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Claude Fable 5 vs GPT-5.6 Sol

Compare Claude Fable 5 and GPT-5.6 Sol side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Open Prompt, and Object Detection.

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AnthropicClaude Fable 5
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OpenAIGPT-5.6 Sol
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Models in this comparison

Claude Fable 5 vs GPT-5.6 Sol on Vision Evals

Claude Fable 5 scores higher on 3 of the five Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Sol leads 68.4% to 56.4%.

Overall, Claude Fable 5 averages 71.7% (#19 of 63) against 72.4% (#18 of 63) for GPT-5.6 Sol.

GPT-5.6 Sol is cheaper ($0.019 vs $0.039 per sample), while Claude Fable 5 is faster (9.5s vs 12.2s per sample).

Claude Fable 5GPT-5.6 Sol

Claude Fable 5 vs GPT-5.6 Sol Comparison Table

Evals updated October 13, 2026Pricing updated October 23, 2026

PropertyClaude Fable 5GPT-5.6 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Jul 2026
Context Window1.0M1.5M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$10.00$4.00
Output $/1M$50.00$20.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
71.7%
72.4%
Avg cost / sample$0.039$0.019
Avg speed / sample9.54s12.16s
By task
Object Detection (low)
56.4%
$0.059
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.031
Object Detection (high)–
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.071
Counting (low)
63.5%
$0.017
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0098
Counting (high)–
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.016
Identification (low)
100.0%
$0.014
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0056
Identification (high)–
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0060
OCR (low)
72.3%
$0.041
63.6%
$0.019
by category
Single value
70.9%
Transcription
90.2%
Structured JSON
85.7%
Text localization
31.6%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)
74.2%
$0.075
65.3%
$0.048
by category
Single value
72.2%
Transcription
87.7%
Structured JSON
89.2%
Text localization
33.3%
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
66.2%
$0.018
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0086
Reasoning (high)
66.2%
$0.024
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.012

Claude Fable 5 vs GPT-5.6 Sol: 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-5.6 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.

GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.