Claude Fable 5 vs GPT-5.5

Compare Claude Fable 5 and GPT-5.5 side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Open Prompt, and Object Detection.

Compare Claude Fable 5 vs GPT-5.5 live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Detect and compare bounding boxes across models on the same image.

Open Object Detection in the full playground
AnthropicClaude Fable 5

Claude Fable 5 can't be run right now. See its model page for details.

OpenAIGPT-5.5
Run to compare this model.

Models in this comparison

OpenAI

Claude Fable 5 vs GPT-5.5: Overview

Claude Fable 5

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.

GPT-5.5

GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.

Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.

Claude Fable 5 vs GPT-5.5 Comparison Table

PropertyClaude Fable 5GPT-5.5
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Apr 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$10.00$5.00
Output $/1M$50.00$30.00
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Chart Question Answering
Document Question Answering
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
74.63%
77.61%
Avg Response Time16.44s30.12s
Median input tokensincl. image tokens1.4K
Median output tokens138
Est. cost / taskon this benchmark$0.011
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
77.8%(7/9)
88.9%(8/9)
Object Counting
30%(3/10)
30%(3/10)
Object Understanding
100%(14/14)
92.9%(13/14)
Spatial Understanding
78.9%(15/19)
78.9%(15/19)

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