Claude Sonnet 4.6 vs Gemini 3 Flash
Compare Claude Sonnet 4.6 and Gemini 3 Flash side-by-side. See how these vision models stack up in Image Captioning, Classification, Open Prompt, Object Detection, and OCR.
Compare Claude Sonnet 4.6 vs Gemini 3 Flash 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
Claude Sonnet 4.6 vs Gemini 3 Flash: Overview
Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.
Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.
The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
Claude Sonnet 4.6 vs Gemini 3 Flash Comparison Table
| Property | Claude Sonnet 4.6 | Gemini 3 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Dec 2025 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.500 |
| Output $/1M | $15.00 | $3.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| 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 | 70.15% | 74.63% |
| Avg Response Time | 4.24s | 9.85s |
| Median input tokensincl. image tokens | 2.2K | 1.1K |
| Median output tokens | 105 | 290 |
| Est. cost / taskon this benchmark | $0.0080 | $0.0014 |
| Defect Detection | 80%(12/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 30%(3/10) | 30%(3/10) |
| Object Understanding | 71.4%(10/14) | 85.7%(12/14) |
| Spatial Understanding | 78.9%(15/19) | 84.2%(16/19) |
| OCR | ||
| Overall Score | 86.82% | 93.01% |
| Avg Response Time | 3.27s | 12.40s |
| Median input tokensincl. image tokens | 702 | 1.1K |
| Median output tokens | 84 | 160 |
| Est. cost / taskon this benchmark | $0.0034 | $0.0010 |
| Focused Scene OCR | 85.9%(85/99) | 94.9%(94/99) |
| Handwritten Math | 100%(10/10) | |
| License Plate Recognition | 90%(27/30) | 100%(30/30) |
| Text Recognition | 86.7%(26/30) | |
| VQA & Extraction | 88.3%(53/60) | |
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