 297, top 8% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1455 | #22 of 295, top 8% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1844 | #16 of 115, top 14% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1315 | #4 of 28, top 15% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1476 | #24 of 295, top 9% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-5.5 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $5 | $30 | $0.50 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $5.50 | $33 | $0.55 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $5 | $30 | $0.50 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-5.5) | $5 | $30 | $0.50 | 2026-10-10 |

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

## Compare GPT-5.5

-   [GPT-5.5 vs GPT-5.4](https://noometry.com/compare/gpt-5-4-vs-gpt-5-5)
-   [GPT-5.5 vs GPT-5.5 Pro](https://noometry.com/compare/gpt-5-5-vs-gpt-5-5-pro)
-   [GPT-5.5 vs Claude Sonnet 5.5](https://noometry.com/compare/claude-sonnet-5-5-vs-gpt-5-5)
-   [GPT-5.5 vs GPT-5.6 Sol](https://noometry.com/compare/gpt-5-5-vs-gpt-5-6-sol)
-   [GPT-5.5 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-5)
-   [GPT-5.5 vs GPT-6.1 Sol](https://noometry.com/compare/gpt-5-5-vs-gpt-6-1-sol)
-   [GPT-5.5 vs GPT-6 Sol](https://noometry.com/compare/gpt-5-5-vs-gpt-6-sol)
-   [GPT-5.5 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-5-5)
-   [GPT-5.5 vs Kimi K3](https://noometry.com/compare/gpt-5-5-vs-kimi-k3)
-   [GPT-5.5 vs Grok 4.6](https://noometry.com/compare/gpt-5-5-vs-grok-4-6)
-   [GPT-5.5 vs Qwen3.8 Max](https://noometry.com/compare/gpt-5-5-vs-qwen3-8-max)
-   [GPT-5.5 vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-5-5)
-   [GPT-5.5 vs Muse Spark 1.3](https://noometry.com/compare/gpt-5-5-vs-muse-spark-1-3)
-   [GPT-5.5 vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gpt-5-5)

## 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-5.6 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-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-5.5?

GPT-5.5 by OpenAI ranks 9th of 354 ranked models on the Noometry Index as of October 2026, with a score of 63.4. Its strongest category is agentic & tool use, where it ranks 6th. API pricing starts at $5 per million input tokens and $30 per million output tokens, with a 1.05M-token context window.

### How much does GPT-5.5 cost?

GPT-5.5 costs $5 per million input tokens and $30 per million output tokens on OpenAI's own API, with cached input at $0.50.

### What is GPT-5.5's context window?

GPT-5.5 accepts up to 1.05M tokens of input and can write up to 128K tokens in one response.

### Is GPT-5.5 open source?

No. GPT-5.5 is proprietary and available only through OpenAI's API and partner platforms.

### How fast is GPT-5.5?

GPT-5.5 generated about 25 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are GPT-5.5's strengths and weaknesses?

Relative to other ranked models, GPT-5.5 places best in reasoning, math, agentic & tool use and lowest in multimodal, multilingual, instruction following.

### What is GPT-5.5 best at?

Its best category is agentic & tool use, where it ranks 6th on Noometry.

### Cite this page

Noometry. (2026). GPT-5.5 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-5-5

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