How to estimate if your codebase fits an AI model's context window before pasting

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Developers feeding entire codebases into AI models often hit context window limits, causing truncation errors or silent data loss where the model answers from incomplete information. A practical workaround involves estimating token count offline using a formula that blends character count and word/symbol runs, achieving roughly 5–10% accuracy compared to real tokenizers. Since context windows differ significantly across models — 200K for Claude, 400K for GPT-5, and 1M for GPT-4.1 and Gemini 2.5 Pro — developers should budget their code bundle against the specific model being used. When a repository is too large, the recommended approach is to omit the largest file bodies first while keeping all filenames listed, so the model retains a full project map. An open-source CLI tool called ctxpack automates this trimming process and is available free under the MIT license on GitHub.

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