Anthropic, proprietary

# Claude Fable 5.1

> Claude Fable 5.1 by Anthropic, released September 2026. Ranked #2 of 354 with a Noometry Index of 69.0. API: $10 in / $50 out per M tokens. 1M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/claude-fable-5-1
- Last updated: 2026-10-10
- Title: Claude Fable 5.1 Benchmarks, Price & Rank (October 2026)

Claude Fable 5.1 by Anthropic ranks 2nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 69.0. Its strongest category is coding, where it ranks 1st. 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:** #2 of 354
- **Index score:** 69.0
- **Evidence:** Confirmed 52 results
- **Provider:** [Anthropic](https://noometry.com/providers/anthropic)
- **Released:** September 1, 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:** Not measured
- **Value:** #207 of 219
- **Knowledge cutoff:** June 2026
- **Input:** text, image, pdf

## Category scores

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

Claude Fable 5.1 category scores

1.  Coding 74.7
2.  Agentic & Tool Use 50.7
3.  Reasoning 76.7
4.  Math 89.6
5.  Knowledge 69.6
6.  Multimodal 53.9
7.  Multilingual 59.1
8.  Instruction Following 79.2
9.  Long Context 46.7
10.  Writing & Preference 79.2
11.  20406080100

Claude Fable 5.1 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 74.7 | #1 | 9 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 50.7 | #5 | 3 |
| [Reasoning](https://noometry.com/best/reasoning) | 76.7 | #7 | 10 |
| [Math](https://noometry.com/best/math) | 89.6 | #4 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 69.6 | #6 | 3 |
| [Multimodal](https://noometry.com/best/multimodal) | 53.9 | #4 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 59.1 | #3 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 79.2 | #6 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 46.7 | #20 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 79.2 | #2 | 4 |

## Strengths and weaknesses

Categories where Claude Fable 5.1 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Claude Fable 5.1: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 74.7 | +36.0 | #1 of 340, top 1% |
| [Writing & Preference](https://noometry.com/best/writing) | 79.2 | +25.4 | #2 of 312, top 1% |
| [Multilingual](https://noometry.com/best/multilingual) | 59.1 | +11.7 | #3 of 297, top 2% |

### Weakest categories

Claude Fable 5.1: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 46.7 | +5.8 | #20 of 296, top 7% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 50.7 | +20.3 | #5 of 154, top 4% |
| [Multimodal](https://noometry.com/best/multimodal) | 53.9 | +15.3 | #4 of 128, top 4% |

## Closest competitors

The models ranked just above and below Claude Fable 5.1. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Claude Fable 5.1
| 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-5-1-vs-gpt-6-astra) |
| [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | #3 | 68.6 | $8 | — | [Compare](https://noometry.com/compare/claude-fable-5-1-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-5-1-vs-claude-opus-5) |
| [Claude Fable 5](https://noometry.com/models/claude-fable-5) | #5 | 66.8 | $20 | 25 | [Compare](https://noometry.com/compare/claude-fable-5-vs-claude-fable-5-1) |
| [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | #6 | 65.6 | $4 | — | [Compare](https://noometry.com/compare/claude-fable-5-1-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-5-1-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-5-1-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-5-1-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 5.1 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 50.9% | #6 of 37, top 17% | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 49.2% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 45.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 51.8% | #3 of 14, top 22% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 46.8% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 51.6% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1744 | #5 of 113, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [FrontierSWE](https://noometry.com/benchmarks/frontierswe) | 56.3% | #4 of 18, top 23% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 57.6% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 55.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 63.1% | #2 of 121, top 2% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 55.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 60.9% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 88.2% | Best of 31 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 92.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 92.9% | #2 of 119, top 2% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1528 | #6 of 294, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MirrorCode](https://noometry.com/benchmarks/mirrorcode) | 73.3% | #2 of 9, top 23% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 2,143 | #6 of 105, top 6% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Claude Fable 5.1 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 68.6% | #4 of 49, top 9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 59.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index) | 17.9% | #2 of 14, top 15% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 29.6% | #6 of 36, top 17% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 27.6% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 5,422 | #25 of 60, top 42% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Claude Fable 5.1 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 88.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 78.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 90% | #6 of 83, top 8% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 86.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 90% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 90% | #18 of 91, top 20% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 96% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 90% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 97.5% | #7 of 83, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 94.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 96.5% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 30.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 27.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 29.7% |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 29.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 31.1% | #6 of 134, top 5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 47% | #12 of 129, top 10% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-01 |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 57.1% | #3 of 24, top 13% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1526 | #5 of 297, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 58% | #6 of 74, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-01 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 97.6% | #4 of 151, top 3% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 96.5% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 94.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 97.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 97.6% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 65.5% | #2 of 125, top 2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 65.1% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 63.5% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 65.3% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 65.1% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 164.7 | #5 of 213, top 3% |  | [Epoch AI](https://epoch.ai/eci) | 2026-09-01 |

