Claude Opus 4.7 vs Grok 4
Compare Claude Opus 4.7 and Grok 4 side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Claude Opus 4.7 vs Grok 4: 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.
Grok 4, released by xAI on July 9, 2025, is the fourth-generation model in the Grok family and the most advanced to date. It is multimodal, supporting text, vision, tool use, and real-time web search, with a reported 256,000-token context window for long-form reasoning and document analysis. Its training data extends through November 2024, making it the most up-to-date Grok model at launch.
The lineup includes Grok 4 Generalist for broad tasks, Grok 4 Heavy for higher-capacity reasoning, and Grok 4 Code optimized for programming and debugging. A notable feature is its always-on “Think” mode, designed for deeper multi-step reasoning. While xAI has not disclosed parameter counts, Grok 4 is positioned to compete with frontier models like GPT-5 and Claude 4, balancing real-time knowledge via web integration with structured tool use. It is best suited for coding, complex reasoning, and multimodal AI assistants.
Claude Opus 4.7 vs Grok 4 Comparison Table
| Property | Claude Opus 4.7 | Grok 4 |
|---|---|---|
| Organization | Anthropic | xAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Jul 2025 |
| Context Window | 1.0M | 256K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | |
| Output $/1M | $25.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | |
| Object Detection | 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% | 52.24% |
| Avg Response Time | 4.85s | 85.24s |
| Median input tokensincl. image tokens | 2.4K | |
| Median output tokens | 110 | |
| Est. cost / taskon this benchmark | $0.015 | |
| Defect Detection | 73.3%(11/15) | 80%(12/15) |
| Document Understanding | 77.8%(7/9) | 44.4%(4/9) |
| Object Counting | 20%(2/10) | 10%(1/10) |
| Object Understanding | 85.7%(12/14) | 57.1%(8/14) |
| Spatial Understanding | 68.4%(13/19) | 52.6%(10/19) |
| OCR | ||
| Overall Score | 86.9% | |
| Avg Response Time | 4.19s | |
| Median input tokensincl. image tokens | 969 | |
| Median output tokens | 81 | |
| Est. cost / taskon this benchmark | $0.0069 | |
| Focused Scene OCR | 88.9%(88/99) | |
| Handwritten Math | 80%(8/10) | |
| License Plate Recognition | 93.3%(28/30) | |
| Text Recognition | 86.7%(26/30) | |
| VQA & Extraction | 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