Claude Opus 4 vs Claude Fable 5
Compare Claude Opus 4 and Claude Fable 5 side-by-side. See how these vision models stack up in Image Captioning, OCR, Object Detection, Open Prompt, and Classification.
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Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
Claude Opus 4 vs Claude Fable 5: Overview
Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.
Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.
Claude Fable 5 is Anthropic's first generally available Mythos-class large language model, released on June 9, 2026. It is built for long-horizon, asynchronous, and agentic tasks that prior Claude generations could not sustain, including multi-day autonomous coding sessions, complex knowledge work, and document-heavy analysis. The model supports a 1 million token context window with up to 128,000 output tokens per request and uses adaptive thinking as its sole reasoning mode, where the effort level is adjustable but raw chain-of-thought is never returned. Vision capabilities allow the model to parse diagrams, charts, and tables embedded in files and PDFs, and to use visual feedback to evaluate its own coding outputs against design goals. On benchmarks such as SWE-Bench Pro, the model scores 80.3% compared to 69.2% for Claude Opus 4.8, and it leads on CursorBench 3.1 for autonomous coding workflows.
Claude Fable 5 shares the same underlying model weights as Claude Mythos 5, but is deployed with safety classifiers that automatically reroute queries in high-risk domains — including cybersecurity, biology, and chemistry — to Claude Opus 4.8. These classifiers trigger in fewer than 5% of sessions on average. As a designated Covered Model, all traffic is subject to mandatory 30-day data retention to support safety monitoring. The model is available via the Claude API, Amazon Bedrock, Vertex AI, and Microsoft Foundry. Anthropic has not publicly disclosed parameter count, architecture details, or training data composition for this model.
Claude Opus 4 vs Claude Fable 5 Comparison Table
| Property | Claude Opus 4 | Claude Fable 5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2025 | Jun 2026 |
| Context Window | 200K | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $10.00 |
| Output $/1M | $75.00 | $50.00 |
| Vision Tasks | ||
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Chart Question Answering | ||
| Document Question Answering | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 56.72% | 79.1% |
| Avg Response Time | 19.74s | 21.66s |
| Median input tokensincl. image tokens | 2.0K | |
| Median output tokens | 406 | |
| Est. cost / taskon this benchmark | $0.041 | |
| Defect Detection | 66.7%(10/15) | 86.7%(13/15) |
| Document Understanding | 88.9%(8/9) | 88.9%(8/9) |
| Object Counting | 0%(0/10) | 40%(4/10) |
| Object Understanding | 64.3%(9/14) | 92.9%(13/14) |
| Spatial Understanding | 57.9%(11/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 89.52% | |
| Avg Response Time | 7.72s | |
| Median input tokensincl. image tokens | 578 | |
| Median output tokens | 155 | |
| Est. cost / taskon this benchmark | $0.014 | |
| Focused Scene OCR | 93.9%(93/99) | |
| Handwritten Math | 80%(8/10) | |
| License Plate Recognition | 90%(27/30) | |
| Text Recognition | 83.3%(25/30) | |
| VQA & Extraction | 86.7%(52/60) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology