Claude Fable 5 vs GPT-5.6 Terra
Compare Claude Fable 5 and GPT-5.6 Terra side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Open Prompt, and Object Detection.
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Claude Fable 5 vs GPT-5.6 Terra: 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.
GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.
GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.
Claude Fable 5 vs GPT-5.6 Terra Comparison Table
| Property | Claude Fable 5 | GPT-5.6 Terra |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jul 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $2.50 |
| Output $/1M | $50.00 | $15.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| classification | Demo | Demo |
| Document Question Answering | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 79.1% | |
| Avg Response Time | 21.66s | |
| Median input tokensincl. image tokens | 2.0K | |
| Median output tokens | 406 | |
| Est. cost / taskon this benchmark | $0.041 | |
| Defect Detection | 86.7%(13/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 40%(4/10) | |
| Object Understanding | 92.9%(13/14) | |
| Spatial Understanding | 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