𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗟𝗟𝗠𝘀 𝗳𝗼𝗿 𝗖𝗼𝗱𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟲
Stop guessing if your coding assistant works. Eyeballing outputs is not a strategy. You need a way to compare models using real data.
A good benchmark tests three specific areas:
- Unit tests: Short functions with hidden tests.
- Project generation: Building a small repo from a spec.
- Debugging: Fixing buggy code and test failures.
You can use the OpenAI Evals suite to automate this. It includes 75 tasks across Python, JavaScript, and Go. It works with any API compatible model.
Follow these steps to build your workflow:
Clone the repository: git clone https://github.com/openai/evals.git
Setup your environment: python3 -m venv .venv source .venv/bin/activate pip install -e .
Create a models.yaml file to list your models. You can test hosted models like Claude or Gemini alongside open source models like Mistral.
Run the tests: python -m evals.legacy.run_all --model-config models.yaml
The tool produces a CSV file. Load this file into a spreadsheet to track these metrics:
- Average accuracy.
- Confidence intervals.
- Average latency.
- Cost per 1k tokens.
Data helps you make better deployment choices.
- High accuracy needs: Use Claude-Opus for critical code generation.
- Low latency needs: Use Mistral-7B for edge devices or quick suggestions.
- Balanced needs: Use a hybrid approach. Route easy tasks to Gemini and complex tasks to Claude.
Models change quickly. Set up a weekly automated run. If accuracy drops by more than 5%, you will know immediately.
Turn vague feelings into concrete numbers for your stakeholders.
Source: https://dev.to/mrclaw207/benchmarking-llms-for-coding-in-2026-a-practical-guide-1ioh
Optional learning community: https://t.me/GyaanSetuAi