Claude Opus 4.7 vs Gemini 3.1 Pro
Compare Claude Opus 4.7 and Gemini 3.1 Pro side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Claude Opus 4.7 vs Gemini 3.1 Pro: Overview
Claude Opus 4.7 is a proprietary multimodal language model developed by Anthropic, released on April 16, 2026. It is designed for agentic coding, long-horizon task execution, and enterprise knowledge work. The model supports text and vision inputs and operates with a context window of up to 1,000,000 tokens. It introduces adaptive thinking, which dynamically allocates reasoning based on task complexity, along with configurable effort controls including a new xhigh setting that sits between the existing high and max levels. It achieves 87.6% on SWE-bench Verified and 78.0% on OSWorld-Verified, reflecting strong performance on autonomous software engineering and computer use tasks respectively.
Compared to Claude Opus 4.6, version 4.7 shows improved instruction following and higher reliability in extended agentic tasks. Vision capabilities now support high-resolution inputs up to 2,576px on the long edge (~3.75 megapixels), more than three times the resolution of prior Claude models, enabling finer interpretation of dense diagrams, UI screenshots, and document layouts. These improvements, combined with self-verification on long-running tasks and a new task budget system for controlling agentic loops, make it well-suited for complex software engineering, technical analysis, and multimodal vision workflows.
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
Claude Opus 4.7 vs Gemini 3.1 Pro Comparison Table
| Property | Claude Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Feb 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $2.00 |
| Output $/1M | $25.00 | $12.00 |
| 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 | 67.16% | 75.76% |
| Avg Response Time | 4.85s | 6.13s |
| Median input tokensincl. image tokens | 2.4K | 1.1K |
| Median output tokens | 110 | 11 |
| Est. cost / taskon this benchmark | $0.015 | $0.0024 |
| 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) | 44.4%(4/9) |
| Object Understanding | 85.7%(12/14) | 92.9%(13/14) |
| Spatial Understanding | 68.4%(13/19) | 73.7%(14/19) |
| OCR | ||
| Overall Score | 86.9% | 89.52% |
| Avg Response Time | 4.19s | 3.11s |
| Median input tokensincl. image tokens | 969 | 1.1K |
| Median output tokens | 81 | 12 |
| Est. cost / taskon this benchmark | $0.0069 | $0.0024 |
| Focused Scene OCR | 88.9%(88/99) | 94.9%(94/99) |
| Handwritten Math | 80%(8/10) | 90%(9/10) |
| License Plate Recognition | 93.3%(28/30) | 90%(27/30) |
| Text Recognition | 86.7%(26/30) | 86.7%(26/30) |
| VQA & Extraction | 81.7%(49/60) | 81.7%(49/60) |
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