Anthropic, proprietary

# Claude Opus 4.7

> Claude Opus 4.7 by Anthropic, released April 2026. Ranked #19 of 354 with a Noometry Index of 58.3. API: $5 in / $25 out per M tokens. 1M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/claude-opus-4-7
- Last updated: 2026-10-10
- Title: Claude Opus 4.7 Benchmarks, Price & Rank (October 2026)

Claude Opus 4.7 by Anthropic ranks 19th of 354 ranked models on the Noometry Index as of October 2026, with a score of 58.3. Its strongest category is writing & preference, where it ranks 8th. 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:** #19 of 354
- **Index score:** 58.3
- **Evidence:** Confirmed 66 results
- **Provider:** [Anthropic](https://noometry.com/providers/anthropic)
- **Released:** April 14, 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:** 33 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #202 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 4.7 category scores

1.  Coding 59.6
2.  Agentic & Tool Use 47.9
3.  Reasoning 53.8
4.  Math 66.7
5.  Knowledge 62.6
6.  Multimodal 41.2
7.  Multilingual 57.3
8.  Instruction Following 78.4
9.  Long Context 46.2
10.  Writing & Preference 75.1
11.  304050607080

Claude Opus 4.7 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 59.6 | #13 | 8 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 47.9 | #10 | 8 |
| [Reasoning](https://noometry.com/best/reasoning) | 53.8 | #29 | 13 |
| [Math](https://noometry.com/best/math) | 66.7 | #26 | 6 |
| [Knowledge](https://noometry.com/best/knowledge) | 62.6 | #23 | 5 |
| [Multimodal](https://noometry.com/best/multimodal) | 41.2 | #38 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 57.3 | #10 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 78.4 | #10 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 46.2 | #25 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 75.1 | #8 | 5 |

## Strengths and weaknesses

Categories where Claude Opus 4.7 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Claude Opus 4.7: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Writing & Preference](https://noometry.com/best/writing) | 75.1 | +21.3 | #8 of 312, top 3% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 78.4 | +7.1 | #10 of 305, top 4% |
| [Multilingual](https://noometry.com/best/multilingual) | 57.3 | +9.9 | #10 of 297, top 4% |

### Weakest categories

Claude Opus 4.7: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 41.2 | +2.7 | #38 of 128, top 30% |
| [Long Context](https://noometry.com/best/long-context) | 46.2 | +5.2 | #25 of 296, top 9% |
| [Reasoning](https://noometry.com/best/reasoning) | 53.8 | +30.2 | #29 of 350, top 9% |

## Closest competitors

The models ranked just above and below Claude Opus 4.7. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Claude Opus 4.7
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Kimi K3](https://noometry.com/models/kimi-k3) | #15 | 59.5 | $6 | — | [Compare](https://noometry.com/compare/claude-opus-4-7-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-4-7-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-4-7-vs-gpt-5-6-terra) |
| [GPT-5.4 Pro](https://noometry.com/models/gpt-5-4-pro) | #18 | 58.9 | $67.50 | — | [Compare](https://noometry.com/compare/claude-opus-4-7-vs-gpt-5-4-pro) |
| [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | #20 | 58.2 | $10 | 19 | [Compare](https://noometry.com/compare/claude-opus-4-6-vs-claude-opus-4-7) |
| [Grok 4.6](https://noometry.com/models/grok-4-6) | #21 | 56.9 | $3 | — | [Compare](https://noometry.com/compare/claude-opus-4-7-vs-grok-4-6) |
| [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) | #22 | 56.8 | $3 | — | [Compare](https://noometry.com/compare/claude-opus-4-7-vs-qwen3-8-max) |
| [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | #23 | 56.7 | $4.50 | — | [Compare](https://noometry.com/compare/claude-opus-4-7-vs-gemini-3-1-pro-preview) |

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 4.7 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 83.5% | Best of 32 | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-20 |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 38.5% | #23 of 37, top 63% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1558 | #31 of 113, top 28% | high | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 54.5% | #27 of 121, top 23% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 44.1% | #6 of 31, top 20% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 44.1% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 76.4% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 76.4% | #14 of 119, top 12% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 75.5% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1518 | #8 of 294, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MirrorCode](https://noometry.com/benchmarks/mirrorcode) | 31.1% | #5 of 9, top 56% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-12 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,323 | #22 of 105, top 21% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Claude Opus 4.7 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 80.2% | #3 of 41, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 49.2% | #26 of 49, top 54% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2) | 18.2% | #4 of 9, top 45% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 40.2% | #7 of 26, top 27% | max | [τ²-bench](https://taubench.com/) | 2026-05-05 |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 28.6% | #7 of 11, top 64% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ExploitBench](https://noometry.com/benchmarks/exploitbench) | 26.5% | #3 of 9, top 34% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GBAEval](https://noometry.com/benchmarks/gbaeval) | 43.8% | #12 of 23, top 53% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 21% | #19 of 36, top 53% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1233 | #4 of 32, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 10,937 | #5 of 60, top 9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Claude Opus 4.7 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 68.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 62.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 75.8% | #17 of 83, top 21% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 67.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 61.7% | #19 of 77, top 25% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 80.7% | #6 of 99, top 7% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 73.3% |  |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 39% | #68 of 91, top 75% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 93.5% | #20 of 83, top 25% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 91% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 92% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 91% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 12% | #43 of 134, top 33% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 20% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-14 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 7% |  | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 30% | #35 of 129, top 28% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-20 |
| [Thematic Generalization](https://noometry.com/benchmarks/thematic-generalization) | 72.8% | #5 of 23, top 22% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/generalization) |  |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 19% | #14 of 24, top 59% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-30 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1506 | #9 of 297, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 13% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 28% | #30 of 74, top 41% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-25 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 94.7% | #21 of 151, top 14% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 52.2% | #18 of 125, top 15% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 156.25 | #25 of 213, top 12% |  | [Epoch AI](https://epoch.ai/eci) | 2026-04-16 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 60.3 | #27 of 72, top 38% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Claude Opus 4.7 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 70.2% | #24 of 81, top 30% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-10 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 31.7% | #26 of 63, top 42% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-10 |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 73.6% | #10 of 29, top 35% | xhigh | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 86.7% |  | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 97.8% | #24 of 173, top 14% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-17 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 54% | #25 of 77, top 33% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1499 | #14 of 285, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 43.8% | #6 of 68, top 9% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-17 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 22.9% | #8 of 55, top 15% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-17 |

