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Claude Fable 5.1 vs GPT-5.6 Sol

Compare Claude Fable 5.1 and GPT-5.6 Sol side-by-side. See how these vision models stack up in Object Detection, OCR, Image Captioning, Open Prompt, and Classification.

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AnthropicClaude Fable 5.1
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OpenAIGPT-5.6 Sol
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Models in this comparison

Claude Fable 5.1 vs GPT-5.6 Sol on Vision Evals

Claude Fable 5.1 scores higher on 3 of the five Vision Evals tasks.

The widest gap is OCR, where Claude Fable 5.1 leads 72.9% to 63.6%.

Overall, Claude Fable 5.1 averages 74.7% (#14 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 5.1 is faster (8.3s vs 12.2s per sample).

Claude Fable 5.1GPT-5.6 Sol

Claude Fable 5.1 vs GPT-5.6 Sol Comparison Table

Evals updated October 13, 2026Pricing updated October 17, 2026

PropertyClaude Fable 5.1GPT-5.6 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 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
74.7%
72.4%
Avg cost / sample$0.039$0.019
Avg speed / sample8.35s12.16s
By task
Object Detection (low)
61.4%
±0.5, Mean of 3 runs, range 61.0 to 62.0
$0.060
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.031
Object Detection (high)
65.0%
±0.4, Mean of 3 runs, range 64.6 to 65.3
$0.078
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.071
Counting (low)
69.4%
±2.7, Mean of 3 runs, range 66.2 to 71.6
$0.019
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0098
Counting (high)
73.0%
±4.7, Mean of 3 runs, range 67.6 to 77.0
$0.023
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.016
Identification (low)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.013
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0056
Identification (high)
96.9%
±3.1, Mean of 3 runs, range 93.8 to 100.0
$0.014
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0060
OCR (low)
72.9%
$0.039
63.6%
$0.019
by category
Single value
64.3%
Transcription
84.7%
Structured JSON
89.2%
Text localization
46.5%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)
75.3%
$0.051
65.3%
$0.048
by category
Single value
69.1%
Transcription
95.2%
Structured JSON
89.5%
Text localization
43.4%
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
72.0%
±1.3, Mean of 3 runs, range 70.9 to 73.5
$0.019
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0086
Reasoning (high)
73.1%
±2.0, Mean of 3 runs, range 71.5 to 75.5
$0.028
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.012

Claude Fable 5.1 vs GPT-5.6 Sol: Overview

Claude Fable 5.1

Claude Fable 5.1 is a proprietary multimodal model from Anthropic in the Mythos-class tier of the Claude 5 family, positioned above Claude Opus for demanding reasoning and long-horizon agentic work. It accepts text and images as input and returns text, with a one million token context window and a maximum output of 128 thousand tokens. Adaptive thinking is always on, and an effort parameter controls how much reasoning the model applies to a given request. Anthropic reports a reliable knowledge and training data cutoff of June 2026. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model; the difference between them is the set of safety classifiers applied to dual-use cybersecurity and biology requests.

On the vision side, Anthropic documents improvements in reading dense charts, financial filings, and tables nested inside PDF documents, which extends the model toward document understanding, chart question answering, and spreadsheet and slide work. Reported evaluations cover agentic scientific research on Terminal-Bench-Science 0.1, agentic coding on Terminal-Bench 4.0, computer use on OSWorld 2.0, and multidisciplinary reasoning on Humanity's Last Exam. Model weights are not published.

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.

Frequently Asked Questions

On Roboflow's Vision Evals, Claude Fable 5.1 performed better. It scores higher on 3 of the five vision tasks and averages 74.7% (#14 of 63) against 72.4% (#18 of 63) for GPT-5.6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals OCR benchmark at low effort, Claude Fable 5.1 leads with 72.9% against 63.6%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.019 per sample against $0.039. Claude Fable 5.1 is priced at $10.00 per 1M input tokens and $50.00 per 1M output; GPT-5.6 Sol is priced at $4.00 per 1M input tokens and $20.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Claude Fable 5.1 is faster. Across Roboflow's Vision Evals it averaged 8.3s per inference against 12.2s. 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 object detection and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.