Claude Opus 4.6 vs Gemini 3 Flash
Compare Claude Opus 4.6 and Gemini 3 Flash side-by-side. See how these vision models stack up in Open Prompt, OCR, Object Detection, Classification, and Image Captioning.
Compare Claude Opus 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 Opus 4.6 vs Gemini 3 Flash: Overview
Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.
As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.
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 Opus 4.6 vs Gemini 3 Flash Comparison Table
| Property | Claude Opus 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 | $5.00 | $0.500 |
| Output $/1M | $25.00 | $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 | 64.18% | 74.63% |
| Avg Response Time | 23.35s | 9.85s |
| Median input tokensincl. image tokens | 2.2K | 1.1K |
| Median output tokens | 130 | 290 |
| Est. cost / taskon this benchmark | $0.014 | $0.0014 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 20%(2/10) | 30%(3/10) |
| Object Understanding | 71.4%(10/14) | 85.7%(12/14) |
| Spatial Understanding | 68.4%(13/19) | 84.2%(16/19) |
| OCR | ||
| Overall Score | 82.53% | 93.01% |
| Avg Response Time | 5.05s | 12.40s |
| Median input tokensincl. image tokens | 736 | 1.1K |
| Median output tokens | 99 | 160 |
| Est. cost / taskon this benchmark | $0.0062 | $0.0010 |
| Focused Scene OCR | 85.9%(85/99) | 94.9%(94/99) |
| Handwritten Math | 70%(7/10) | 100%(10/10) |
| License Plate Recognition | 90%(27/30) | 100%(30/30) |
| Text Recognition | 80%(24/30) | 86.7%(26/30) |
| VQA & Extraction | 76.7%(46/60) | 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