Gemini 1.5 Pro vs Claude 3.5 Sonnet for Multi-File Codebase Refactoring: Does 2M Context Beat Better Reasoning?

Not long ago, refactoring a legacy codebase with an LLM was a tedious exercise in copy-pasting. You would isolate two or three files, feed them to the model, ask for changes, manually stitch the code back together, and pray that you didn't break an undocumented dependency elsewhere in the system.

Today, the frontier models have shifted the goalposts. We now have engines capable of digesting entire repositories in a single prompt. But this has set up a massive architectural clash of philosophies: Google's Gemini 1.5 Pro, which boasts an astronomical 2-million-token context window, versus Anthropic's Claude 3.5 Sonnet, which caps out at a much smaller 200k tokens but is widely considered the smartest coder in the room.

If you have a messy legacy codebase that needs a major structural overhaul, which approach actually works? Does Gemini’s ability to see everything at once triumph, or does Claude’s superior reasoning engine make up for its smaller lung capacity?

Claude 3.5 Sonnet: The Precision Scalpel

Claude 3.5 Sonnet is the current gold standard for software engineering tasks. Its reasoning capabilities are exceptionally sharp, particularly when it comes to understanding abstract architectural patterns, identifying subtle edge cases, and outputting idiomatic, production-ready code.

However, Sonnet's 200k context window is a hard boundary. While 200,000 tokens is roughly equivalent to 150,000 words (plenty for mid-sized projects), a modern web application with dozens of components, database schemas, utility files, and configuration scripts will quickly push past this limit.

To make Claude work on a larger codebase, you have to be clever. You cannot simply dump the entire project into the prompt. Instead, you must use tools to prune your codebase down to the essential paths. If you want to see how to build a tool that automates this exact process, check out our guide on how to build a codebase context packer for Claude using Python.

• Strengths: Sonnet writes incredibly clean code. It rarely hallucinating functions, respects modern language features, and naturally follows complex system design instructions without needing to be babysat.
• Weaknesses: If your refactor requires touching a service file that depends on five other modules, and you couldn't fit those modules into the 200k limit, Claude will make assumptions—often resulting in import errors or misaligned type definitions. For debugging tips when Claude misses context, check our Claude articles hub.

Gemini 1.5 Pro: The Infinite Bucket

Gemini 1.5 Pro’s 2-million-token context window is, frankly, mind-boggling. You can zip up an entire legacy Node.js backend, complete with its package locks, database migrations, and unit tests, and upload the whole thing directly into the prompt.

This completely changes how you approach the problem. You don't need to spend time choosing which helper files are relevant or map out your imports for the model. You simply hand Gemini the keys to the entire house and say: "Rewrite our entire database access layer to use Prisma instead of raw SQL queries."

Because Gemini has the entire codebase in memory, it can trace dependencies across your entire architecture in a way that feels almost magical. It knows exactly which controllers import the old DB utility and can rewrite them all in one go.

• Strengths: Absolute global context. It understands how a change in file A ripples through file Z, even if those files are separated by hundreds of thousands of lines of code.
• Weaknesses: While Gemini can see everything, its reasoning is occasionally less precise than Claude's. It has a tendency to output "lazy" code blocks—such as leaving // TODO: implement remaining endpoints placeholders in the middle of crucial files—especially as you approach the deeper end of that massive context window. For handling these output quirks, refer to our Gemini articles hub.

The Stress Test: Refactoring an Express App to FastAPI

To test how these models behave under pressure, we task them both with refactoring a legacy Node.js Express application (around 120,000 tokens of code spread across 40 files) into a modern, type-safe Python FastAPI backend.

With Claude 3.5 Sonnet, we have to feed the codebase in chunks. We pack the core database schemas and route definitions first, get Claude to generate the equivalent FastAPI models and router structures, and then iteratively feed it the controller logic. The resulting Python code is flawless—clean async handlers, excellent use of Pydantic for validation, and proper type-hinting. However, the process is highly manual and requires significant developer oversight to ensure no routes are missed during the piecemeal translation.

With Gemini 1.5 Pro, we upload the entire directory structure. We ask it to output the complete FastAPI replacement in a structured format. Gemini successfully maps out every single endpoint, maintaining the exact URL paths and HTTP methods across all forty files. It doesn’t miss a single controller. However, some of the generated Python code is distinctly sub-optimal. It occasionally uses synchronous database drivers where it should use async, and we have to manually fix several instances where it got lazy and left placeholder comments instead of fully writing out the validation logic.

Token Consumption and Cost Implications

This is where understanding what makes your infrastructure tick becomes critical. Processing millions of tokens is not cheap, and both platforms handle pricing and limits differently.

Gemini 1.5 Pro's massive context window is incredibly powerful, but running prompts with 1 million+ tokens gets expensive quickly. Fortunately, Google supports context caching, which significantly reduces the cost of subsequent queries on the same codebase.

Claude 3.5 Sonnet is highly cost-efficient for smaller, highly targeted prompts, but if you are constantly hitting its 200k limit, you will quickly run into Anthropic's strict rate limits and find your development workflow paused while you wait for your quota to reset.

The Verdict: How to Choose Your Coding Co-Pilot

• Use Gemini 1.5 Pro if your codebase is a sprawling web of legacy dependencies where you honestly don't know what might break if you change a single helper function. Its global awareness is an absolute lifesaver for identifying hidden couplings across large codebases.
• Use Claude 3.5 Sonnet if you have a clear, modular architecture and need the highest possible code quality. If you can fit the target module and its immediate dependencies into its 200k window, Claude will write cleaner, safer, and more performant code with far fewer syntax errors.