User Compares PI Agent and OpenCode Performance Using Qwen 3.8 27B
A local LLM user shared a performance comparison between the "PI Agent" and "OpenCode" AI coding agents running a Qwen 3.8 27B model on an RTX 3090. The test showed that PI Agent performed better, was faster, avoided freezing, and managed context compression much more efficiently than OpenCode. This comparison highlights how different agent frameworks handle context windows and token limits differently even when using the same underlying local model. It provides practical insights for developers looking to optimize local AI coding workflows on consumer hardware like the RTX 3090. The user ran the Qwen 3.8 27B model via `llama-server` with a 100k context size, offloading a vision module to RAM to assess output quality. PI Agent began compressing context at 90k tokens compared to OpenCode's 67k tokens, and it did not suffer from OpenCode's 32k output token limit.
## BACKGROUND
AI coding agents like OpenCode and Pi Agent are designed to automate software development tasks by interacting with LLMs and executing tools. Local LLM hosting tools like `llama-server` (part of the llama.cpp project) allow users to run large models on consumer GPUs, but managing large context windows (like 100k tokens) requires efficient memory and context compression strategies.