### Math

Claude Fable 5.1 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 90.2% | #4 of 81, top 5% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-01 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 87.8% | #6 of 63, top 10% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-01 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 100% | #2 of 173, top 2% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-01 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 100% | Best of 77 | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1525 | #4 of 285, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath Erdős](https://noometry.com/benchmarks/frontiermath-erdos) | 0% | #5 of 7, top 72% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-01 |

### Knowledge

Claude Fable 5.1 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 46.5% | #2 of 41, top 5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 70.8% | #5 of 77, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-01 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1535 | #7 of 273, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Claude Fable 5.1 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1318 | #4 of 122, top 4% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 41.9% | #4 of 31, top 13% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 70% | #5 of 31, top 17% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1513 | #2 of 38, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Claude Fable 5.1 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1507 | #3 of 297, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1586 | #3 of 285, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1525 | Best of 223 |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1500 | #5 of 231, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1543 | Best of 211 |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1534 | Best of 213 |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1521 | #2 of 283, top 1% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1516 | #2 of 226, top 1% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Claude Fable 5.1 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1517 | #5 of 298, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Claude Fable 5.1 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1522 | #3 of 291, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Claude Fable 5.1 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1510 | #3 of 297, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1507 | #3 of 295, top 2% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 2162 | #2 of 115, top 2% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1492 | #9 of 295, top 4% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Claude Fable 5.1 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [anthropic](https://docs.anthropic.com/en/docs/about-claude/models) | $10 | $50 | $0.25 | 2026-10-10 |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $10 | $50 | $0.25 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $10 | $50 | $0.25 | 2026-10-10 |
| [openrouter](https://openrouter.ai/anthropic/claude-fable-5.1) | $10 | $50 | $0.25 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude) | $10 | $50 | $0.25 | 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 5.1

-   [Claude Fable 5.1 vs Claude Fable 5](https://noometry.com/compare/claude-fable-5-vs-claude-fable-5-1)
-   [Claude Fable 5.1 vs GPT-6 Astra](https://noometry.com/compare/claude-fable-5-1-vs-gpt-6-astra)
-   [Claude Fable 5.1 vs Claude Opus 5.5](https://noometry.com/compare/claude-fable-5-1-vs-claude-opus-5-5)
-   [Claude Fable 5.1 vs Claude Opus 5](https://noometry.com/compare/claude-fable-5-1-vs-claude-opus-5)
-   [Claude Fable 5.1 vs GPT-6.1 Sol](https://noometry.com/compare/claude-fable-5-1-vs-gpt-6-1-sol)
-   [Claude Fable 5.1 vs GPT-5.6 Sol](https://noometry.com/compare/claude-fable-5-1-vs-gpt-5-6-sol)
-   [Claude Fable 5.1 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-fable-5-1-vs-gemini-3-8-flash)
-   [Claude Fable 5.1 vs Kimi K3](https://noometry.com/compare/claude-fable-5-1-vs-kimi-k3)
-   [Claude Fable 5.1 vs Grok 4.6](https://noometry.com/compare/claude-fable-5-1-vs-grok-4-6)
-   [Claude Fable 5.1 vs Qwen3.8 Max](https://noometry.com/compare/claude-fable-5-1-vs-qwen3-8-max)
-   [Claude Fable 5.1 vs GLM-5.3](https://noometry.com/compare/claude-fable-5-1-vs-glm-5-3)
-   [Claude Fable 5.1 vs Muse Spark 1.3](https://noometry.com/compare/claude-fable-5-1-vs-muse-spark-1-3)
-   [Claude Fable 5.1 vs DeepSeek V4 Pro](https://noometry.com/compare/claude-fable-5-1-vs-deepseek-v4-pro)
-   [Claude Fable 5.1 vs MiMo-V2.6-Pro](https://noometry.com/compare/claude-fable-5-1-vs-mimo-v2-6-pro)

## Other Anthropic models

-   [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
-   [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5)54.6

## Frequently asked questions

### How good is Claude Fable 5.1?

Claude Fable 5.1 by Anthropic ranks 2nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 69.0. Its strongest category is coding, where it ranks 1st. 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 5.1 cost?

Claude Fable 5.1 costs $10 per million input tokens and $50 per million output tokens on Anthropic's own API, with cached input at $0.25.

### What is Claude Fable 5.1's context window?

Claude Fable 5.1 accepts up to 1M tokens of input and can write up to 128K tokens in one response.

### Is Claude Fable 5.1 open source?

No. Claude Fable 5.1 is proprietary and available only through Anthropic's API and partner platforms.

### What are Claude Fable 5.1's strengths and weaknesses?

Relative to other ranked models, Claude Fable 5.1 places best in coding, writing & preference, multilingual and lowest in long context, agentic & tool use, multimodal.

### What is Claude Fable 5.1 best at?

Its best category is coding, where it ranks 1st on Noometry.

### Cite this page

Noometry. (2026). Claude Fable 5.1 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/claude-fable-5-1

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/claude-fable-5-1.md).