What does Greptile do that is different from Claude Code or Codex?

Greptile continuously evolves as it sees new bugs

Your Codex or Claude instance is only exposed to new types of bugs as they come up in your codebase.

Greptile reviews and tests millions of pull requests across tens of thousands of companies. This range exposes types of bugs and model errors that one company's code may never reveal.

As a result, we are able to make Greptile very good at catching a very wide range of bug types.

We maximize price/performance through optimizing subtask-level model routing

Thanks to Greptile’s expansive eval set, we are constantly optimizing tools and models for every subtask within the review process so we can provide outsized value at a predictable fixed cost per review, particularly compared to Claude/Codex agents.

For instance, the review agent needs to filter through hundreds of files to identify which ones might be relevant to review a diff. This is very token intensive due to the large number of file reads but doesn't require frontier intelligence. It does, however, need to be fast. To accomplish this, we may run a small open source model on Cerebras hardware. On the other hand, identifying edge cases to test during E2E testing does require high intelligence, so we use frontier models for that subtask.

Because we review and test so millions of changes a week, we can find opportunities for for optimization without causing regressions. As a result, Greptile's review agent is drastically more cost efficient for the same performance level than a pure frontier model agents.

We deeply understand the product, business, and codebase

Teams can give Greptile access to Jira/Notion/Linear. They can link Greptile to the docs in the repo. Greptile uses these to understand what you are trying to achieve.

We’ve also spend a lot of time building better ways to index codebases. Today, Greptile generates and maintains a large internal knowledge base about the codebase. What it does, how it works, how it’s architected, how its connected to other parts of the codebase, bugs that have been introduced in the past here, and potential sources of risk.

A monorepo for a medium-sized company could yield hundreds of pages of content for the knowledge base. This makes Greptile much more context-aware.

We run the code and simulate users to test the application through browser/mobile agents, simulation of production traffic on backend APIs etc.

At its core, Greptile has to tell you whether or not a change will break something for users. Theoretically, very smart models with enough inference might be able to determine this by simply reading the code, but we find that a much more predictable and efficient way to do this is to install the dependencies, run the dev server, user browser agents to click around the app, simulate production traffic on the APIs, and write and run ephemeral test scripts.