Contamination MapText / LLM / Model
Claude Fable 5 / Claude Mythos 5
- Organisation
- Anthropic
- Family
- Claude · version 5
- Released
- 2026-06-09 (Claude docs model page: 'Released June 9, 2026')
- Training cutoff
- 2026-01-01
Evidence
“Training data cutoff Jan 2026”
platform.claude.com · website · Model page, Additional details table · retrieved 2026-10-15 - Checkpoints
- claude-fable-5, claude-mythos-5
- Weights
- Closed · proprietary
- Links
- Model card
Training data disclosure: Partial list
Level determined for 0% of benchmark cells.
- TestTest data seen0
- SeenSeen, split unclear0
- TrainTraining data seen0
- Related data seen0
- NoNot seen0
- ?Unknown22
“Mythos 5 and Fable 5 were trained on a proprietary mix of publicly available information from the internet, public and private datasets, and synthetic data generated by other models.”
“The training corpus is derived from several publicly accessible repositories, notably including Common Crawl, a repository of web crawl data, as well as specialized datasets available through platforms like GitHub and HuggingFace.”
Other declared training data
-
Common Crawl (broad web data: may contain benchmark test sets) · split: unknown
Evidence
“The training corpus is derived from several publicly accessible repositories, notably including Common Crawl, a repository of web crawl data, as well as specialized datasets available through platforms like GitHub and HuggingFace.”
aial.ie · website · Section 2.1 · retrieved 2026-10-15 -
specialized datasets available through platforms like GitHub and HuggingFace (broad web data: may contain benchmark test sets) · split: unknown
Evidence
“The training corpus is derived from several publicly accessible repositories, notably including Common Crawl, a repository of web crawl data, as well as specialized datasets available through platforms like GitHub and HuggingFace.”
aial.ie · website · Section 2.1 · retrieved 2026-10-15 -
publicly available information from the internet (broad web data: may contain benchmark test sets) · split: unknown
Evidence
“Mythos 5 and Fable 5 were trained on a proprietary mix of publicly available information from the internet, public and private datasets, and synthetic data generated by other models.”
www-cdn.anthropic.com · model card · Section 1.1 · retrieved 2026-10-15 -
public and private datasets (not yet matched to a dataset) · split: unknown
Evidence
“Mythos 5 and Fable 5 were trained on a proprietary mix of publicly available information from the internet, public and private datasets, and synthetic data generated by other models.”
www-cdn.anthropic.com · model card · Section 1.1 · retrieved 2026-10-15 -
synthetic data generated by other models (not yet matched to a dataset) · split: unknown
Evidence
“Mythos 5 and Fable 5 were trained on a proprietary mix of publicly available information from the internet, public and private datasets, and synthetic data generated by other models.”
www-cdn.anthropic.com · model card · Section 1.1 · retrieved 2026-10-15
Benchmark cells
Classic LLM benchmarks
Unknown MMLU (Measuring Massive Multitask Language Understanding) · training data not disclosed in enough detail
Unknown GSM8K (Grade School Math 8K) · training data not disclosed in enough detail
Unknown MATH (Mathematics Aptitude Test of Heuristics) · training data not disclosed in enough detail
Unknown HumanEval · training data not disclosed in enough detail
Unknown ARC-Challenge (AI2 Reasoning Challenge, Challenge Set) · training data not disclosed in enough detail
Unknown HellaSwag · training data not disclosed in enough detail
Unknown TruthfulQA · training data not disclosed in enough detail
Unknown WinoGrande · training data not disclosed in enough detail
Multimodal LLM benchmarks
Unknown MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning) · training data not disclosed in enough detail
Unknown MMMU-Pro · training data not disclosed in enough detail
Unknown MathVista · training data not disclosed in enough detail
Unknown ChartQA · training data not disclosed in enough detail
Unknown DocVQA · training data not disclosed in enough detail
Unknown AI2D (AI2 Diagrams) · training data not disclosed in enough detail
Unknown CharXiv · training data not disclosed in enough detail
Recent LLM benchmarks
Unknown GPQA Diamond · training data not disclosed in enough detail
Unknown MMLU-Pro · training data not disclosed in enough detail
Unknown LiveCodeBench (code generation, release_v6) · training data not disclosed in enough detail
Unknown SWE-bench Verified · training data not disclosed in enough detail
Unknown AIME 2024 · training data not disclosed in enough detail
Unknown AIME 2025 · training data not disclosed in enough detail
Unknown MATH-500 · training data not disclosed in enough detail
Notes
Docs: Mythos 5 'shares Claude Fable 5's specifications and pricing' and is offered separately for verified cybersecurity use; the system card covers both. The EU summary says some data was acquired up to April 2026 (later than the Jan 2026 training data cutoff). The system card does not state its own decontamination; for SWE-bench it points to the Mythos Preview memorisation screening, and it chose ArXivMath March/April 2026 problems 'to avoid contamination with Fable's training data'. Accepts image input: “Claude Fable 5 can understand both text (including voice dictation) and image inputs, engaging in conversation, analysis, coding, and creative tasks.” Accepts image input: “Text and images → text”
Sources
- System Card: Claude Fable 5 & Claude Mythos 5 · model card · retrieved 2026-10-15
- Claude docs: Claude Fable 5 model page · website · retrieved 2026-10-15
- Public Summary of Training Content: Claude Mythos 5 / Fable 5 (Anthropic; copy archived by AIAL, 2026-08-03) · website · retrieved 2026-10-15
- Anthropic Transparency Hub: Model Report · website · retrieved 2026-10-15