multimodal LLM
Claude Fable 5.1
Anthropic's model for demanding reasoning and long-horizon agentic work; adaptive thinking always on, default effort high. Available via API, GitHub Copilot (admin opt-in) and Kiro limited preview.
Levelmodel
Classmultimodal LLM
Parametersnot in the notes
Context1,000,000
Released1 Sep 2026
Opennessproprietary
Runs onnot in the notes
Statusactive
Benchmark Results
vendor-reportedindependent
Arena Text leaderboardhuman preference arena
maxindependent
1,501 ±6
1,2001,4001,600
Score in Elo; the axis starts at 1,200, as Elo has no zero. Higher is better.
rank 6, Arena Text overall
Arena Vision leaderboardhuman preference arena
maxindependent
1,286 ±10
1,2701,2901,310
Score in Elo; the axis starts at 1,270, as Elo has no zero. Higher is better.
rank 17 of 165, Arena Vision overall, style control default, 165 models
Artificial Analysis Intelligence Index v4.3.2composite index
maxindependent
53
04590
Score in index. Higher is better.
rank 5 of 205, AA leaderboard, 205 current models
benchlm.ai frontier compositecomposite index
vendor-reported
82
04590
Score in index. Higher is better.
benchlm.ai frontier top 6
GDPval-AA v2.1agents and tool use
vendor-reported
1,735
1,6001,7501,900
Score in Elo; the axis starts at 1,600, as Elo has no zero. Higher is better.
Humanity's Last Exam (HLE)knowledge and reasoning
Table mixes runs with and without toolsvendor-reported
65
050100
Score in %. Higher is better.
rank 1, BenchLM aggregator, top score
IOIcoding
Vals.ai, max effortindependent
90.78 ±4.65
050100
Score in %. Higher is better.
LiveCodeBenchcoding
Vals.ai, max effortindependent
90.52 ±0.86
050100
Score in %. Higher is better.
rank 1 of 143
Vals-run variantindependent
90.5
050100
Score in %. Higher is better.
rank 1, Vals.ai run shown on BenchLM
MMMUmultimodal
Vals mirrorindependent
90.64
050100
Score in %. Higher is better.
Vals.ai mirror shown on BenchLM
OSWorld 2.0agents and tool use
vendor-reported
80.7
050100
Score in %. Higher is better.
ProgramBenchcoding
Vals.ai, max effortindependent
7 ±1.81
050100
Score in %. Higher is better.
Terminal-Bench 2.1agents and tool use
Vals.ai, max effort; 79.03% if fallback-assisted tasks count as failuresindependent
85.02
050100
Score in %. Higher is better.
Terminal-Bench 4.0agents and tool use
Vals.ai, max effortindependent
58.08 ±3.31
050100
Score in %. Higher is better.
Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).
Lineage
successorproduct of
Claude Fable 5 is the parent (successor)medium confidencenotesWhere this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 3 models. Confidence and evidence are the notes' own.
Identity
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- not in the notes Not disclosed
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- 1,000,000 tokens
- Input
- text, image
- Output
- text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- proprietary
- Licence
- Proprietary sources
- Access routes
- API
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- Vendor cloud/API only (T1). Max output 128K tokens.
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- Anthropic docs state all current models support multilingual capabilities; Bahasa Indonesia not verified
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Industry Use Cases
No industry use case links this model.
Links
DeveloperAnthropicfrontier lab, USA
Anthropicfrontier lab, USAModelClaude Fable 5.1Anthropic, multimodal LLM
Claude Fable 5.1Anthropic, multimodal LLMUse CasesIndustrial Copilots and Maintenance AssistantsIndustrialClinical LLM (Frontier Proprietary)HealthcarePharma Documents, Regulatory and Life-Science R&DHealthcareAmbient Clinical ScribingHealthcare+3 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Pertamina Geothermal EnergyIn production, 14 Jul 2026
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026
Banner HealthIn production, 11 Jan 2026CommureIn production, 11 Jan 2026+45 moreDeployments of these use cases; none names this model.
PT Pertamina Geothermal EnergyIn production, 14 Jul 2026PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026Banner HealthIn production, 11 Jan 2026CommureIn production, 11 Jan 2026+45 moreDeployments of these use cases; none names this model.Organisations 1
Sources
- Models overview platform.claude.com/docs/en/models/overview https://platform.claude.com/docs/en/models/overview
- Best AI models, October 2026: Opus 5.5, GPT-6, Gemini slash-digital.io https://slash-digital.io/en/insights/ai-models-2026/
- LLM Leaderboard - Comparison of AI models from OpenAI, Anthropic, Google, SpaceXAI & others | Artificial Analysis artificialanalysis.ai https://artificialanalysis.ai/leaderboards/models
- Claude Fable 5.1 Benchmarks, Cost and Capabilities | Vals AI vals.ai https://vals.ai/models/anthropic_claude-fable-5-1
- Models overview platform.claude.com/docs/en/about-claude/models/overview https://platform.claude.com/docs/en/about-claude/models/overview
- Supported AI models in GitHub Copilot - GitHub Docs docs.github.com https://docs.github.com/en/copilot/reference/ai-models/supported-models
- Models - Docs - Kiro kiro.dev https://kiro.dev/docs/models/
- HLE Leaderboard: Claude Fable 5.1 Leads at 65% benchlm.ai https://www.benchlm.ai/benchmarks/hle
- Best LLM for Coding (October 2026): SWE-bench & LiveCodeBench Ranked benchlm.ai https://benchlm.ai/coding
- MMMU Leaderboard & Scores , October 2026 benchlm.ai https://www.benchlm.ai/benchmarks/mmmu
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Claude Fable 5.1", https://aurion.semai.id/models/anthropic-claude-fable-5-1/, records of October 2026.