Anthropic, proprietary

# Claude Opus 4.5

> Claude Opus 4.5 by Anthropic, released November 2025. Ranked #47 of 354 with a Noometry Index of 50.5. API: $5 in / $25 out per M tokens. 200K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/claude-opus-4-5
- Last updated: 2026-10-10
- Title: Claude Opus 4.5 Benchmarks, Price & Rank (October 2026)

Claude Opus 4.5 by Anthropic ranks 47th of 354 ranked models on the Noometry Index as of October 2026, with a score of 50.5. Its strongest category is agentic & tool use, where it ranks 12th. API pricing starts at $5 per million input tokens and $25 per million output tokens, with a 200K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #47 of 354
- **Index score:** 50.5
- **Evidence:** Confirmed 69 results
- **Provider:** [Anthropic](https://noometry.com/providers/anthropic)
- **Released:** November 1, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 200K
- **Max output:** 64K
- **Input price:** $5 / M
- **Output price:** $25 / M
- **Blended price:** $10 / M
- **Output speed:** 13 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #205 of 219
- **Knowledge cutoff:** May 2025
- **Input:** text, image, pdf

## Category scores

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

Claude Opus 4.5 category scores

1.  Coding 54.8
2.  Agentic & Tool Use 47.3
3.  Reasoning 42.6
4.  Math 38.6
5.  Knowledge 56.5
6.  Multimodal 31.4
7.  Multilingual 54.3
8.  Instruction Following 77.5
9.  Long Context 46.5
10.  Writing & Preference 68.1
11.  020406080

Claude Opus 4.5 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 54.8 | #27 | 7 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 47.3 | #12 | 12 |
| [Reasoning](https://noometry.com/best/reasoning) | 42.6 | #51 | 12 |
| [Math](https://noometry.com/best/math) | 38.6 | #132 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 56.5 | #44 | 5 |
| [Multimodal](https://noometry.com/best/multimodal) | 31.4 | #107 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 54.3 | #47 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.5 | #19 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 46.5 | #22 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 68.1 | #28 | 4 |

## Strengths and weaknesses

Categories where Claude Opus 4.5 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Claude Opus 4.5: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.5 | +6.3 | #19 of 305, top 7% |
| [Long Context](https://noometry.com/best/long-context) | 46.5 | +5.5 | #22 of 296, top 8% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 47.3 | +17.0 | #12 of 154, top 8% |

### Weakest categories

Claude Opus 4.5: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 31.4 | −7.1 | #107 of 128, top 84% |
| [Math](https://noometry.com/best/math) | 38.6 | +2.1 | #132 of 327, top 41% |
| [Multilingual](https://noometry.com/best/multilingual) | 54.3 | +6.9 | #47 of 297, top 16% |

## Closest competitors

The models ranked just above and below Claude Opus 4.5. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Claude Opus 4.5
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Qwen3.6 Max Preview](https://noometry.com/models/qwen3-6-max-preview) | #43 | 51.5 | $2.92 | — | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-qwen3-6-max-preview) |
| [GLM-5.2](https://noometry.com/models/glm-5-2) | #44 | 51.1 | $2.15 | 23 | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-glm-5-2) |
| [GPT-5](https://noometry.com/models/gpt-5) | #45 | 50.9 | $3.44 | 2 | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-gpt-5) |
| [Muse Spark](https://noometry.com/models/muse-spark) | #46 | 50.6 | — | — | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-muse-spark) |
| [Muse Spark 1.2](https://noometry.com/models/muse-spark-1-2) | #48 | 50.3 | $2 | — | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-muse-spark-1-2) |
| [MiMo-V2.6-Pro](https://noometry.com/models/mimo-v2-6-pro) | #49 | 50.3 | $0.54 | — | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-mimo-v2-6-pro) |
| [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | #50 | 50.3 | $6 | — | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-claude-sonnet-4-6) |
| [Muse Spark 1.1](https://noometry.com/models/muse-spark-1-1) | #51 | 49.9 | $2 | — | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-muse-spark-1-1) |

