Anthropic, proprietary

# Claude Opus 4.8

> Claude Opus 4.8 by Anthropic, released May 2026. Ranked #13 of 354 with a Noometry Index of 60.7. API: $5 in / $25 out per M tokens. 1M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/claude-opus-4-8
- Last updated: 2026-10-10
- Title: Claude Opus 4.8 Benchmarks, Price & Rank (October 2026)

Claude Opus 4.8 by Anthropic ranks 13th of 354 ranked models on the Noometry Index as of October 2026, with a score of 60.7. Its strongest category is agentic & tool use, where it ranks 11th. 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:** #13 of 354
- **Index score:** 60.7
- **Evidence:** Confirmed 65 results
- **Provider:** [Anthropic](https://noometry.com/providers/anthropic)
- **Released:** May 28, 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:** 34 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #201 of 219
- **Knowledge cutoff:** January 2026
- **Input:** text, image, pdf

## Category scores

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

Claude Opus 4.8 category scores

1.  Coding 59.9
2.  Agentic & Tool Use 47.6
3.  Reasoning 64.7
4.  Math 78.4
5.  Knowledge 61.3
6.  Multimodal 42.9
7.  Multilingual 55.2
8.  Instruction Following 77.4
9.  Long Context 45.4
10.  Writing & Preference 72.0
11.  304050607080

Claude Opus 4.8 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 59.9 | #12 | 7 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 47.6 | #11 | 8 |
| [Reasoning](https://noometry.com/best/reasoning) | 64.7 | #16 | 14 |
| [Math](https://noometry.com/best/math) | 78.4 | #13 | 6 |
| [Knowledge](https://noometry.com/best/knowledge) | 61.3 | #29 | 3 |
| [Multimodal](https://noometry.com/best/multimodal) | 42.9 | #26 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 55.2 | #33 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.4 | #24 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | #35 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 72.0 | #16 | 5 |

## Strengths and weaknesses

Categories where Claude Opus 4.8 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Claude Opus 4.8: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 59.9 | +21.2 | #12 of 340, top 4% |
| [Math](https://noometry.com/best/math) | 78.4 | +41.8 | #13 of 327, top 4% |
| [Reasoning](https://noometry.com/best/reasoning) | 64.7 | +41.1 | #16 of 350, top 5% |

### Weakest categories

Claude Opus 4.8: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 42.9 | +4.4 | #26 of 128, top 21% |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | +4.5 | #35 of 296, top 12% |
| [Multilingual](https://noometry.com/best/multilingual) | 55.2 | +7.8 | #33 of 297, top 12% |

## Closest competitors

The models ranked just above and below Claude Opus 4.8. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Claude Opus 4.8
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [GPT-5.5](https://noometry.com/models/gpt-5-5) | #9 | 63.4 | $11.25 | 25 | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gpt-5-5) |
| [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5) | #10 | 61.9 | $4 | — | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-claude-sonnet-5-5) |
| [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) | #11 | 61.8 | $1.50 | — | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gemini-3-8-flash) |
| [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | #12 | 61.8 | $4 | — | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gpt-6-sol) |
| [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) | #14 | 59.8 | $1.50 | — | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gemini-3-7-flash) |
| [Kimi K3](https://noometry.com/models/kimi-k3) | #15 | 59.5 | $6 | — | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-kimi-k3) |
| [GPT-5.4](https://noometry.com/models/gpt-5-4) | #16 | 59.4 | $5.63 | 12 | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gpt-5-4) |
| [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | #17 | 59.2 | $4.50 | 11 | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gpt-5-6-terra) |

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 4.8 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 51.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 40.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 59% | #17 of 29, top 59% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 48.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 54.4% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 46.5% | #12 of 37, top 33% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1556 | #32 of 113, top 29% | high | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 53.5% | #31 of 121, top 26% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 47.1% | #5 of 31, top 17% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 76% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 70.5% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 82.9% | #8 of 119, top 7% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1490 | #30 of 294, top 11% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,564 | #15 of 105, top 15% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,412 |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Claude Opus 4.8 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 48.9% | #27 of 49, top 56% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 20.6% | #3 of 9, top 34% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index) | 8.3% | #4 of 14, top 29% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 39.7% | #9 of 26, top 35% | max | [τ²-bench](https://taubench.com/) | 2026-08-04 |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 50.2% | #6 of 24, top 25% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 49.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 47.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 33.8% | #4 of 11, top 37% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 32.9% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GBAEval](https://noometry.com/benchmarks/gbaeval) | 70.9% | #3 of 23, top 14% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 24% | #11 of 36, top 31% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1204 | #12 of 32, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 5,787 | #21 of 60, top 35% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 2,992 |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Claude Opus 4.8 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 72.1% | #19 of 83, top 23% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 62.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 71.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 64.8% | #15 of 77, top 20% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 88.8% | #2 of 99, top 3% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 91.1% | #16 of 91, top 18% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 92% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 88% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 92.5% | #21 of 83, top 26% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 91.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 20.9% | #20 of 134, top 15% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 29% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 34% | #30 of 129, top 24% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-05-29 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 13% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 23.5% | #6 of 38, top 16% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 28.6% | #11 of 24, top 46% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1482 | #31 of 297, top 11% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 36% | #17 of 74, top 23% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-25 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 31% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-26 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 94.9% | #18 of 151, top 12% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 57.5% | #8 of 125, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 87.5% | #5 of 25, top 20% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 68.1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.14 | #6 of 10, top 60% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.13 |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 158.21 | #14 of 213, top 7% |  | [Epoch AI](https://epoch.ai/eci) | 2026-05-28 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 59.1 |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 59.9 | #34 of 72, top 48% | 24K | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Claude Opus 4.8 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 80% | #15 of 81, top 19% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-10 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 56.1% | #16 of 63, top 26% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-10 |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 91.8% | #2 of 29, top 7% | max | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 97.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 98.3% | #20 of 173, top 12% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 84.4% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 69% | #16 of 77, top 21% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1487 | #22 of 285, top 8% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 47.2% | #5 of 68, top 8% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-08 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 31.3% | #6 of 55, top 11% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-08 |

