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Gemini 3.5 Flash vs Claude Fable 5+ 1 other

Compare Gemini 3.5 Flash, Claude Fable 5, and 1 other vision model side-by-side. Test these models on Open Prompt, Image Captioning, OCR, Classification, and Object Detection in the Playground.

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GoogleGemini 3.5 Flash
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AnthropicClaude Fable 5
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AnthropicClaude Sonnet 5
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Models in this comparison

Model Overviews

Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.

Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.

Gemini 3.5 Flash vs Claude Fable 5 Comparison Table + 1 other

PropertyGemini 3.5 FlashClaude Fable 5Claude Sonnet 5
OrganizationGoogleAnthropicAnthropic
Categoryclosedclosedclosed
Modalitymultimodalmultimodalmultimodal
Release DateMay 2026Jun 2026Jun 2026
Context Window1.0M1.0M1.0M
Parameters
LicenseProprietaryProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.50$10.00$2.00
Output $/1M$9.00$50.00$10.00
Vision Tasks
CaptioningDemoDemoDemo
ClassificationDemoDemoDemo
Document Question Answering
Object DetectionDemoDemoDemo
OCRDemoDemoDemo
Visual Question AnsweringDemoDemoDemo
Chart Question Answering
Multi-Label Classification
Vision Language
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
79.1%
79.1%
70.15%
Avg Response Time6.71s21.66s3.90s
Median input tokensincl. image tokens1.1K2.0K2.1K
Median output tokens29440661
Est. cost / taskon this benchmark$0.0043$0.041$0.0048
Defect Detection
80%(12/15)
86.7%(13/15)
73.3%(11/15)
Document Understanding
77.8%(7/9)
88.9%(8/9)
66.7%(6/9)
Object Counting
60%(6/10)
40%(4/10)
20%(2/10)
Object Understanding
92.9%(13/14)
92.9%(13/14)
92.9%(13/14)
Spatial Understanding
78.9%(15/19)
78.9%(15/19)
78.9%(15/19)
OCR
Overall Score
90.39%
89.52%
83.84%
Avg Response Time4.86s7.72s2.77s
Median input tokensincl. image tokens1.1K578642
Median output tokens19615564
Est. cost / taskon this benchmark$0.0034$0.014$0.0019
Focused Scene OCR
90.9%(90/99)
93.9%(93/99)
88.9%(88/99)
Handwritten Math
90%(9/10)
80%(8/10)
50%(5/10)
License Plate Recognition
100%(30/30)
90%(27/30)
90%(27/30)
Text Recognition
86.7%(26/30)
83.3%(25/30)
80%(24/30)
VQA & Extraction
86.7%(52/60)
86.7%(52/60)
80%(48/60)

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