Claude Opus 4.8 — Benchmarks, Capabilities, and Use Cases
Claude Opus 4.8 is a proprietary AI model from Anthropic, classified as Frontier tier with a 1M tokens context window. Explore benchmark scores, strengths, and ...
5 min read
Claude Fable 5 is the highest-rated AI model in this leaderboard, above all standard frontier models, developed by Anthropic and released in June 2026. It is a proprietary closed-weight model, available through the Anthropic API. The 1M-token context window is large enough to hold entire codebases, lengthy technical documents, or extended agentic sessions without truncation.
On the FlowHunt AI Leaderboard it is one of 24 tracked models, evaluated across 2 benchmarks. Standout scores: 95.0% on SWE-bench Verified (#1 of 13); 80.3% on SWE-bench Pro (#1 of 9).
Claude Fable 5 was released on June 9, 2026 as the first model in Anthropic’s new Mythos-class tier — a capability level above the existing Opus line. The name ‘Fable’ continues Anthropic’s literary naming convention (Opus, Sonnet, Haiku) while marking a significant architectural departure. Fable 5 is the production deployment of Anthropic’s internal Mythos 5 research model, with enterprise-grade safety and reliability constraints applied. At launch it posted 95% on SWE-bench Verified and 80.3% on SWE-bench Pro — the highest scores ever recorded on both benchmarks — establishing it as the first model capable of autonomously closing the majority of real-world GitHub issues without human guidance.
Note: New Mythos-class tier above Opus. Same base as Mythos 5 with production safeguards.
- Released
- June 2026
- Context
- 1M tokens
- Weights
- Closed
- Tier
- Super-Frontier
- Provider
- Anthropic
Claude Fable 5 Benchmark Rankings
Rank among all 24 models tracked on this leaderboard. SR = self-reported · 3P / AA / C = independently verified.
| Benchmark | Score | Rank | Source | What it measures |
|---|---|---|---|---|
| SWE-bench Verified | 95.0% | 1 of 13 | SR | Real GitHub issue resolution (500 verified issues) |
| SWE-bench Pro | 80.3% | 1 of 9 | SR | Multi-language, standardised scaffold |
Detailed Benchmark Scores
SWE-V
SR
95.0%
Real GitHub issue resolution (500 verified issues)
SWE-Pro
SR
80.3%
Multi-language, standardised scaffold
About Anthropic
Anthropic is an AI safety company founded in 2021 by former OpenAI researchers Dario Amodei and Daniela Amodei, along with a group of colleagues who had led OpenAI's safety and policy teams. The company's stated mission is the responsible development of AI for the long-term benefit of humanity, and its research agenda is defined by that commitment: Constitutional AI (CAI), a technique for training models to be helpful, harmless, and honest through self-critique; mechanistic interpretability, which aims to reverse-engineer what individual neurons and circuits inside large networks actually compute; and scaling science, which tries to predict how model behaviour changes with compute and data. Anthropic publishes this research openly and has become one of the most cited AI safety labs in academic literature. Claude is Anthropic's public model family — named for Claude Shannon, the founder of information theory — and spans four generations (1 through 4.x/Fable 5). Anthropic operates a commercial API and consumer-facing Claude.ai product, and maintains deep cloud partnerships with AWS (Amazon Bedrock) and Google Cloud (Vertex AI).
Use Cases
What to Use Claude Fable 5 For
- Software Engineering
Claude Fable 5 achieves 95% on SWE-bench Verified — ranked #1 of 13 models tracked here. The benchmark uses 500 human-validated real GitHub issues: the model reads the repo, understands the failing test, and produces a passing patch. At this level it autonomously closes complex tickets, performs multi-file refactors, and acts as an agentic coding assistant in real development workflows.
- Long-Document & Codebase Analysis
With a 1M-token context window, Claude Fable 5 can ingest entire software repositories, long legal contracts, book-length research reports, or extensive conversation histories in a single prompt — enabling deep document Q&A, cross-file codebase reasoning, and comprehensive summarisation without chunking heuristics or RAG pipelines.
Strengths & Limitations
Strengths
- Exceptional real-world coding — 95% SWE-bench Verified
- Very long documents and large codebases (1M context)
Limitations
- Closed-weight — cannot be self-hosted or fine-tuned on private data
Browse the Full AI Model Leaderboard
Compare all 24 models across 11 benchmarks — sortable, sourced, and updated June 2026.
Frequently asked questions
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