### Knowledge

Claude Opus 4.8 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 88.4% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 91% | #27 of 186, top 15% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-07 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 85.4% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 53% | #20 of 77, top 26% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1502 | #23 of 273, top 9% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Claude Opus 4.8 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1294 | #20 of 122, top 17% | high | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 14.5% | #24 of 31, top 78% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 42.5% | #13 of 31, top 42% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1475 | #9 of 38, top 24% | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Claude Opus 4.8 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1450 | #33 of 297, top 12% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1507 | #42 of 285, top 15% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1481 | #26 of 223, top 12% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1472 | #24 of 231, top 11% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1440 | #29 of 211, top 14% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1432 | #28 of 213, top 14% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1474 | #23 of 283, top 9% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1466 | #29 of 226, top 13% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Claude Opus 4.8 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1476 | #21 of 298, top 8% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Claude Opus 4.8 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1483 | #16 of 291, top 6% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Claude Opus 4.8 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1461 | #34 of 297, top 12% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1454 | #24 of 295, top 9% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1840 | #19 of 115, top 17% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1281 | #6 of 28, top 22% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1476 | #25 of 295, top 9% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Claude Opus 4.8 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-4.8) | $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 4.8

-   [Claude Opus 4.8 vs Claude Opus 4.7](https://noometry.com/compare/claude-opus-4-7-vs-claude-opus-4-8)
-   [Claude Opus 4.8 vs GPT-6 Sol](https://noometry.com/compare/claude-opus-4-8-vs-gpt-6-sol)
-   [Claude Opus 4.8 vs Gemini 3.7 Flash](https://noometry.com/compare/claude-opus-4-8-vs-gemini-3-7-flash)
-   [Claude Opus 4.8 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-opus-4-8-vs-gemini-3-8-flash)
-   [Claude Opus 4.8 vs Kimi K3](https://noometry.com/compare/claude-opus-4-8-vs-kimi-k3)
-   [Claude Opus 4.8 vs Claude Sonnet 5.5](https://noometry.com/compare/claude-opus-4-8-vs-claude-sonnet-5-5)
-   [Claude Opus 4.8 vs GPT-5.4](https://noometry.com/compare/claude-opus-4-8-vs-gpt-5-4)
-   [Claude Opus 4.8 vs GPT-6 Astra](https://noometry.com/compare/claude-opus-4-8-vs-gpt-6-astra)
-   [Claude Opus 4.8 vs Grok 4.6](https://noometry.com/compare/claude-opus-4-8-vs-grok-4-6)
-   [Claude Opus 4.8 vs Qwen3.8 Max](https://noometry.com/compare/claude-opus-4-8-vs-qwen3-8-max)
-   [Claude Opus 4.8 vs GLM-5.3](https://noometry.com/compare/claude-opus-4-8-vs-glm-5-3)
-   [Claude Opus 4.8 vs Muse Spark 1.3](https://noometry.com/compare/claude-opus-4-8-vs-muse-spark-1-3)
-   [Claude Opus 4.8 vs DeepSeek V4 Pro](https://noometry.com/compare/claude-opus-4-8-vs-deepseek-v4-pro)
-   [Claude Opus 4.8 vs MiMo-V2.6-Pro](https://noometry.com/compare/claude-opus-4-8-vs-mimo-v2-6-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 Opus 5](https://noometry.com/models/claude-opus-5)67.8
-   [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 4.7](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 4.8?

Claude Opus 4.8 by Anthropic ranks 13th of 354 ranked models on the Noometry Index as of October 2026, with a score of 60.7. Its strongest category is agentic & tool use, where it ranks 11th. 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 4.8 cost?

Claude Opus 4.8 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 4.8's context window?

Claude Opus 4.8 accepts up to 1M tokens of input and can write up to 128K tokens in one response.

### Is Claude Opus 4.8 open source?

No. Claude Opus 4.8 is proprietary and available only through Anthropic's API and partner platforms.

### How fast is Claude Opus 4.8?

Claude Opus 4.8 generated about 34 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Claude Opus 4.8's strengths and weaknesses?

Relative to other ranked models, Claude Opus 4.8 places best in coding, math, reasoning and lowest in multimodal, long context, multilingual.

### What is Claude Opus 4.8 best at?

Its best category is agentic & tool use, where it ranks 11th on Noometry.

### Cite this page

Noometry. (2026). Claude Opus 4.8 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/claude-opus-4-8

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/claude-opus-4-8.md).