Best LLMs for OCR

LLMs such as Claude Fable 5 and Kimi K3 can go beyond text transcription to understand documents, answer questions, and return structured outputs. With Roboflow Playground, you can compare LLMs on your production images by accuracy, cost, and latency to find the best fit for your OCR use case. Optical character recognition (OCR) has traditionally relied on specialized computer vision models designed to detect and recognize text in images. Today, general-purpose multimodal large language models (LLMs) can also extract and interpret text from images, screenshots, scanned documents, and PDFs. Beyond text extraction, these models can interpret document content, preserve aspects of its structure, answer questions, summarize information, and return results in structured formats. This makes them particularly useful for workflows where OCR is just the first step in a larger process. In this guide, we will evaluate leading closed and open-weight LLMs for OCR, comparing their accuracy, cost, and inference speed. We will also show you how to test these models on your own images using Roboflow Playground (opens in a new tab) and integrate LLM-based OCR into computer vision pipelines with Roboflow Workflows and Roboflow Agent. LLMs vs. Dedicated OCR Models There are two broad approaches to using AI…