Anthropic, proprietary

# Claude Opus 5

> Claude Opus 5 by Anthropic, released July 2026. Ranked #4 of 354 with a Noometry Index of 67.8. API: $5 in / $25 out per M tokens. 1M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/claude-opus-5
- Last updated: 2026-10-10
- Title: Claude Opus 5 Benchmarks, Price & Rank (October 2026)

Claude Opus 5 by Anthropic ranks 4th of 354 ranked models on the Noometry Index as of October 2026, with a score of 67.8. Its strongest category is agentic & tool use, where it ranks 1st. API pricing starts at $5 per million input tokens and $25 per million output tokens, with a 1M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #4 of 354
- **Index score:** 67.8
- **Evidence:** Confirmed 57 results
- **Provider:** [Anthropic](https://noometry.com/providers/anthropic)
- **Released:** July 24, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1M
- **Max output:** 128K
- **Input price:** $5 / M
- **Output price:** $25 / M
- **Blended price:** $10 / M
- **Output speed:** Not measured
- **Value:** #199 of 219
- **Knowledge cutoff:** May 2026
- **Input:** text, image, pdf

## Category scores

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

Claude Opus 5 category scores

1.  Coding 67.5
2.  Agentic & Tool Use 55.6
3.  Reasoning 77.2
4.  Math 86.2
5.  Knowledge 66.8
6.  Multimodal 50.8
7.  Multilingual 58.8
8.  Instruction Following 79.2
9.  Long Context 46.5
10.  Writing & Preference 79.2
11.  405060708090

Claude Opus 5 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 67.5 | #5 | 8 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 55.6 | #1 | 7 |
| [Reasoning](https://noometry.com/best/reasoning) | 77.2 | #4 | 11 |
| [Math](https://noometry.com/best/math) | 86.2 | #8 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 66.8 | #9 | 3 |
| [Multimodal](https://noometry.com/best/multimodal) | 50.8 | #8 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 58.8 | #4 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 79.2 | #7 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 46.5 | #21 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 79.2 | #1 | 5 |

## Strengths and weaknesses

Categories where Claude Opus 5 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Claude Opus 5: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Writing & Preference](https://noometry.com/best/writing) | 79.2 | +25.4 | #1 of 312, top 1% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 55.6 | +25.2 | #1 of 154, top 1% |
| [Reasoning](https://noometry.com/best/reasoning) | 77.2 | +53.6 | #4 of 350, top 2% |

### Weakest categories

Claude Opus 5: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 46.5 | +5.5 | #21 of 296, top 8% |
| [Multimodal](https://noometry.com/best/multimodal) | 50.8 | +12.3 | #8 of 128, top 7% |
| [Knowledge](https://noometry.com/best/knowledge) | 66.8 | +29.5 | #9 of 314, top 3% |

## Closest competitors

The models ranked just above and below Claude Opus 5. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Claude Opus 5
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | #1 | 70.8 | $20 | — | [Compare](https://noometry.com/compare/claude-opus-5-vs-gpt-6-astra) |
| [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-claude-opus-5) |
| [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | #3 | 68.6 | $8 | — | [Compare](https://noometry.com/compare/claude-opus-5-vs-claude-opus-5-5) |
| [Claude Fable 5](https://noometry.com/models/claude-fable-5) | #5 | 66.8 | $20 | 25 | [Compare](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5) |
| [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | #6 | 65.6 | $4 | — | [Compare](https://noometry.com/compare/claude-opus-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/claude-opus-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/claude-opus-5-vs-gpt-5-5-pro) |
| [GPT-5.5](https://noometry.com/models/gpt-5-5) | #9 | 63.4 | $11.25 | 25 | [Compare](https://noometry.com/compare/claude-opus-5-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

Claude Opus 5 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 72.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 58.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 73.6% | #4 of 29, top 14% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 68.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 73.2% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 53.4% | #3 of 37, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 44.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 40.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 46.6% | #4 of 14, top 29% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 43.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 46.1% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1691 | #6 of 113, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1657 |  | high | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [FrontierSWE](https://noometry.com/benchmarks/frontierswe) | 52% | #6 of 18, top 34% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 54.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 48% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 56.4% | #21 of 121, top 18% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 50.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 55% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 86.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 91.6% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 91.8% | #4 of 119, top 4% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1534 | #5 of 294, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1533 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 2,165 | #4 of 105, top 4% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Claude Opus 5 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 65.8% | #6 of 49, top 13% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 29% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 22.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 31.4% | Best of 9 | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 25.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 30.2% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 48.7% | #2 of 26, top 8% | max | [τ²-bench](https://taubench.com/) | 2026-08-04 |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 35% | #3 of 11, top 28% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 63.4% | #2 of 35, top 6% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GBAEval](https://noometry.com/benchmarks/gbaeval) | 79.6% | Best of 23 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 24% | #12 of 36, top 34% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 11,182 | #4 of 60, top 7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Claude Opus 5 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 88.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 90.4% | #5 of 83, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 80.6% | #2 of 77, top 3% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 94.3% | #7 of 91, top 8% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 97.5% | #8 of 83, top 10% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 97.5% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 28.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 23.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 29.1% | #11 of 134, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 26.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 27.7% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 33% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 20% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 42% | #17 of 129, top 14% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-24 |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 45.7% | #6 of 24, top 25% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-05 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1526 | #4 of 297, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1522 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 37% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 59% | #5 of 74, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-25 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 97.9% | #3 of 151, top 2% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 93.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 97.6% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 94.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 97.9% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 63.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 62.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 63.3% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 62.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 64.5% | #3 of 125, top 3% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.12 | #10 of 10, top 100% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 162.78 | #6 of 213, top 3% |  | [Epoch AI](https://epoch.ai/eci) | 2026-07-24 |

