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Claude Fable 5.1 vs GPT-6.1 Sol

Compare Claude Fable 5.1 and GPT-6.1 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-6.1 Sol
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Models in this comparison

Claude Fable 5.1 vs GPT-6.1 Sol on Vision Evals

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

The widest gap is Object Detection, where GPT-6.1 Sol leads 80.8% to 61.4%.

Overall, Claude Fable 5.1 averages 81.3% (#12 of 61) against 85.5% (#4 of 61) for GPT-6.1 Sol.

GPT-6.1 Sol is cheaper ($0.0061 vs $0.035 per sample), while Claude Fable 5.1 is faster (8.3s vs 14.3s per sample).

Claude Fable 5.1GPT-6.1 Sol

Claude Fable 5.1 vs GPT-6.1 Sol Comparison Table

Evals updated September 29, 2026Pricing updated September 29, 2026

PropertyClaude Fable 5.1GPT-6.1 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Sep 2026
Context Window1.0M1.1M
Parametersundisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$10.00$2.00
Output $/1M$50.00$10.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Promptable Concept SegmentationDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
81.3%
85.5%
Avg cost / sample$0.035$0.0061
Avg speed / sample8.28s14.31s
By task
Object Detection (low)
61.4%
±0.5, Mean of 3 runs, range 61.0 to 62.0
$0.060
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
Object Detection (high)
65.0%
±0.4, Mean of 3 runs, range 64.6 to 65.3
$0.078
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
Counting (low)
69.4%
±2.7, Mean of 3 runs, range 66.2 to 71.6
$0.019
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
Counting (high)
73.0%
±4.7, Mean of 3 runs, range 67.6 to 77.0
$0.023
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
Identification (low)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.013
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
Identification (high)
96.9%
±3.1, Mean of 3 runs, range 93.8 to 100.0
$0.014
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
OCR (low)
94.0%
±0.4, Mean of 3 runs, range 93.6 to 94.4
$0.039
92.0%
±0.5, Mean of 3 runs, range 91.5 to 92.5
$0.0064
OCR (high)
93.6%
±0.2, Mean of 3 runs, range 93.5 to 93.9
$0.039
91.7%
±0.3, Mean of 3 runs, range 91.2 to 91.9
$0.017
Data Extraction (low)
93.1%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.016
88.0%
±0.5, Mean of 3 runs, range 87.6 to 88.7
$0.0030
Data Extraction (high)
93.5%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.016
90.0%
±1.0, Mean of 3 runs, range 88.7 to 90.7
$0.0037
Reasoning (low)
72.0%
±1.3, Mean of 3 runs, range 70.9 to 73.5
$0.019
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
Reasoning (high)
73.1%
±2.0, Mean of 3 runs, range 71.5 to 75.5
$0.028
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042

Claude Fable 5.1 vs GPT-6.1 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-6.1 Sol

GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.

On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6.1 Sol performed slightly better overall. The two split the six vision tasks 3 to 3, but GPT-6.1 Sol averages 85.5% (#4 of 61) against 81.3% (#12 of 61) for Claude Fable 5.1. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark at low effort, GPT-6.1 Sol leads with 80.8% against 61.4%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6.1 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0061 per sample against $0.035. Claude Fable 5.1 is priced at $10.00 per 1M input tokens and $50.00 per 1M output; GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 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 14.3s. 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.