Anthropic, proprietary

# Claude Fable 5

> Claude Fable 5 by Anthropic, released June 2026. Ranked #5 of 354 with a Noometry Index of 66.8. API: $10 in / $50 out per M tokens. 1M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/claude-fable-5
- Last updated: 2026-10-10
- Title: Claude Fable 5 Benchmarks, Price & Rank (October 2026)

Claude Fable 5 by Anthropic ranks 5th of 354 ranked models on the Noometry Index as of October 2026, with a score of 66.8. Its strongest category is agentic & tool use, where it ranks 2nd. API pricing starts at $10 per million input tokens and $50 per million output tokens, with a 1M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #5 of 354
- **Index score:** 66.8
- **Evidence:** Confirmed 62 results
- **Provider:** [Anthropic](https://noometry.com/providers/anthropic)
- **Released:** June 7, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1M
- **Max output:** 128K
- **Input price:** $10 / M
- **Output price:** $50 / M
- **Blended price:** $20 / M
- **Output speed:** 25 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #208 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 Fable 5 category scores

1.  Coding 70.6
2.  Agentic & Tool Use 54.0
3.  Reasoning 76.8
4.  Math 88.5
5.  Knowledge 62.2
6.  Multimodal 45.3
7.  Multilingual 57.3
8.  Instruction Following 78.6
9.  Long Context 46.3
10.  Writing & Preference 75.9
11.  20406080100

Claude Fable 5 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 70.6 | #4 | 9 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 54.0 | #2 | 6 |
| [Reasoning](https://noometry.com/best/reasoning) | 76.8 | #6 | 14 |
| [Math](https://noometry.com/best/math) | 88.5 | #5 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 62.2 | #25 | 3 |
| [Multimodal](https://noometry.com/best/multimodal) | 45.3 | #17 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 57.3 | #9 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 78.6 | #8 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 46.3 | #23 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 75.9 | #5 | 5 |

## Strengths and weaknesses

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

### Strongest categories

Claude Fable 5: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 70.6 | +31.9 | #4 of 340, top 2% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 54.0 | +23.7 | #2 of 154, top 2% |
| [Math](https://noometry.com/best/math) | 88.5 | +51.9 | #5 of 327, top 2% |

### Weakest categories

Claude Fable 5: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 45.3 | +6.7 | #17 of 128, top 14% |
| [Knowledge](https://noometry.com/best/knowledge) | 62.2 | +24.9 | #25 of 314, top 8% |
| [Long Context](https://noometry.com/best/long-context) | 46.3 | +5.4 | #23 of 296, top 8% |

## Closest competitors

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

Models ranked closest to Claude Fable 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-fable-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-vs-claude-fable-5-1) |
| [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | #3 | 68.6 | $8 | — | [Compare](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5-5) |
| [Claude Opus 5](https://noometry.com/models/claude-opus-5) | #4 | 67.8 | $10 | — | [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-fable-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-fable-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-fable-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-fable-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 Fable 5 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 68.6% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 59.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 69.7% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 65.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 69.9% | #6 of 29, top 21% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 53.5% | #2 of 37, top 6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1625 | #16 of 113, top 15% | high | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [FrontierSWE](https://noometry.com/benchmarks/frontierswe) | 47% | #7 of 18, top 39% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 61% | #4 of 121, top 4% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 78.4% | #3 of 31, top 10% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 87.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 91.9% | #3 of 119, top 3% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1519 | #7 of 294, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MirrorCode](https://noometry.com/benchmarks/mirrorcode) | 63.9% | #3 of 9, top 34% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 2,041 | #7 of 105, top 7% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Claude Fable 5 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 63.6% | #10 of 49, top 21% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index) | 16.1% | #3 of 14, top 22% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 39.7% | #8 of 26, top 31% | max | [τ²-bench](https://taubench.com/) | 2026-08-04 |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 41.8% | Best of 11 | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GBAEval](https://noometry.com/benchmarks/gbaeval) | 74.5% | #2 of 23, top 9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 30% | #5 of 36, top 14% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 29.8% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1230 | #5 of 32, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 4,530 |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 5,680 | #22 of 60, top 37% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 5,019 |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 4,967 |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 4,340 |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Claude Fable 5 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 87.5% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 76.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 89.2% | #8 of 83, top 10% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 82.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 88.3% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 81.9% | Best of 77 | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 88.8% |  |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 91.4% | Best of 99 |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 92.7% | #11 of 91, top 13% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 95.5% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 90.5% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 98.5% | Best of 83 | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 92.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 98.5% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 28.6% | #12 of 134, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 41% | #18 of 129, top 14% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 28% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 41% |  | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-09 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 39.3% | Best of 38 | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 39.5% | #8 of 24, top 34% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-17 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1508 | #8 of 297, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 52% | #10 of 74, top 14% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-31 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 96.5% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 91.5% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 98.4% | #2 of 151, top 2% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 95.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 97.3% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 61.1% | #5 of 125, top 4% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 59.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 60.3% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 60.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 61% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 95% | Best of 25 | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.13 | #9 of 10, top 90% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 162.06 | #9 of 213, top 5% |  | [Epoch AI](https://epoch.ai/eci) | 2026-06-09 |

