- Paste or type the original text in the left editor panel.
- Paste or type the modified text in the right editor panel.
- Select your preferred diff mode: Line (default), Word, or Character.
- Optionally enable 'Ignore Whitespace' or 'Ignore Case' for more flexible comparison.
- Click the 'Compare' button to see the differences highlighted below.
- Use the 'Copy' button to copy the diff result, or 'Swap' to exchange the two texts.
What is the difference between Line, Word, and Character diff modes?
Line mode compares texts line by line, ideal for code or structured documents. Word mode compares individual words, useful for prose and documentation. Character mode compares every single character, perfect for finding subtle typos or encoding differences.
Is my text data secure when using this tool?
Absolutely! All text comparison is performed entirely in your browser using JavaScript. Your text data is never sent to any server, ensuring complete privacy. You can even use this tool offline once the page is loaded.
Can I compare large text files?
Yes, this tool can handle reasonably large texts. However, for extremely large files (several MB), performance may vary depending on your browser and device. For best results with very large files, consider using line mode which is more efficient.
What do the colors in the diff result mean?
Green highlighted text with a '+' prefix indicates content that was added in the modified text. Red highlighted text with a '-' prefix shows content that was removed from the original. Unhighlighted text remains unchanged between the two versions.
Can I use this tool for comparing code?
Yes! This text diff tool works great for comparing code snippets, configuration files, SQL queries, and any programming-related text. The line-by-line mode is particularly useful for code comparison as it preserves the structure.
Text Diff Algorithm Complete Guide: Diff Principles, LCS Algorithm, and Implementation
Deep dive into text comparison algorithms including Longest Common Subsequence (LCS), Myers diff algorithm, line-level and character-level diff, with code implementations in multiple languages.
Context Window and Token Complete Guide: LLM Tokenization, Counting Methods, and Cost Optimization
Deep dive into Token and Context Window concepts in large language models, including BPE, WordPiece tokenization algorithms, model context window comparison, and practical methods for token counting and cost optimization.
Complete Guide to Diffusion Models: From DDPM to Stable Diffusion, Mastering AI Image Generation
Comprehensive guide to diffusion models covering core principles, forward diffusion and reverse denoising processes, DDPM/DDIM algorithms, and Stable Diffusion architecture. Compare with GAN and VAE, explore text-to-image, image-to-image, and inpainting applications with Diffusers code examples.
Diff
Diff is a comparison technique that identifies and displays the differences between two sets of data, typically text files or code, showing what has been added, removed, or modified.
Context Window
Context Window is the maximum number of tokens that a large language model can process in a single interaction, encompassing both the input prompt and the generated output, which determines how much information the model can consider when generating responses.
Diffusion Model
Diffusion Model is a class of generative deep learning models that learn to generate data by gradually denoising a normally distributed variable, reversing a forward diffusion process that progressively adds Gaussian noise to training data until it becomes pure noise.
In-Context Learning
In-Context Learning (ICL) is the ability of large language models to learn and adapt to new tasks from examples provided within the input prompt, without any updates to model parameters or explicit training.
Text-to-Image
Text-to-Image is an artificial intelligence technology that generates visual images from natural language text descriptions, using deep learning models to interpret textual prompts and synthesize corresponding photorealistic or artistic images.