What Are Tokens and Why Do They Matter?
The fundamental unit of AI language models explained simply. How text becomes tokens and why every developer should understand them.
BasicsHow Tokenizers Work: BPE, WordPiece, and SentencePiece
A visual guide to the three main tokenization algorithms used by GPT, Claude, and Gemini — and why the same text produces different token counts.
CodeHow to Count Tokens Before Making an API Call
Practical code examples in Python, JavaScript, and Go to estimate token counts locally before sending requests to OpenAI, Anthropic, or Google.
Prompts10 Ways to Reduce Your Prompt Token Count
Concrete techniques to cut your system prompts and user messages by 30-50% without losing any instruction quality.
ModelsContext Windows Explained: From 4K to 10M Tokens
What context window actually means, how it affects your app, and a comparison of every major model's limit in 2026.
CostAI Token Pricing Compared: GPT vs Claude vs Gemini
A side-by-side cost breakdown of every major model. Find the cheapest option for your use case without sacrificing quality.
PromptsSystem Prompt Optimization: Same Instructions, Fewer Tokens
Your system prompt runs on every single request. Learn how to compress it by 40% and save thousands of dollars at scale.
BasicsWhy Non-English Text Uses More Tokens
Japanese, Arabic, Chinese, and other languages can use 2-4x more tokens than English for the same meaning. Here's why and what to do about it.
CostPrompt Caching: Cut Your Token Costs by 90%
OpenAI, Anthropic, and Google all offer prompt caching. Learn how to structure your requests to maximize cache hits and slash your bill.
RAGChunking Strategies for Long Documents
How to split large documents into token-aware chunks for RAG pipelines. Covers fixed-size, semantic, and recursive chunking with code examples.
CodeJSON vs YAML vs XML: Which Format Uses Fewer Tokens?
We tested the same data in three formats across four tokenizers. The results might change how you structure your API responses.
CostInput Tokens vs Output Tokens: Why Output Costs 3-6x More
Understanding the pricing asymmetry between input and output tokens, and how to design your prompts to minimize expensive output.
CodeHandling Token Limits Gracefully in Production
What happens when you exceed the context window? Error handling patterns, truncation strategies, and fallback logic for production apps.
PromptsFew-Shot Prompting Without Blowing Your Token Budget
Examples improve output quality but eat tokens fast. Learn how to pick the right number of examples and compress them effectively.
ModelsReasoning Tokens: The Hidden Cost of o3 and o4-mini
OpenAI's reasoning models use internal "thinking tokens" that don't appear in the output but still cost money. Here's how to account for them.
RAGEmbedding Tokens vs LLM Tokens: What's the Difference?
Embeddings and chat completions tokenize text differently and price it differently. A clear guide to both for RAG developers.
CodeHow to Count Tokens in Streaming Responses
When you stream responses, you don't get a token count upfront. Here's how to track usage in real time across OpenAI, Anthropic, and Google APIs.
ModelsHow Images Are Tokenized in Multimodal Models
GPT-4o, Claude, and Gemini all handle images differently. Learn how image resolution maps to token count and how to optimize visual inputs.
CostBatch API: Process Millions of Tokens at 50% Off
OpenAI's Batch API lets you queue requests and pay half price. When to use it, how to set it up, and the tradeoffs to consider.
CodeDesigning a Token Budget System for AI Applications
How to architect a token budget manager that tracks usage, enforces limits, and routes requests to the cheapest capable model automatically.