cto bench

Overview

Tired of AI benchmarks that feel like academic exercises rather than real-world indicators? Many AI evaluation platforms focus on hypothetical, often contrived, challenges. But what truly matters is how AI agents perform on the actual tasks that fill your daily workflow. Enter cto.bench, a platform designed to bridge this gap by grounding its evaluations in the reality of how developers use AI in practice. The benchmark measures merged code as a percentage of completed tasks from real end-to-end coding activities.

Important Clarification: cto.bench is developed by Engine Labs, which raised \$5.7M to launch cto.new as a completely free AI code agent platform. The official URL is https://cto.new/bench.

Key Features

• Data Derived from Real Platform Usage: Unlike synthetic benchmarks, cto.bench’s evaluations are built upon actual data from how users interact with the cto.new platform, ensuring relevance and authenticity. Every data point comes directly from real developer tasks.
• Developer-Centric Benchmark: This benchmark is specifically designed with developers in mind, focusing on the practical application of AI in day-to-day coding and development tasks across the entire software development lifecycle.
• Measures Applied Task Performance: Instead of theoretical problem-solving, cto.bench quantifies how well AI agents execute real work that developers are actively engaged in, using merged code as the ultimate success metric.
• Dynamic Leaderboard: The platform displays a rolling 72-hour success rate with a 2-day lag to allow for task resolution, updating continuously as new data becomes available.
• Production-Realistic Toolset: Evaluates models using tools that mirror actual developer workflows: ReadFile, WriteFile, EditFile, GlobTool, GrepTool, LsTool, and TerminalTool for VM terminal interaction.
• Statistical Rigor: Only models meeting minimum usage thresholds for statistical significance are included, and data from teams that have never merged code is excluded to ensure quality measurements.

How It Works

cto.bench operates by meticulously collecting real-world usage data directly from developers interacting with the cto.new platform. This rich dataset then fuels the evaluation process, allowing for precise measurement of agent efficiency and accuracy in performing applied tasks. The system tracks tasks from initiation through completion, measuring success by whether code gets merged into the codebase. It’s a feedback loop of real work informing real evaluation, with the leaderboard displaying the most recently available measurements for models that meet benchmark criteria within the last calendar month.

Use Cases

• CTOs Evaluating Practical AI Performance: Gain concrete insights into how AI tools will integrate and perform within existing product development workflows, with data grounded in actual engineering practices.
• Developers Assessing Agent Effectiveness: Understand which AI agents are truly beneficial for specific coding and development tasks, moving beyond abstract performance metrics to see real-world success rates.
• AI Integration Decision-Making: Engineering leaders can use the leaderboard to select optimal models for their team’s specific codebase and workflow requirements.
• Performance Tracking: Organizations can track model regression or improvement on real tasks over time using the consistent, updated benchmark data.

Pros \& Cons

Advantages

• Realistic Evaluation: Benchmarks are based on actual, real-world usage, providing a true reflection of AI performance in production environments.
• Relevant Results: The data and insights generated are directly applicable to the tasks developers face daily, including tooling integration, legacy code complexity, and team-specific practices.
• Ground Truth Metric: Using merged code as the success criterion provides an objective, high-fidelity measure of useful output.
• Transparent Methodology: The evaluation process and toolset are publicly documented, allowing for understanding of how agents are tested.
• Free Access: cto.new provides completely free access to frontier models without requiring credit cards or API keys, democratizing AI development capabilities.

Disadvantages

• Limited Generalization for Other Tasks: Because the benchmark is derived from specific platform usage on cto.new, its findings might not directly translate to AI performance on entirely different types of tasks or platforms.
• Platform Dependency: The benchmark is tightly coupled to the cto.new ecosystem and its specific toolset, which may not represent all development environments.
• No Private Model Support: Currently, cto.bench reports only on models used within the public cto.new platform; private model benchmarking would require API integration.
• Lag Time: The 2-day lag for task resolution, while necessary for accurate measurement, means leaderboard data is not real-time.
• Selection Bias: The benchmark only includes teams that have successfully merged code, potentially excluding data from struggling teams or early-stage projects.

How Does It Compare?

vs. BIG-Bench

BIG-Bench Approach: Creates hypothetical problems through expert construction or synthesis, testing models on contrived scenarios that may not reflect real-world usage patterns.

cto.bench Differentiation: Uses exclusively real user tasks from production environments, measuring end-to-end task completion rather than isolated puzzles. While BIG-Bench provides broad coverage of linguistic capabilities, cto.bench offers practical insights into coding agent performance on actual development work.

Key Distinction: BIG-Bench tasks are designed to be challenging and diverse but artificial; cto.bench tasks emerge organically from developer needs, capturing the complexity of real codebases, legacy systems, and team workflows.

vs. HumanEval and SWE-bench

HumanEval Approach: Provides hand-written programming problems with unit tests to verify correctness, focusing on function-level code generation in isolated contexts.

SWE-bench Approach: Uses real GitHub issues from popular open-source repositories, providing a more realistic evaluation than HumanEval but still limited to specific bug-fixing scenarios.

cto.bench Differentiation: Goes beyond both by measuring complete task resolution through merged code, not just test passage. While SWE-bench tasks are real GitHub issues, cto.bench captures the entire spectrum of development activities including feature implementation, refactoring, and architectural decisions across private and public codebases.

Key Distinction: cto.bench’s “ground truth” metric of merged code serves as a more comprehensive success indicator than unit test passage alone, reflecting actual developer satisfaction and production readiness.

vs. Synthetic Coding Benchmarks

Synthetic Benchmarks: Platforms like Codeforces, LeetCode problems, or academic benchmarks create artificial coding challenges designed to test specific algorithmic skills.

cto.bench Differentiation: Synthetic benchmarks evaluate problem-solving skills in constrained, well-defined environments. cto.bench evaluates practical engineering capabilities including code integration, tool usage, debugging, and collaboration—skills essential for professional software development but absent from synthetic evaluations.

Key Distinction: While synthetic benchmarks are valuable for assessing fundamental coding abilities, cto.bench measures the higher-level skills required for productive software engineering in team environments.

vs. Other Real-World Benchmarks

Emerging Real-World Benchmarks: Newer initiatives like Context-Bench (from Letta) and τ-bench focus on specific aspects of agent performance such as context management or human-agent interaction.

cto.bench Positioning: While these benchmarks target important capabilities, cto.bench maintains a singular focus on coding task completion as the primary metric. This specialization allows for deeper insights into developer productivity rather than general agent capabilities.

Complementary Value: cto.bench serves as a practical complement to these emerging benchmarks, providing the coding-specific evaluation that general agent benchmarks may lack.

Final Thoughts

For CTOs and developers seeking to understand the true impact of AI on their workflows, cto.bench offers a valuable tool. By moving beyond theoretical exercises and focusing on real-world application, it delivers insights that are not only accurate but also immediately relevant to the challenges and opportunities of modern development.

Expert Perspective: The platform addresses a critical gap in AI evaluation by providing ground truth data from production environments. However, users should recognize that while cto.bench excels at measuring coding task performance, it doesn’t evaluate other important aspects of AI assistance such as code explanation, architectural advice, or collaborative problem-solving. The benchmark is most valuable when combined with other evaluation methods and when interpreted within the context of specific team workflows and codebase characteristics.

Implementation Consideration: Organizations adopting cto.new should be aware that while the platform is free, integrating it effectively requires established development workflows with version control and code review processes. Teams without these practices may not see immediate value from the benchmark metrics.