### Math

Claude Fable 5 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 87% | #9 of 81, top 12% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-09 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 90.2% | #4 of 63, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-09 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 100% | Best of 173 | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [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) | 99.7% |  | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-10 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 95% | #8 of 77, top 11% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1519 | #5 of 285, top 2% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath Erdős](https://noometry.com/benchmarks/frontiermath-erdos) | 0% | #4 of 7, top 58% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |

### Knowledge

Claude Fable 5 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 83.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 78.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 85.9% | #61 of 186, top 33% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 70.7% | #6 of 77, top 8% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1534 | #8 of 273, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Claude Fable 5 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1324 | Best of 122 | high | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 38.6% | #5 of 31, top 17% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 35.8% | #18 of 31, top 59% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1496 | #4 of 38, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Claude Fable 5 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1481 | #9 of 297, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1543 | #8 of 285, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1505 | #8 of 223, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1486 | #17 of 231, top 8% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1506 | #5 of 211, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1488 | #4 of 213, top 2% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1504 | #7 of 283, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1498 | #7 of 226, top 4% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Claude Fable 5 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1502 | #6 of 298, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Claude Fable 5 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1509 | #6 of 291, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Claude Fable 5 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1491 | #8 of 297, top 3% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1494 | #5 of 295, top 2% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1943 | #10 of 115, top 9% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1340 | #2 of 28, top 8% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1504 | #4 of 295, top 2% | high | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Claude Fable 5 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [anthropic](https://docs.anthropic.com/en/docs/about-claude/models) | $10 | $50 | $1 | 2026-10-10 |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $10 | $50 | $1 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $10 | $50 | $1 | 2026-10-10 |
| [openrouter](https://openrouter.ai/anthropic/claude-fable-5) | $10 | $50 | $1 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude) | $10 | $50 | $1 | 2026-10-10 |

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

## Compare Claude Fable 5

-   [Claude Fable 5 vs Claude Opus 5](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5)
-   [Claude Fable 5 vs GPT-6.1 Sol](https://noometry.com/compare/claude-fable-5-vs-gpt-6-1-sol)
-   [Claude Fable 5 vs Claude Opus 5.5](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5-5)
-   [Claude Fable 5 vs GPT-5.6 Sol](https://noometry.com/compare/claude-fable-5-vs-gpt-5-6-sol)
-   [Claude Fable 5 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-vs-claude-fable-5-1)
-   [Claude Fable 5 vs GPT-5.5 Pro](https://noometry.com/compare/claude-fable-5-vs-gpt-5-5-pro)
-   [Claude Fable 5 vs GPT-6 Astra](https://noometry.com/compare/claude-fable-5-vs-gpt-6-astra)
-   [Claude Fable 5 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-fable-5-vs-gemini-3-8-flash)
-   [Claude Fable 5 vs Kimi K3](https://noometry.com/compare/claude-fable-5-vs-kimi-k3)
-   [Claude Fable 5 vs Grok 4.6](https://noometry.com/compare/claude-fable-5-vs-grok-4-6)
-   [Claude Fable 5 vs Qwen3.8 Max](https://noometry.com/compare/claude-fable-5-vs-qwen3-8-max)
-   [Claude Fable 5 vs GLM-5.3](https://noometry.com/compare/claude-fable-5-vs-glm-5-3)
-   [Claude Fable 5 vs Muse Spark 1.3](https://noometry.com/compare/claude-fable-5-vs-muse-spark-1-3)
-   [Claude Fable 5 vs DeepSeek V4 Pro](https://noometry.com/compare/claude-fable-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 Opus 5](https://noometry.com/models/claude-opus-5)67.8
-   [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 Fable 5?

Claude Fable 5 by Anthropic ranks 5th of 354 ranked models on the Noometry Index as of October 2026, with a score of 66.8. Its strongest category is agentic & tool use, where it ranks 2nd. API pricing starts at $10 per million input tokens and $50 per million output tokens, with a 1M-token context window.

### How much does Claude Fable 5 cost?

Claude Fable 5 costs $10 per million input tokens and $50 per million output tokens on Anthropic's own API, with cached input at $1.

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

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

### Is Claude Fable 5 open source?

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

### How fast is Claude Fable 5?

Claude Fable 5 generated about 25 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

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

Relative to other ranked models, Claude Fable 5 places best in coding, agentic & tool use, math and lowest in multimodal, knowledge, long context.

### What is Claude Fable 5 best at?

Its best category is agentic & tool use, where it ranks 2nd on Noometry.

### Cite this page

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

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