### Knowledge

Claude Opus 4.7 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 86.4% |  | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 90.2% | #37 of 186, top 20% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-04-17 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 36.2% | #9 of 41, top 22% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 51.7% | #23 of 77, top 30% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 12% | #73 of 96, top 77% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1521 | #11 of 273, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Claude Opus 4.7 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1316 | #5 of 122, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 24.5% | #22 of 31, top 71% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 33.3% | #21 of 31, top 68% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1495 | #5 of 38, top 14% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Claude Opus 4.7 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1480 | #10 of 297, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1531 | #12 of 285, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1503 | #10 of 223, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1495 | #10 of 231, top 5% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1472 | #17 of 211, top 9% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1464 | #8 of 213, top 4% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1494 | #10 of 283, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1495 | #9 of 226, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Claude Opus 4.7 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1498 | #7 of 298, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Claude Opus 4.7 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1505 | #8 of 291, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Claude Opus 4.7 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1490 | #9 of 297, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1486 | #9 of 295, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1914 | #13 of 115, top 12% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1311 | #5 of 28, top 18% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1505 | #3 of 295, top 2% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Claude Opus 4.7 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-4.7) | $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 4.7

-   [Claude Opus 4.7 vs Claude Opus 4.6](https://noometry.com/compare/claude-opus-4-6-vs-claude-opus-4-7)
-   [Claude Opus 4.7 vs GPT-5.4 Pro](https://noometry.com/compare/claude-opus-4-7-vs-gpt-5-4-pro)
-   [Claude Opus 4.7 vs GPT-5.6 Terra](https://noometry.com/compare/claude-opus-4-7-vs-gpt-5-6-terra)
-   [Claude Opus 4.7 vs Grok 4.6](https://noometry.com/compare/claude-opus-4-7-vs-grok-4-6)
-   [Claude Opus 4.7 vs GPT-5.4](https://noometry.com/compare/claude-opus-4-7-vs-gpt-5-4)
-   [Claude Opus 4.7 vs Qwen3.8 Max](https://noometry.com/compare/claude-opus-4-7-vs-qwen3-8-max)
-   [Claude Opus 4.7 vs GPT-6 Astra](https://noometry.com/compare/claude-opus-4-7-vs-gpt-6-astra)
-   [Claude Opus 4.7 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-opus-4-7-vs-gemini-3-8-flash)
-   [Claude Opus 4.7 vs Kimi K3](https://noometry.com/compare/claude-opus-4-7-vs-kimi-k3)
-   [Claude Opus 4.7 vs GLM-5.3](https://noometry.com/compare/claude-opus-4-7-vs-glm-5-3)
-   [Claude Opus 4.7 vs Muse Spark 1.3](https://noometry.com/compare/claude-opus-4-7-vs-muse-spark-1-3)
-   [Claude Opus 4.7 vs DeepSeek V4 Pro](https://noometry.com/compare/claude-opus-4-7-vs-deepseek-v4-pro)
-   [Claude Opus 4.7 vs MiMo-V2.6-Pro](https://noometry.com/compare/claude-opus-4-7-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 4.8](https://noometry.com/models/claude-opus-4-8)60.7
-   [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 4.7?

Claude Opus 4.7 by Anthropic ranks 19th of 354 ranked models on the Noometry Index as of October 2026, with a score of 58.3. Its strongest category is writing & preference, where it ranks 8th. 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 4.7 cost?

Claude Opus 4.7 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 4.7's context window?

Claude Opus 4.7 accepts up to 1M tokens of input and can write up to 128K tokens in one response.

### Is Claude Opus 4.7 open source?

No. Claude Opus 4.7 is proprietary and available only through Anthropic's API and partner platforms.

### How fast is Claude Opus 4.7?

Claude Opus 4.7 generated about 33 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Claude Opus 4.7's strengths and weaknesses?

Relative to other ranked models, Claude Opus 4.7 places best in writing & preference, instruction following, multilingual and lowest in multimodal, long context, reasoning.

### What is Claude Opus 4.7 best at?

Its best category is writing & preference, where it ranks 8th on Noometry.

### Cite this page

Noometry. (2026). Claude Opus 4.7 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/claude-opus-4-7

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/claude-opus-4-7.md).