### Math

Claude Opus 5 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 85.6% | #11 of 81, top 14% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-24 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 73.2% | #11 of 63, top 18% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-24 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 97.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 93.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 98.9% | #16 of 173, top 10% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-24 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 99% | #4 of 77, top 6% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1530 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1531 | #2 of 285, top 1% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

Claude Opus 5 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 92.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 87.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 93.9% | #13 of 186, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-24 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 59.9% | #16 of 77, top 21% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1552 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1557 | Best of 273 | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Claude Opus 5 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1319 | #3 of 122, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 30.4% | #16 of 31, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 60.8% | #6 of 31, top 20% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1516 | Best of 38 | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Claude Opus 5 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1501 | #4 of 297, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1496 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1574 | #4 of 285, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1568 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1511 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1519 | #3 of 223, top 2% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1524 | Best of 231 |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1502 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1516 | #2 of 211, top 1% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1515 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1521 | #2 of 213, top 1% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1514 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1507 | #6 of 283, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1505 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1519 | Best of 226 |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1505 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Claude Opus 5 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1517 | #4 of 298, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1516 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Claude Opus 5 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1515 | #5 of 291, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1515 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Claude Opus 5 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1507 | #4 of 297, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1503 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1491 | #7 of 295, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1486 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 2133 | #3 of 115, top 3% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1385 | Best of 28 |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1499 | #7 of 295, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1492 |  | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Claude Opus 5 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [anthropic](https://docs.anthropic.com/en/docs/about-claude/models) | $5 | $25 | $0.50 | 2026-10-10 |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $5 | $25 | $0.50 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $5 | $25 | $0.50 | 2026-10-10 |
| [openrouter](https://openrouter.ai/anthropic/claude-opus-5) | $5 | $25 | $0.50 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude) | $5 | $25 | $0.50 | 2026-10-10 |

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

## Compare Claude Opus 5

-   [Claude Opus 5 vs Claude Opus 4.8](https://noometry.com/compare/claude-opus-4-8-vs-claude-opus-5)
-   [Claude Opus 5 vs Claude Opus 5.5](https://noometry.com/compare/claude-opus-5-vs-claude-opus-5-5)
-   [Claude Opus 5 vs Claude Fable 5](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5)
-   [Claude Opus 5 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-claude-opus-5)
-   [Claude Opus 5 vs GPT-6.1 Sol](https://noometry.com/compare/claude-opus-5-vs-gpt-6-1-sol)
-   [Claude Opus 5 vs GPT-6 Astra](https://noometry.com/compare/claude-opus-5-vs-gpt-6-astra)
-   [Claude Opus 5 vs GPT-5.6 Sol](https://noometry.com/compare/claude-opus-5-vs-gpt-5-6-sol)
-   [Claude Opus 5 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-opus-5-vs-gemini-3-8-flash)
-   [Claude Opus 5 vs Kimi K3](https://noometry.com/compare/claude-opus-5-vs-kimi-k3)
-   [Claude Opus 5 vs Grok 4.6](https://noometry.com/compare/claude-opus-5-vs-grok-4-6)
-   [Claude Opus 5 vs Qwen3.8 Max](https://noometry.com/compare/claude-opus-5-vs-qwen3-8-max)
-   [Claude Opus 5 vs GLM-5.3](https://noometry.com/compare/claude-opus-5-vs-glm-5-3)
-   [Claude Opus 5 vs Muse Spark 1.3](https://noometry.com/compare/claude-opus-5-vs-muse-spark-1-3)
-   [Claude Opus 5 vs DeepSeek V4 Pro](https://noometry.com/compare/claude-opus-5-vs-deepseek-v4-pro)

## Other Anthropic models

-   [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1)69.0
-   [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5)68.6
-   [Claude Fable 5](https://noometry.com/models/claude-fable-5)66.8
-   [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5)61.9
-   [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8)60.7
-   [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7)58.3
-   [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6)58.2
-   [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5)54.6

## Frequently asked questions

### How good is Claude Opus 5?

Claude Opus 5 by Anthropic ranks 4th of 354 ranked models on the Noometry Index as of October 2026, with a score of 67.8. Its strongest category is agentic & tool use, where it ranks 1st. API pricing starts at $5 per million input tokens and $25 per million output tokens, with a 1M-token context window.

### How much does Claude Opus 5 cost?

Claude Opus 5 costs $5 per million input tokens and $25 per million output tokens on Anthropic's own API, with cached input at $0.50.

### What is Claude Opus 5's context window?

Claude Opus 5 accepts up to 1M tokens of input and can write up to 128K tokens in one response.

### Is Claude Opus 5 open source?

No. Claude Opus 5 is proprietary and available only through Anthropic's API and partner platforms.

### What are Claude Opus 5's strengths and weaknesses?

Relative to other ranked models, Claude Opus 5 places best in writing & preference, agentic & tool use, reasoning and lowest in long context, multimodal, knowledge.

### What is Claude Opus 5 best at?

Its best category is agentic & tool use, where it ranks 1st on Noometry.

### Cite this page

Noometry. (2026). Claude Opus 5 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/claude-opus-5

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