How to switch between Claude, GPT-4 and Gemini without losing your context

The context lock-in problem

When you've been working with an AI model for two hours on a codebase, you've built up a shared context: the model understands your architecture, your conventions, what you've already tried, and what the goal is. Switching models means rebuilding that context from scratch.

This is why most developers stay with one model even when another would produce better results for a specific task. The switching cost — re-explaining your entire project — isn't worth it.

The solution isn't to stop switching models. It's to make the context portable. If you can re-establish full project context in 10 seconds on any model, switching becomes free.

Why models differ — and why you'd want to switch

You might use Claude for debugging a tricky logic problem, then switch to GPT-4o to generate a full test suite, then switch to Gemini when your project grows beyond 128K tokens. Each switch currently costs you the context you've built up.

The portable context workflow

The key insight: your codebase is your context. If you can represent your codebase as a file — one file, complete, structured — you can paste that file into any model at the start of any conversation and instantly have full context.

This is what CODEXT produces. A single .txt file containing your entire project: file tree, all source files, all content. It's model-agnostic by design. Claude, GPT-4, Gemini, Mistral, Ollama — any model that accepts text input accepts this file.

How to generate a fresh context bundle

Re-bundle before each session. CODEXT takes about 3 seconds for most projects. A fresh bundle reflects your current codebase — the changes you made since the last session are included. Don't reuse bundles from yesterday; re-bundle.

Adapting the prompt for each model

The CODEXT output format is consistent across models, but a small framing difference improves results on each:

For Claude

For GPT-4o

For Gemini

Context window considerations by model

The practical constraint is your bundle's token count versus the model's context limit. Run CODEXT to get the estimate before choosing which model to use for a session:

• Under 100K tokens: Any model works. Use whichever is best for the task.
• 100K–200K tokens: Claude (200K limit). GPT-4o works but is near its limit — exclude test fixtures.
• 200K–1M tokens: Gemini 1.5 Pro is the only major model with sufficient context. Or bundle by subdirectory.
• Over 1M tokens: Bundle by concern. No model fits the full project. This is a monorepo.

Handling model-specific limitations

Each model has documented weaknesses that affect context-heavy sessions:

Context degradation — Models can lose track of details mentioned early in a very long context. If you notice the model forgetting files it saw earlier, re-paste the relevant section or start a fresh conversation with the full bundle.

Middle-of-context blindness — A known issue with some models: information in the middle of a long context is recalled less reliably than information at the start or end. CODEXT places the file tree at the top and individual files inline, which tends to put the most important structural information early.

Token limits mid-session — As a long conversation accumulates, you'll eventually hit the context limit. When this happens, start a new conversation with the bundle re-pasted. Don't try to continue a degraded session.

The model-agnostic principle

The AI model landscape changes fast. The model that's state-of-the-art today will be commoditized in 12 months. If your workflow is tied to a single model's interface or proprietary context format, you're betting on that model staying ahead.

A portable context format — plain text, structured, readable — means you're betting on your codebase, not on any model. The bundle you create today works with models that don't exist yet. That's the correct abstraction layer for a workflow that needs to survive model generation changes.