CodeFormula
docling-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 21
CodeFormulaV2
docling-projectThe **Code Formula Model** processes an image of a code snippet or formula at 120 DPI and outputs its content. - **Code Snippets**: The model identifies the programming language and outputs the code repsecting the indendation shown in the given image. The output format will be: " \_> \ " Example: " System.out.println("Hello World.");" This model was trained using the following two datasets: 1. https://huggingface.co/datasets/ds4sd/SynthFormulaNet 2. https://huggingface.co/datasets/ds4sd/SynthCodeNet
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DeskForge-Gemma4-E4B
docling-project**DeskForge-Gemma4-E4B** is Gemma 4 E4B (instruction-tuned) fine-tuned on 200K grounding examples from DeskForge-1M. Given a desktop screenshot and a natural-language instruction, it answers with the next action as a `pyautogui` call whose coordinates are fractions of the screen. **Live demo** · **Project page** · **Dataset** · **Code** · **Paper** · Also available: DeskForge-Qwen3.5-4B **Grounding on DeskForge-1M held-out conditions** (accuracy, %). New Scenes are unseen scenes with seen attributes; Theme, App and Resolution hold an appearance preset, three applications and a display resolution out of training entirely.
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DeskForge-Qwen3.5-4B
docling-project**News — v1.1:** Updated fine-tuning with additional GUI workflows improves performance on all five external grounding benchmarks. The original paper checkpoint remains available at revision `v1.0`. **DeskForge-Qwen3.5-4B** is Qwen3.5-4B fine-tuned for desktop GUI grounding and next-action prediction using grounding and GUI-workflow supervision. Given a desktop screenshot and a natural-language instruction, it answers with the next action as a `pyautogui` call whose coordinates are fractions of the screen. **Live demo** · **Project page** · **Dataset** · **Code** · **Paper** · Also available: DeskForge-Gemma4-E4B
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗docling-layout-egret-large
docling-project📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, 🔗 https://arxiv.org/abs/2509.11720
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗docling-layout-egret-medium
docling-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗docling-layout-egret-xlarge
docling-project📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, 🔗 https://arxiv.org/abs/2509.11720
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗docling-layout-heron
docling-project🚀 **`heron`** is the **default layout analysis model** of the Docling project, designed for robust and high-quality document layout understanding. 📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, 🔗 https://arxiv.org/abs/2509.11720
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗docling-layout-heron-101
docling-project🚀 **`heron-101`** is a Document Layout Analysis Model used in the Docling project. 📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, 🔗 https://arxiv.org/abs/2509.11720
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗docling-layout-heron-onnx
docling-projectThis is an ONNX export of the docling-layout-heron model. Please refer to the original model card for more details.
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗docling-layout-old
docling-project`docling-layout-old` is the old Layout Model of Docling project. This model uses the RT-DETR architecture and has been trained from scratch on a variety of document datasets.
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗docling-models
docling-projectThis page contains models that power the PDF document converion package docling. The layout model will take an image from a page and apply RT-DETR model in order to find different layout components. It currently detects the labels: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. As a reference (from the DocLayNet-paper), this is the performance of standard object detection methods on the DocLayNet dataset compared to human evaluation, The tableformer model will identify the structure of the table, starting from an image of a table. It uses the predicted table regions of the layout model to identify the tables. Tableformer has SOTA table structure identification,
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DocumentFigureClassifier
docling-projectThis is an image classification model based on **Google EfficientNet-B0**, fine-tuned to classify input images into one of the following 16 categories: 1. **bar_chart** 2. **bar_code** 3. **chemistry_markush_structure** 4. **chemistry_molecular_structure** 5. **flow_chart** 6. **icon** 7. **line_chart** 8. **logo** 9. **map** 10. **other** 11. **pie_chart** 12. **qr_code** 13. **remote_sensing** 14. **screenshot** 15. **signature** 16. **stamp**
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DocumentFigureClassifier-v2.0
docling-projectThis is an image classification model based on **Google EfficientNet-B0**, fine-tuned to classify input images into one of the following 26 categories: 1. **logo** 2. **photograph** 3. **icon** 4. **engineering_drawing** 5. **line_chart** 6. **bar_chart** 7. **other** 8. **table** 9. **flow_chart** 10. **screenshot_from_computer** 11. **signature** 12. **screenshot_from_manual** 13. **geographical_map** 14. **pie_chart** 15. **page_thumbnail** 16. **stamp** 17. **music** 18. **calendar** 19. **qr_code** 20. **bar_code** 21. **full_page_image** 22. **scatter_plot** 23. **chemistry_structure** 24. **topographical_map** 25. **crossword_puzzle** 26. **box_plot**
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DocumentFigureClassifier-v2.5
docling-projectThis is an image classification model based on **Google EfficientNet-B0**, fine-tuned on a subset of the subset of HuggingFace/finepdfs to classify document figures into one of the following 26 categories: 1. **logo** 2. **photograph** 3. **icon** 4. **engineering_drawing** 5. **line_chart** 6. **bar_chart** 7. **other** 8. **table** 9. **flow_chart** 10. **screenshot_from_computer** 11. **signature** 12. **screenshot_from_manual** 13. **geographical_map** 14. **pie_chart** 15. **page_thumbnail** 16. **stamp** 17. **music** 18. **calendar** 19. **qr_code** 20. **bar_code** 21. **full_page_image** 22. **scatter_plot** 23. **chemistry_structure** 24. **topographical_map** 25. **crossword_puzzle** 26. **box_plot** **Note:** This model uses the same architecture and implementation as v2.0. The improved performance is achieved by training on a dataset that is 10 times larger than the one used for v2.0.
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗granite-docling-2stage-258m
docling-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗ScreenParser
docling-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗ScreenVLM
docling-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗SmolDocling-256M-preview
docling-project📢 New Release: We’ve released granite-docling-258M , the successor to SmolDocling . It will now receive updates and support, check it out! SmolDocling-256M-preview SmolDocling is a multimodal Image-Text-to-Text model designed for efficient document conversion. It retains Docling's most popular features while ensuring full compatibility with Docling through seamless support for DoclingDocuments . This model was presented in the paper SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion.
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗SmolDocling-256M-preview-mlx-bf16
docling-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗TableFormerV2
docling-project> [!WARNING] > **Preview model.** TableFormerV2 is a preview release. Its weights, outputs, and > integration options can change in future releases without notice. The default > table structure model in Docling is still > TableFormer V1. TableFormerV2 is a table structure recognition model for Docling. It takes an image of a table and predicts its structure as an OTSL token sequence, together with a bounding box for each cell. Docling then maps the cells back to the text of the document to build a `TableItem` in the `DoclingDocument`. Docling downloads the weights from this repository automatically the first time you use the model. To download them before you run a conversion (for example, for offline use), run:
Input Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.