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title: Agentic Version Control Benchmarks | daily.dev
description: GitButler benchmarked its CLI against Git and Jujutsu for agentic version control tasks using Claude Code and Codex. Across 300 graded runs covering squashing,...
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og:description: GitButler benchmarked its CLI against Git and Jujutsu for agentic version control tasks using Claude Code and Codex. Across 300 graded runs covering squashing,...
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# Agentic Version Control Benchmarks

**[GitButler Blog](https://daily.dev/sources/gitbutler)** · 3 min read · 1 upvotes · 0 comments

## Summary

GitButler benchmarked its CLI against Git and Jujutsu for agentic version control tasks using Claude Code and Codex. Across 300 graded runs covering squashing, amending, splitting, and committing, GitButler completed tasks ~60% faster than Git with ~80% fewer commands. Jujutsu was slower than Git. The benchmark used a deterministic grader checking final Git state, with raw git write commands blocked in non-Git runs to prevent fallback cheating.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.gitbutler.com/vcbench>

## Questions this post answers

### How does GitButler's CLI compare to Git and Jujutsu when used by AI coding agents like Claude Code or Codex?

GitButler ran about 60% faster than Git while using roughly 80% fewer commands to complete version control tasks, whereas Jujutsu ran slower than Git. The comparison used 300 graded runs across five scenarios (like squashing, splitting, and amending commits), tested with Claude Code on claude-opus-4-8 and Codex on gpt-5.5, with raw git write commands blocked to prevent fallback cheating.

_Choosing a version-control tool for agent workflows is easier with benchmarks like this on daily.dev._

### What methodology was used to benchmark version control tools for AI coding agents?

Two coding agents (Claude Code and Codex) were tested against three toolsets (Git, Jujutsu, GitButler) with ten runs each, totaling 300 graded runs across five version-control scenarios. No code was written during runs; agents received plain text commands like squashing or amending, and a deterministic script graded only the final Git state, accepting any command sequence producing correct history.

_Developers evaluating agent-tooling setups can dig into benchmark methodology details on daily.dev._

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#git](https://daily.dev/tags/git), [#version-control](https://daily.dev/tags/version-control)

[View this post on daily.dev](https://daily.dev/posts/agentic-version-control-benchmarks-pg2mbs3vw)

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