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Claude Fable 5.1 vs Gemini 3.1 Pro

Compare Claude Fable 5.1 and Gemini 3.1 Pro side-by-side. See how these vision models stack up in Object Detection, OCR, Image Captioning, Open Prompt, and Classification.

Compare Claude Fable 5.1 vs Gemini 3.1 Pro live

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

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AnthropicClaude Fable 5.1
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GoogleGemini 3.1 Pro
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Models in this comparison

Claude Fable 5.1 vs Gemini 3.1 Pro on Vision Evals

Gemini 3.1 Pro scores higher on 4 of the five Vision Evals tasks.

The widest gap is Object Detection, where Gemini 3.1 Pro leads 67.4% to 61.4%.

Overall, Claude Fable 5.1 averages 74.7% (#14 of 63) against 76.8% (#13 of 63) for Gemini 3.1 Pro.

Gemini 3.1 Pro is cheaper ($0.010 vs $0.039 per sample), while Claude Fable 5.1 is faster (8.3s vs 8.4s per sample).

Claude Fable 5.1Gemini 3.1 Pro

Claude Fable 5.1 vs Gemini 3.1 Pro Comparison Table

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

PropertyClaude Fable 5.1Gemini 3.1 Pro
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Feb 2026
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$10.00$2.00
Output $/1M$50.00$12.00
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%
76.8%
Avg cost / sample$0.039$0.010
Avg speed / sample8.35s8.44s
By task
Object Detection (low)
61.4%
±0.5, Mean of 3 runs, range 61.0 to 62.0
$0.060
67.4%
$0.010
Object Detection (high)
65.0%
±0.4, Mean of 3 runs, range 64.6 to 65.3
$0.078
–
Counting (low)
69.4%
±2.7, Mean of 3 runs, range 66.2 to 71.6
$0.019
71.6%
$0.0071
Counting (high)
73.0%
±4.7, Mean of 3 runs, range 67.6 to 77.0
$0.023
–
Identification (low)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.013
100.0%
$0.0070
Identification (high)
96.9%
±3.1, Mean of 3 runs, range 93.8 to 100.0
$0.014
–
OCR (low)
72.9%
$0.039
72.6%
$0.010
by category
Single value
64.3%
Transcription
84.7%
Structured JSON
89.2%
Text localization
46.5%
Single value
67.8%
Transcription
88.5%
Structured JSON
82.0%
Text localization
52.0%
OCR (high)
75.3%
$0.051
73.1%
$0.026
by category
Single value
69.1%
Transcription
95.2%
Structured JSON
89.5%
Text localization
43.4%
Single value
68.7%
Transcription
90.8%
Structured JSON
83.9%
Text localization
47.1%
Reasoning (low)
72.0%
±1.3, Mean of 3 runs, range 70.9 to 73.5
$0.019
72.2%
$0.012
Reasoning (high)
73.1%
±2.0, Mean of 3 runs, range 71.5 to 75.5
$0.028
74.8%
$0.021

Claude Fable 5.1 vs Gemini 3.1 Pro: 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.

Gemini 3.1 Pro

Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.

The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.1 Pro performed better. It scores higher on 4 of the five vision tasks and averages 76.8% (#13 of 63) against 74.7% (#14 of 63) 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, Gemini 3.1 Pro leads with 67.4% against 61.4%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.1 Pro is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.010 per sample against $0.039. Claude Fable 5.1 is priced at $10.00 per 1M input tokens and $50.00 per 1M output; Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 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 8.4s. 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.