LightOnOCR-3

Best OCR model. LightOnOCR-3-4B is the largest and most accurate model of the LightOnOCR-3 family, LightOn’s new generation of lightweight end-to-end OCR models with layout and visual understanding. We recommend it for most OCR tasks.

Same architecture as LightOnOCR-2-1B, new capabilities. LightOnOCR-3-1B keeps the architecture of LightOn’s previous LightOnOCR-2 models, so switching requires no change, and adds the new grounding, image description and chart extraction features.

Smallest model. LightOnOCR-3-0.8B is the lightest model of LightOn’s LightOnOCR-3 family, with the same layout and visual understanding features.

About LightOnOCR-3

LightOnOCR-3 is a new family of highly performant lightweight OCR models. Compared to the previous generation, they bring significant improvements in speed and transcription quality and introduce new visual understanding features: the models now output bounding box coordinates with labels for all visual elements of a document, short descriptions of images, and the numerical data of figures and charts. With these capabilities, they offer a ready-to-use, easier-to-maintain alternative to complex document understanding pipelines.

The models come in three sizes. The 1B keeps the LightOnOCR-2-1B architecture, while the 0.8B and 4B adopt the Qwen3.5 vision-language architecture, which simplifies integration with existing tools and brings a significant speed-up. All models are released under the Apache 2.0 license for research and commercial use.

Highlights

• 📝 Transcription mode: call the model with an empty prompt and get the full page text, as before. Switching from LightOnOCR-2 requires no change
• 📍 Grounding mode: call it with the grounding prompt and every block comes back with a label and a bounding box
• 🖼️ Visual understanding: images get a short description, charts become a table of their data points
• 🧠 End-to-End: one model instead of a document understanding pipeline, easier to deploy and maintain
• 🧾 Versatile: handles tables, receipts, forms, charts, multi-column layouts, handwriting, and math notation

Usage with Ollama

4B

ollama run maternion/LightOnOCR-3:4b --think=false

1B

ollama run maternion/LightOnOCR-3:1b

0.8B

ollama run maternion/LightOnOCR-3:0.8b --think=false

Benchmark Results

Our main point of comparison is top open-source and open-weight OCR models that can be downloaded and run locally. Scores are reported by category; higher is better within each table. Bold marks the best score in each column.

olmOCR-Bench

On olmOCR-Bench, LightOnOCR-3-4B scores 86.3 overall, 1.3 points behind Infinity Parser Pro, which has 35.1B listed parameters. At the same listed 4B size, it scores 0.5 points above Chandra 2. The 4B model leads on ArXiv (91.1), while the 1B variant leads on multi-column pages (85.9) and the 0.8B variant leads on long tiny text (94.1). Infinity Parser Pro remains stronger on old scans and headers/footers. Overall, the 0.8B variant scores 85.5 and the 1B variant scores 84.5.

* The LightOnOCR-2-1B overall score (83.2) excludes headers/footers; the other overall scores include that category. Its score is therefore not directly comparable with the other rows in this column.

ParseBench

On ParseBench, the 4B and 0.8B variants rank first and second on the five-category overall score (75.1 and 74.6), narrowly ahead of Infinity Parser Pro (74.3). The 4B model leads on charts (66.1) and semantic formatting (67.6); Infinity Parser Pro leads on visual grounding (74.9). The 1B variant scores 71.4 overall, with most of its gap to the 0.8B variant coming from charts and visual grounding which highlights the benefits of starting from pre-trained VLMs for visual categories.

fr-bench-pdf2md

On the French-document fr-bench-pdf2md benchmark, LightOnOCR-3-4B leads overall at 74.1, 3.6 points above the 0.8B variant at 70.5; the 1B variant scores 69.6. The 4B model is strongest on handwritten pages (46.1), forms (49.6), and multi-column layouts (83.6). Other models retain category leads: Chandra 2 on graphics and the second long-table category, and Infinity Parser Pro on tiny text. This spread shows why the overall score alone does not describe every document type.

Model Variants

Prompt Modes

The models can be used as before in a transcription-only mode: called with an empty prompt (""; the image only), they output all textual elements of the page as markdown. If you are currently using our previous models, switching to LightOnOCR-3 requires no change, as this is the default behavior.

The new usage mode is to call the models with the grounding prompt. They then output the new vision features alongside the transcribed text. Each block of content is prepended with a placeholder giving its type and its bounding box, in page coordinates normalized to 0–1000:

![title](80,70,920,140)
# Company report 2025

![text](80,175,920,225)
Revenue rose 20%.

![image](80,285,460,545)
A solar-powered factory.

![chart](540,285,920,545)
<table>
  <tr><th>Year</th><th>Revenue</th></tr>
  <tr><td>2024</td><td>€10M</td></tr>
  <tr><td>2025</td><td>€12M</td></tr>
</table>

• Text blocks contain the transcribed content of paragraphs, titles and other text elements.
• Image blocks pair a bounding box with a short description, making visual content accessible to retrieval and question-answering pipelines.
• Chart blocks contain an HTML table of the data points extracted from the figure, turning visual information into structured data.

The placeholder label is one of:

Grounding adds about 25% output tokens over plain transcription (1,433 vs 1,158 tokens per page on average for the 4B). Other instructions are out of distribution: use the empty prompt ("") or grounding.

Rendering and Preprocessing Tips

• Maintain aspect ratio to preserve text geometry

License

Apache License 2.0

Citation

@misc{lightonocr2_2026,
  title        = {LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR},
  author       = {Said Taghadouini and Adrien Cavaill\`{e}s and Baptiste Aubertin},
  year         = {2026},
  howpublished = {\url{https://arxiv.org/abs/2601.14251}}
}