Claude Sonnet 4.5 vs Google Vision OCR
Compare Claude Sonnet 4.5 and Google Vision OCR side-by-side. See how these vision models stack up in OCR.
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Claude Sonnet 4.5 vs Google Vision OCR: Overview
Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.
The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.
Google Vision OCR, released as part of the Cloud Vision API’s general availability in February 2016, is a proprietary Google Cloud service for extracting text from images and documents. It supports common formats like JPEG, PNG, GIF, TIFF, and PDF, and provides two main modes: TEXT_DETECTION for short snippets and scene text, and DOCUMENT_TEXT_DETECTION for dense documents, which returns structured layout information with bounding boxes.
While not an LLM (so it has no token context window or parameter count), the service performs OCR across printed text and some handwriting. It outputs detected text along with positional metadata, making it useful for digitizing scanned files, receipts, forms, and signs. However, complex layouts like tables often require downstream processing. Accessible via REST and RPC APIs, with client libraries in major languages, Google Vision OCR is widely used for document processing pipelines, archival, and accessibility applications.
Claude Sonnet 4.5 vs Google Vision OCR Comparison Table
| Property | Claude Sonnet 4.5 | Google Vision OCR |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | vision |
| Release Date | Sep 2025 | Feb 2016 |
| Context Window | 200K | — |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | |
| Output $/1M | $15.00 | |
| Vision Tasks | ||
| OCR | Demo | Demo |
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| Vision Language | ||
| Visual Question Answering | 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 | 59.7% | |
| Avg Response Time | 5.67s | |
| Median input tokensincl. image tokens | 2.2K | |
| Median output tokens | 182 | |
| Est. cost / taskon this benchmark | $0.0092 | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 10%(1/10) | |
| Object Understanding | 64.3%(9/14) | |
| Spatial Understanding | 63.2%(12/19) | |
| OCR | ||
| Overall Score | 67.25% | |
| Avg Response Time | 3.93s | |
| Median input tokensincl. image tokens | 735 | |
| Median output tokens | 115 | |
| Est. cost / taskon this benchmark | $0.0039 | |
| Focused Scene OCR | 71.7%(71/99) | |
| Handwritten Math | 20%(2/10) | |
| License Plate Recognition | 53.3%(16/30) | |
| Text Recognition | 66.7%(20/30) | |
| VQA & Extraction | 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