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Claude Fable 5.1 vs GPT-5.4 Mini

Compare Claude Fable 5.1 and GPT-5.4 Mini side-by-side. See how these vision models stack up in Object Detection, OCR, Image Captioning, Open Prompt, and Classification.

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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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OpenAIGPT-5.4 Mini
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Models in this comparison

Claude Fable 5.1 vs GPT-5.4 Mini on Vision Evals

Claude Fable 5.1 scores higher on all six Vision Evals tasks.

The widest gap is Object Detection, where Claude Fable 5.1 leads 61.4% to 16.1%.

Overall, Claude Fable 5.1 averages 81.3% (#7 of 36) against 64.7% (#30 of 36) for GPT-5.4 Mini.

GPT-5.4 Mini is both cheaper ($0.0030 vs $0.035 per sample) and faster (5.3s vs 8.3s per sample).

Claude Fable 5.1GPT-5.4 Mini

Claude Fable 5.1 vs GPT-5.4 Mini Comparison Table

Evals updated September 2, 2026Pricing updated September 2, 2026

PropertyClaude Fable 5.1GPT-5.4 Mini
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Mar 2026
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$10.00$0.750
Output $/1M$50.00$4.50
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
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%
64.7%
Avg cost / sample$0.035$0.0030
Avg speed / sample8.28s5.35s
By task
Object Detection (low)
61.4%
±0.5, Mean of 3 runs, range 61.0 to 62.0
$0.060
16.1%
$0.0044
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
60.8%
$0.0019
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
84.4%
$0.0013
Identification (high)
96.9%
±3.1, Mean of 3 runs, range 93.8 to 100.0
$0.014
OCR (low)
94.0%
±0.4, Mean of 3 runs, range 93.6 to 94.4
$0.039
88.1%
$0.0042
OCR (high)
93.6%
±0.2, Mean of 3 runs, range 93.5 to 93.9
$0.039
Data Extraction (low)
93.1%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.016
83.5%
$0.0014
Data Extraction (high)
93.5%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.016
Reasoning (low)
72.0%
±1.3, Mean of 3 runs, range 70.9 to 73.5
$0.019
55.6%
$0.0023
Reasoning (high)
73.1%
±2.0, Mean of 3 runs, range 71.5 to 75.5
$0.028
62.9%
$0.0081

Claude Fable 5.1 vs GPT-5.4 Mini: 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-5.4 Mini

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

Frequently Asked Questions

On Roboflow's Vision Evals, Claude Fable 5.1 performed better. It scores higher on all six vision tasks and averages 81.3% (#7 of 36) against 64.7% (#30 of 36) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Object Detection benchmark at low effort, Claude Fable 5.1 leads with 61.4% against 16.1%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0030 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-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.3s per inference against 8.3s. 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.