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Claude Haiku 4.5 vs docTR

Compare Claude Haiku 4.5 and docTR side-by-side.

Compare Claude Haiku 4.5 vs docTR live

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Models in this comparison

Claude Haiku 4.5 vs docTR Comparison Table

Evals updated October 8, 2026Pricing updated October 10, 2026

PropertyClaude Haiku 4.5docTR
OrganizationAnthropicMindee
Categoryclosedopen
Modalitymultimodalvision
Release DateOct 2025Feb 2021
Context Window200K—
ParametersUnknownUnknown
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$1.00No published price
Output $/1M$5.00No published price
Vision Tasks
OCRDemoSupported
CaptioningDemoNot listed
Chart Question AnsweringSupportedNot listed
ClassificationDemoNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
Object DetectionDemoNot listed
Vision LanguageSupportedNot listed
Visual Question AnsweringDemoNot listed
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot listed

Claude Haiku 4.5 vs docTR: Overview

Claude Haiku 4.5

Claude Haiku 4.5 is Anthropic’s lightweight model in the Claude 4.5 series, released in October 2025 under a proprietary license. Designed for speed and cost efficiency, it delivers near-frontier performance while maintaining Anthropic’s AI Safety Level 2 standard. Haiku 4.5 supports both text and multimodal (text and image) inputs, integrates tool use and extended reasoning, and features a 200,000 token context window, making it adept at handling long or complex workflows. Though the parameter count remains undisclosed, it achieves about 73.3% on SWE-bench Verified, reflecting strong coding and reasoning ability. Haiku 4.5 is ideal for developers and researchers seeking rapid, cost-effective model calls for analysis, coding, or multimodal understanding.

docTR

docTR (Document Text Recognition) is an open-source OCR toolkit developed by Mindee, with its initial public release in March 2021 under the Apache 2.0 license. It provides end-to-end document text recognition through a two-stage pipeline consisting of text detection and text recognition, both implemented as deep learning models. docTR supports multiple detection architectures including DBNet and LinkNet, and recognition architectures including CRNN and SAR, with both TensorFlow and PyTorch backends available.

docTR is designed for reading text in document images including scanned PDFs, photographs of printed documents, and forms. It handles multilingual text recognition across standard Latin-script languages and is deployable through Roboflow Inference. It is suited for document digitization pipelines, automated form processing, and applications requiring accurate structured text extraction from document images.