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
| Property | Claude Haiku 4.5 | docTR |
|---|---|---|
| Organization | Anthropic | Mindee |
| Category | closed | open |
| Modality | multimodal | vision |
| Release Date | Oct 2025 | Feb 2021 |
| Context Window | 200K | — |
| Parameters | Unknown | Unknown |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | No published price |
| Output $/1M | $5.00 | No published price |
| Vision Tasks | ||
| OCR | Demo | Supported |
| Captioning | Demo | Not listed |
| Chart Question Answering | Supported | Not listed |
| Classification | Demo | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Object Detection | Demo | Not listed |
| Vision Language | Supported | Not listed |
| Visual Question Answering | Demo | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Claude Haiku 4.5 vs docTR: Overview
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 (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.