cto bench

Product Introduction

1. Definition: cto.bench is a real-world AI coding benchmark platform (technical category: AI performance evaluation tool) that measures Large Language Models (LLMs) on end-to-end coding tasks sourced directly from user activity on the cto.new platform.
2. Core Value Proposition: It exists to solve the disconnect between traditional AI benchmarks (using artificial tasks) and real-world performance. cto.bench provides actionable insights into how AI coding agents perform on actual development work queued by engineering teams, enabling data-driven model selection.

Main Features

1. Real-World Task Benchmarking: Measures LLM success rates based on merged code from genuine user tasks on cto.new. How it works: Tasks initiated by users (e.g., feature implementation, bug fixes) are tracked; success is defined as the code being merged. Technologies: Integrates with cto.new’s workflow automation and version control systems.
2. Dynamic Leaderboard: Ranks LLMs (e.g., Claude Sonnet, GPT-5.2) by their rolling 72-hour success rate. How it works: Aggregates task outcomes with a 2-day lag for resolution, updating continuously. Technologies: Real-time data pipelines and statistical significance filters (minimum usage threshold).
3. Production-Realistic Toolset Simulation: Evaluates models using tools mirroring developer workflows. How it works: Tests LLMs on tools like EditFile (code block replacement), GrepTool (regex search), TerminalTool (shell command execution), and GlobTool (file discovery) within isolated VM environments.

Problems Solved

1. Pain Point: Traditional benchmarks (e.g., HumanEval) fail to predict how AI agents handle actual engineering tasks in production environments, leading to poor tool selection.
2. Target Audience: Engineering leaders (CTOs, VPEs), AI integration specialists, and development teams using AI coding assistants (e.g., teams leveraging GitHub Copilot, Claude for code generation).
3. Use Cases:
   • Selecting the optimal LLM for a team’s specific codebase and workflow.
   • Validating AI agent performance before deployment in CI/CD pipelines.
   • Tracking model regression or improvement on real tasks over time.

Unique Advantages

1. Differentiation: Unlike synthetic benchmarks (e.g., SWE-bench), cto.bench uses exclusively real user tasks, offering performance data grounded in production scenarios. Competitors measure isolated coding puzzles; cto.bench measures end-to-end task completion.
2. Key Innovation: Its methodology directly ties success to merged code – the ultimate indicator of useful output in software development. The integration of actual developer tools (TerminalTool, EditFile) within the benchmark environment creates a uniquely realistic testing ground.

Frequently Asked Questions (FAQ)

1. How does cto.bench differ from standard AI coding benchmarks? cto.bench uniquely sources tasks from real user activity on cto.new, measuring success by merged code rates in actual development workflows, unlike benchmarks using predefined synthetic problems.
2. Why is real-world task data crucial for evaluating AI coding agents? Synthetic benchmarks often overlook tooling integration, legacy code complexity, and team-specific practices. Real-world data ensures performance metrics reflect practical usability and integration viability.
3. How frequently is the cto.bench leaderboard updated? The leaderboard displays a rolling 72-hour success rate updated continuously, with a mandatory 2-day lag to allow for task resolution, ensuring statistical reliability.
4. Which AI models are evaluated on cto.bench? The benchmark includes widely used commercial and research models (e.g., Claude Sonnet 4.5, GPT-5.2, Gemini 3 Pro) that meet minimum usage thresholds on the cto.new platform for statistical significance.
5. Can teams use cto.bench to compare private/internal AI models? Currently, cto.bench reports on models used within the public cto.new platform. Private model benchmarking would require integration with the cto.new API and task data.