Claude Fable 5 vs Claude Opus 5
Compare Claude Fable 5 and Claude Opus 5 side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Open Prompt, and Object Detection.
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Models in this comparison
Claude Fable 5 vs Claude Opus 5 on Vision Evals
Claude Fable 5 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Identification, where Claude Fable 5 leads 100.0% to 90.6%.
Overall, Claude Fable 5 averages 78.7% (#13 of 53) against 78.3% (#14 of 53) for Claude Opus 5.
Claude Opus 5 is both cheaper ($0.017 vs $0.034 per sample) and faster (7.4s vs 8.7s per sample).
Claude Fable 5 vs Claude Opus 5 Comparison Table
Evals updated September 5, 2026Pricing updated September 8, 2026
| Property | Claude Fable 5 | Claude Opus 5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $5.00 |
| Output $/1M | $50.00 | $25.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Image Tagging | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 78.7% | 78.3% |
| Avg cost / sample | $0.034 | $0.017 |
| Avg speed / sample | 8.71s | 7.38s |
| By task | ||
| Object Detection | 56.4% | 54.4% |
| Counting | 63.5% | 70.3% |
| Identification | 100.0% | 90.6% |
| OCR | 94.0% | 93.2% |
| Data Extraction | 91.8% | 89.7% |
| Reasoning (low) | 66.2% | 71.5% |
| Reasoning (high) | 66.2% | 74.2% |
Claude Fable 5 vs Claude Opus 5: Overview
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 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at roughly half the cost, priced at $5 per million input tokens and $25 per million output tokens. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.
Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Fable 5 performed better. It scores higher on 4 of the six vision tasks and averages 78.7% (#13 of 53) against 78.3% (#14 of 53) for Claude Opus 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Identification benchmark at low effort, Claude Fable 5 leads with 100.0% against 90.6%. This is the widest gap between the two models across the benchmark's tasks.
Claude Opus 5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.017 per sample against $0.034. Claude Fable 5 is priced at $10.00 per 1M input tokens and $50.00 per 1M output; Claude Opus 5 is priced at $5.00 per 1M input tokens and $25.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 5 is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 8.7s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.