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.
Compare these vision models live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
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
| Property | Gemini 3.5 Flash | Claude Fable 5 | Claude Sonnet 5 |
|---|---|---|---|
| Organization | Anthropic | Anthropic | |
| Category | closed | closed | closed |
| Modality | multimodal | multimodal | multimodal |
| Release Date | May 2026 | Jun 2026 | Jun 2026 |
| Context Window | 1.0M | 1.0M | 1.0M |
| Parameters | |||
| License | Proprietary | Proprietary | Proprietary |
| Pricing per 1M tokens | |||
| Input $/1M | $1.50 | $10.00 | $2.00 |
| Output $/1M | $9.00 | $50.00 | $10.00 |
| Vision Tasks | |||
| Captioning | Demo | Demo | Demo |
| Classification | Demo | Demo | Demo |
| Document Question Answering | |||
| Object Detection | Demo | Demo | Demo |
| OCR | Demo | Demo | Demo |
| Visual Question Answering | Demo | Demo | Demo |
| 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 Time | 6.71s | 21.66s | 3.90s |
| Median input tokensincl. image tokens | 1.1K | 2.0K | 2.1K |
| Median output tokens | 294 | 406 | 61 |
| 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 Time | 4.86s | 7.72s | 2.77s |
| Median input tokensincl. image tokens | 1.1K | 578 | 642 |
| Median output tokens | 196 | 155 | 64 |
| 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