Testing Local LLMs for Autonomous Code Generation: Quality vs. Speed Benchmark

A developer spent months building an AI agent that autonomously writes Go code using local LLMs, specifically for generating log parsers for SIEM pipelines. The main challenge was evaluation: how to objectively measure whether a model is actually useful for autonomous coding tasks.

Benchmark Harness

The harness works as follows:

• Agents generate real Go parsers from log format descriptions.
• The generated Go code is compiled.
• Extracted fields and types are validated against expected schemas.
• Parsing quality is measured against expected schemas.
• Throughput and speed are tracked over longer runs.

First Public Release

The author published the first public version of the benchmark and methodology at the following link. The post discusses results given the current release cadence of open-weight models. The author also asks for feedback and suggestions on which model to test next.

Read the full blog post for detailed results and methodology: Testing Local LLMs in Practice: Code Generation, Quality vs. Speed

This is a practical resource for developers building AI coding agents and choosing local LLMs for code generation tasks.