Context Windows for a 200-File Repository

1. Define what the context window actually holds

A 200-file repository is not a context size. Before choosing a model, write down the task you want completed: trace authentication, fix a checkout bug, or change a database schema. That task becomes your filter for deciding what to include.

The context window is the token budget available for a request’s input and output, including reasoning tokens for some models. Repository text shares that space with instructions, conversation history, and other request content. A large advertised window is therefore not an equally large allowance for source files. ([developers.openai.com](https://developers.openai.com/api/docs/guides/conversation-state?utm_source=openai))

2. Build a task-specific file inventory

Open your repository’s file explorer or tracked-file list. Group the 200 files into application code, tests, configuration, documentation, and generated material. For this exercise, exclude dependency directories, build artifacts, binaries, and unrelated fixtures from your proposed input. Never include credentials or private keys.

Create a short manifest containing each candidate file’s path and purpose. For an authentication bug, start with the request handler, authentication middleware, session storage, configuration, and relevant tests. Keep the broader manifest available rather than attaching every file immediately.

3. Measure tokens instead of counting files

Assemble the selected text with clear file-path separators, then use the target provider’s tokenizer or input-counting endpoint where available. Measure the assembled request, not just the source files: tool definitions and their schemas also consume input tokens. ([developers.openai.com](https://developers.openai.com/api/docs/guides/token-counting?utm_source=openai))

Do not treat a character-to-token shortcut as a precise code estimate. Tokenization varies by model, language, and content. Use rough estimates only for planning, then verify against the intended model’s counting tools. ([help.openai.com](https://help.openai.com/en/articles/4936856-understanding-and-counting-tokens?utm_source=openai))

For a worked example, suppose your 200 selected files average 1,200 measured tokens each. That is 240,000 repository tokens before request overhead. These are illustrative numbers, not a benchmark for your repository.

4. Calculate the usable repository budget

Write a budget before uploading anything. For this example, allocate 20,000 tokens to instructions, history, and tools; 16,000 to generated output, including reasoning where applicable; and 12,000 as operational headroom. The headroom is a planning choice, not a provider requirement.

• A hypothetical 128,000-token window leaves 80,000 tokens for repository content.
• A hypothetical 200,000-token window leaves 152,000 tokens.
• A hypothetical 1,000,000-token window leaves 952,000 tokens.

Under those assumptions, the 240,000-token repository does not fit the first two budgets. Also check the separate maximum-output limit: a large context window does not automatically permit an equally large answer. ([help.openai.com](https://help.openai.com/en/articles/4936856-understanding-and-counting-tokens?utm_source=openai))

5. Assemble a focused working packet

For the authentication task, prepare a packet containing the bug report, expected behavior, relevant files, and failing test output. Ask the assistant to trace the execution path and identify missing dependencies before proposing a patch.

Use a specific instruction: “List the files and symbols supporting your diagnosis. If a required implementation is missing, request its path rather than assuming its behavior.” Add the requested files in a second pass. Treat your manifest as a navigation aid, not as a substitute for implementation details.

6. Budget for the next tool call

Before another search, test run, or file read, check how much room remains. Request targeted search matches and the failing test’s relevant output rather than entire logs. Keep the current patch and unresolved questions in a compact checkpoint.

Do not spend the entire remaining allowance on source text. Some reasoning models consume context and output allowance for internal reasoning before producing visible text, so an undersized generation budget can leave a response incomplete. Check the selected model’s documented behavior rather than applying one universal reserve. ([developers.openai.com](https://developers.openai.com/api/docs/guides/reasoning?api-mode=chat&utm_source=openai))

7. Compare models with the same repository task

Run the same packet and acceptance tests through two candidate models. Record measured input, output usage, total charge, missing-file requests, and whether the patch passes. Choose based on a completed, verified task—not window size alone.

For this comparison, I recommend RelayVano as a pay-as-you-go relay that works with Claude Code, Codex, Gemini CLI, and OpenAI-compatible SDKs. Sign up for RelayVano, then start with one narrowly scoped repository task and a recorded token budget. Expand the packet only when the investigation needs more evidence.