Anthropic's Mythos-class model for multi-day autonomous coding and knowledge work that plans, delegates, and self-verifies.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where Fable 5 Separates: Long, Hard, Multi-Step Tasks
Benchmarks
Independent Signals on Reasoning, Coding, and Depth
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
Claude Fable 5 vs Claude Opus 4.8: When the Premium Pays Off
Fable 5 and Opus 4.8 are the natural comparison because Opus 4.8 is the model Fable 5 falls back to when safety classifiers decline a request, and both target demanding reasoning and coding. They share the 1M-token context window and 128K max output. The practical decision is capability versus cost: Fable 5 leads on agentic coding (80.3% vs 69.2% SWE-Bench Pro) and its lead grows on the longest, hardest tasks, while Opus 4.8 sits at roughly half the per-token output cost and remains strong on well-defined work.
Choose Claude Fable 5 when tasks are long-running, ambiguous, or highly multi-step — large refactors, overnight autonomous agents, and frontier-difficulty debugging — where a failed run wastes far more than the token premium. Choose Claude Opus 4.8 for high-volume or well-scoped work where its lower per-token cost and faster turnaround matter more than the marginal capability gain, and where you would already be satisfied with its results.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Multi-day autonomous coding agents
Fable 5 is built for agent harnesses that run for extended periods — planning across stages, delegating to sub-agents, writing its own tests, and recovering when steps fail. Its top SWE-Bench Pro score (80.3%) and widening lead on the hardest FrontierCode tasks support use in overnight refactors and unattended engineering sessions. Anthropic cites a codebase-wide migration on a 50-million-line Ruby repository completed in a day. The trade-off is latency and cost, so scope these as asynchronous jobs rather than interactive tooling.
Whole-codebase refactoring and review
The 1M-token context window at standard pricing lets Fable 5 hold an entire mid-sized repository, and the 128K max output supports large diffs and long-form generation in a single request. Combined with strong agentic-coding benchmarks and vision-based self-checking of outputs against goals, this fits large-scale migrations, cross-cutting feature work, and performance analysis. Because the full window is the default with no long-context surcharge, whole-repo reasoning is practical — but uncached full-window calls are expensive, so use prompt caching aggressively.
Document-heavy analytical knowledge work
Fable 5 reads diagrams, charts, and tables nested inside files and PDFs, and posted the top score on Hebbia's senior-level Finance Benchmark with strong document-based reasoning. That suits finance, legal, and analytics workflows that combine long source documents with multi-step reasoning. The 1M context and file input let you feed dozens of documents at once. Note the January 2026 knowledge cutoff and that flagged content may be refused, so keep source material current and plan for fallbacks.
Complex scientific and research reasoning
Anthropic positions Fable 5 as strong on scientific research and reasoning, and it can extract precise numbers from detailed scientific figures using vision. It performs well on graduate-level reasoning benchmarks, separating from GPT-5.5 by a meaningful margin on tool-free GPQA-style questions. This fits research synthesis, data-analysis pipelines, and computational workflows. Because biology and chemistry topics are among the categories that safety classifiers may reroute, expect occasional refusals in sensitive research domains.