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Claude Fable 5 vs Gemini 3 Pro

Compare Claude Fable 5 and Gemini 3 Pro 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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GoogleGemini 3 Pro

Gemini 3 Pro is deprecated and can no longer be run. Details and evals are still available on its model page.

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Claude Fable 5 vs Gemini 3 Pro: 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.

Gemini 3 Pro

Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.

The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.

Claude Fable 5 vs Gemini 3 Pro Comparison Table

PropertyClaude Fable 5Gemini 3 Pro
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Nov 2025
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$10.00
Output $/1M$50.00
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Chart Question Answering
Document Question Answering
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
79.1%
Avg Response Time21.66s
Median input tokensincl. image tokens2.0K
Median output tokens406
Est. cost / taskon this benchmark$0.041
Defect Detection
86.7%(13/15)
Document Understanding
88.9%(8/9)
Object Counting
40%(4/10)
Object Understanding
92.9%(13/14)
Spatial Understanding
78.9%(15/19)
OCR
Overall Score
89.52%
Avg Response Time7.72s
Median input tokensincl. image tokens578
Median output tokens155
Est. cost / taskon this benchmark$0.014
Focused Scene OCR
93.9%(93/99)
Handwritten Math
80%(8/10)
License Plate Recognition
90%(27/30)
Text Recognition
83.3%(25/30)
VQA & Extraction
86.7%(52/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology