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OpenAI's GPT-5.4 supports a massive 256K token context window, allowing you to feed entire codebases, books, or lengthy research papers into a single prompt. This makes it ideal for tasks that require long-range comprehension.

Meta's Llama 4 Scout model supports a record-breaking 10 million token context window. While this is impressive for research use cases, production inference at this context depth requires significant GPU memory and specialized hardware.

Test models with smaller context windows first to save on API costs during development.

For summarization tasks, shorter context windows help LLMs focus on key points.

Use Gemini's 1M+ token context window for processing entire codebases or long documents in one shot.

An LLM's context window is the amount of text (measured in tokens) it can consider at one time. Larger context windows allow for more complex tasks and longer conversations, but can also increase costs.

Different LLMs use different tokenizers. The same piece of text can result in a varying number of tokens depending on the model, which impacts cost and context window usage. Tools like TokenCalculator help you see these differences.

Understand the context window limit of the model you are using. Information outside this window will be ignored. For long interactions, summarization or RAG is key.

Different LLMs tokenize text differently. The same sentence can result in varying token counts across models, directly affecting API costs and context window usage.