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 4.5 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 76.7% | #9 of 32, top 29% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-05 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 76.8% | Best of 39 | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 74.4% |  | medium | [SWE-bench](https://www.swebench.com/) | 2025-11-24 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1494 | #49 of 113, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1469 |  |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual) | 70.7% | #3 of 13, top 24% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-13 |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 26.5% | #12 of 31, top 39% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 63.7% | #24 of 119, top 21% | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1499 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1504 | #16 of 294, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,025 | #41 of 105, top 40% | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [AlgoTune](https://noometry.com/benchmarks/algotune) | 1.77 | #5 of 18, top 28% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Claude Opus 4.5 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 63.1% | #11 of 41, top 27% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 59.1% |  | 128K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 77.5% | Best of 49 | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [GDPval](https://noometry.com/benchmarks/gdpval) | 45.5% | #2 of 11, top 19% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index) | 3.8% | #9 of 14, top 65% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Airline](https://noometry.com/benchmarks/tau2-airline) | 84% | Best of 7 | high | [τ²-bench](https://taubench.com/) | 2026-02-26 |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 24.7% | #19 of 26, top 74% | high | [τ²-bench](https://taubench.com/) | 2026-02-26 |
| [τ²-bench Retail](https://noometry.com/benchmarks/tau2-retail) | 79.6% | #3 of 7, top 43% | high | [τ²-bench](https://taubench.com/) | 2026-02-26 |
| [τ²-bench Telecom](https://noometry.com/benchmarks/tau2-telecom) | 92.3% | #2 of 7, top 29% | high | [τ²-bench](https://taubench.com/) | 2026-02-26 |
| [Cybench](https://noometry.com/benchmarks/cybench) | 82% | #2 of 21, top 10% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 54.8% | #3 of 24, top 13% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 53.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld](https://noometry.com/benchmarks/osworld) | 66.3% | #2 of 8, top 25% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 43.5% | #10 of 35, top 29% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 43% |  | 64K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1180 | #19 of 32, top 60% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 73% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 75% | #5 of 32, top 16% | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 4,967 | #31 of 60, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Claude Opus 4.5 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 7.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 22.8% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 37.6% | #37 of 83, top 45% | 64K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 13.9% |  | 8K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 62% | #18 of 77, top 24% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 80.2% | #7 of 99, top 8% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 70.7% |  |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 52.5% | #61 of 91, top 68% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 49.4% |  | no reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 40% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 72% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 75.8% |  | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 80% | #38 of 83, top 46% | 64K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 58.7% |  | 8K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 4% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 12% | #75 of 129, top 59% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 11.9% | #11 of 38, top 29% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 14.3% | #15 of 24, top 63% | 128K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-25 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1476 | #35 of 297, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1473 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 22% | #37 of 74, top 50% | 48K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-25 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 89.9% | #41 of 151, top 28% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 44.5% | #36 of 125, top 29% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 150.09 | #52 of 213, top 25% |  | [Epoch AI](https://epoch.ai/eci) | 2025-11-24 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 60.7 | #23 of 72, top 32% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Claude Opus 4.5 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 34.4% | #57 of 81, top 71% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 4.9% | #54 of 63, top 86% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 48.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-24 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 81.7% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-24 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 86.1% | #68 of 173, top 40% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-24 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 36% | #37 of 77, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1458 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1463 | #50 of 285, top 18% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 20.7% | #30 of 68, top 45% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 20.3% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 20.7% |  | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 4.2% | #29 of 55, top 53% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 2.1% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 4.2% |  | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |

### Knowledge

Claude Opus 4.5 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 80.7% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-24 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 85.5% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-25 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 86% | #60 of 186, top 33% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-24 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 25.2% | #14 of 41, top 35% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 45.7% | #35 of 77, top 46% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 10.9% | #65 of 96, top 68% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1487 | #35 of 273, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1481 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Claude Opus 4.5 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GeoBench](https://noometry.com/benchmarks/geobench) | 75% | #9 of 25, top 36% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 40% | #12 of 24, top 50% | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 28.3% | #23 of 31, top 75% | 64K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1462 | #17 of 38, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Claude Opus 4.5 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1438 | #47 of 297, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1435 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1470 | #72 of 285, top 26% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1466 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1457 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1471 | #38 of 223, top 18% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1449 | #45 of 231, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1440 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1413 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1416 | #42 of 211, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1424 | #34 of 213, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1374 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1447 | #47 of 283, top 17% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1437 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1458 | #37 of 226, top 17% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1453 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Claude Opus 4.5 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1478 | #16 of 298, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1473 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Claude Opus 4.5 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 21.1% | #4 of 19, top 22% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1478 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1480 | #24 of 291, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Claude Opus 4.5 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1448 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1451 | #42 of 297, top 15% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1445 | #32 of 295, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1443 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1687 | #33 of 115, top 29% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1461 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1466 | #34 of 295, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Claude Opus 4.5 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-4.5) | $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 4.5

-   [Claude Opus 4.5 vs Claude Opus 4.1](https://noometry.com/compare/claude-opus-4-1-vs-claude-opus-4-5)
-   [Claude Opus 4.5 vs Muse Spark](https://noometry.com/compare/claude-opus-4-5-vs-muse-spark)
-   [Claude Opus 4.5 vs Muse Spark 1.2](https://noometry.com/compare/claude-opus-4-5-vs-muse-spark-1-2)
-   [Claude Opus 4.5 vs GPT-5](https://noometry.com/compare/claude-opus-4-5-vs-gpt-5)
-   [Claude Opus 4.5 vs MiMo-V2.6-Pro](https://noometry.com/compare/claude-opus-4-5-vs-mimo-v2-6-pro)
-   [Claude Opus 4.5 vs GLM-5.2](https://noometry.com/compare/claude-opus-4-5-vs-glm-5-2)
-   [Claude Opus 4.5 vs Claude Sonnet 4.6](https://noometry.com/compare/claude-opus-4-5-vs-claude-sonnet-4-6)
-   [Claude Opus 4.5 vs GPT-6 Astra](https://noometry.com/compare/claude-opus-4-5-vs-gpt-6-astra)
-   [Claude Opus 4.5 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-opus-4-5-vs-gemini-3-8-flash)
-   [Claude Opus 4.5 vs Kimi K3](https://noometry.com/compare/claude-opus-4-5-vs-kimi-k3)
-   [Claude Opus 4.5 vs Grok 4.6](https://noometry.com/compare/claude-opus-4-5-vs-grok-4-6)
-   [Claude Opus 4.5 vs Qwen3.8 Max](https://noometry.com/compare/claude-opus-4-5-vs-qwen3-8-max)
-   [Claude Opus 4.5 vs GLM-5.3](https://noometry.com/compare/claude-opus-4-5-vs-glm-5-3)
-   [Claude Opus 4.5 vs Muse Spark 1.3](https://noometry.com/compare/claude-opus-4-5-vs-muse-spark-1-3)

## 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 4.8](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

## Frequently asked questions

### How good is Claude Opus 4.5?

Claude Opus 4.5 by Anthropic ranks 47th of 354 ranked models on the Noometry Index as of October 2026, with a score of 50.5. Its strongest category is agentic & tool use, where it ranks 12th. API pricing starts at $5 per million input tokens and $25 per million output tokens, with a 200K-token context window.

### How much does Claude Opus 4.5 cost?

Claude Opus 4.5 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 4.5's context window?

Claude Opus 4.5 accepts up to 200K tokens of input and can write up to 64K tokens in one response.

### Is Claude Opus 4.5 open source?

No. Claude Opus 4.5 is proprietary and available only through Anthropic's API and partner platforms.

### How fast is Claude Opus 4.5?

Claude Opus 4.5 generated about 13 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Claude Opus 4.5's strengths and weaknesses?

Relative to other ranked models, Claude Opus 4.5 places best in instruction following, long context, agentic & tool use and lowest in multimodal, math, multilingual.

### What is Claude Opus 4.5 best at?

Its best category is agentic & tool use, where it ranks 12th on Noometry.

### Cite this page

Noometry. (2026). Claude Opus 4.5 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/claude-opus-4-5

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/claude-opus-4-5.md).