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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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AnthropicClaude Sonnet 4.5
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GoogleGoogle Vision OCR
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Models in this comparison

Claude Sonnet 4.5 vs Google Vision OCR: Overview

Claude Sonnet 4.5

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

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

PropertyClaude Sonnet 4.5Google Vision OCR
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalvision
Release DateSep 2025Feb 2016
Context Window200K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00
Output $/1M$15.00
Vision Tasks
OCRDemoDemo
CaptioningDemo
ClassificationDemo
Object DetectionDemo
Vision Language
Visual Question AnsweringDemo
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 Time5.67s
Median input tokensincl. image tokens2.2K
Median output tokens182
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 Time3.93s
Median input tokensincl. image tokens735
Median output tokens115
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