
ts-bench
Measure and compare the performance of AI coding agents on TypeScript tasks.
Stars: 162

TS-Bench is a performance benchmarking tool for TypeScript projects. It provides detailed insights into the performance of TypeScript code, helping developers optimize their projects. With TS-Bench, users can measure and compare the execution time of different code snippets, functions, or modules. The tool offers a user-friendly interface for running benchmarks and analyzing the results. TS-Bench is a valuable asset for developers looking to enhance the performance of their TypeScript applications.
README:
ts-bench is a transparent and reproducible benchmark project for evaluating the TypeScript code editing capabilities of AI coding agents.
Rank | Agent | Model | Success Rate | Solved | Avg Time | Result |
---|---|---|---|---|---|---|
1 | opencode | openai/gpt-5 | 96.0% | 24/25 | 64.8s | #415419 |
2 | codex | gpt-5-codex | 92.0% | 23/25 | 98.7s | #015544 |
3 | goose | claude-sonnet-4-20250514 | 92.0% | 23/25 | 122.2s | #186071 |
4 | opencode | anthropic/claude-sonnet-4-20250514 | 92.0% | 23/25 | 127.8s | #043809 |
5 | gemini | gemini-2.5-pro | 92.0% | 23/25 | 168.5s | #052819 |
6 | codex | gpt-5 | 88.0% | 22/25 | 91.7s | #734992 |
7 | opencode | opencode/grok-code | 88.0% | 22/25 | 97.0s | #083421 |
8 | claude | glm-4.5 | 80.0% | 20/25 | 172.3s | #591219 |
9 | claude | claude-sonnet-4-20250514 | 72.0% | 18/25 | 206.1s | #732069 |
10 | qwen | qwen3-coder-plus | 64.0% | 16/25 | 123.9s | #246268 |
Currently supported agents:
This project is strongly inspired by benchmarks like Aider Polyglot. Rather than measuring the performance of large language models (LLMs) alone, it focuses on evaluating the agent layerβthe entire AI coding assistant tool, including prompt strategies, file operations, and iterative logic.
Based on this vision, the benchmark is designed according to the following principles:
- TypeScript-First: Focused on TypeScript, which is essential in modern development. Static typing presents unique challenges and opportunities for AI agents, making it a crucial evaluation target.
-
Agent-Agnostic: Designed to be independent of any specific AI agent, allowing fair comparison of multiple CLI-based agents such as
Aider
andClaude Code
. - Baseline Performance: Uses self-contained problem sets sourced from Exercism to serve as a baseline for measuring basic code reading and editing abilities. It is not intended to measure performance on large-scale editing tasks or complex bug fixes across entire repositories like SWE-bench.
All benchmark results are generated and published via GitHub Actions.
Each results page provides a formatted summary and downloadable artifacts containing raw data (JSON).
For detailed documentation, see:
- Environment Setup: Details on setting up the local and Docker environments.
- Leaderboard Operation Design: Explains how the leaderboard is updated and maintained.
bun install
Run the benchmark with the following commands. Use --help
to see all available options.
# Run the default 25 problems with Claude Code (Sonnet 3.5)
bun src/index.ts --agent claude --model claude-3-5-sonnet-20240620
# Run only the 'acronym' problem with Aider (GPT-4o)
bun src/index.ts --agent aider --model gpt-4o --exercise acronym
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