Claude Opus 4.6 vs Gemini 2.5 Pro
Compare Claude Opus 4.6 and Gemini 2.5 Pro 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 2.5 Pro 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 2.5 Pro: 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 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.
Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.
Claude Opus 4.6 vs Gemini 2.5 Pro Comparison Table
| Property | Claude Opus 4.6 | Gemini 2.5 Pro |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Jun 2025 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $1.25 |
| Output $/1M | $25.00 | $10.00 |
| 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% | 70.15% |
| Avg Response Time | 23.35s | 11.87s |
| Median input tokensincl. image tokens | 2.2K | 294 |
| Median output tokens | 130 | 565 |
| Est. cost / taskon this benchmark | $0.014 | $0.0060 |
| 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) | 20%(2/10) |
| Object Understanding | 71.4%(10/14) | 78.6%(11/14) |
| Spatial Understanding | 68.4%(13/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 82.53% | 78.6% |
| Avg Response Time | 5.05s | 4.91s |
| Median input tokensincl. image tokens | 736 | 290 |
| Median output tokens | 99 | 323 |
| Est. cost / taskon this benchmark | $0.0062 | $0.0036 |
| Focused Scene OCR | 85.9%(85/99) | 78.8%(78/99) |
| Handwritten Math | 70%(7/10) | 80%(8/10) |
| License Plate Recognition | 90%(27/30) | 90%(27/30) |
| Text Recognition | 80%(24/30) | 73.3%(22/30) |
| VQA & Extraction | 76.7%(46/60) | 75%(45/60) |
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