Testing JetBrains Mellum2.1-12B LLM on the Pi Terminal Coding Agent
A developer tested JetBrains' open-weights Mellum2.1-12B-A2.5B Mixture-of-Experts model using the terminal-based Pi coding agent on consumer hardware. The evaluation revealed that while the model struggles with complex one-shot code generation, it excels at multi-step agentic tasks, tool calling, and self-correcting code edits. The evaluation highlights how smaller, agent-focused open models can provide high-utility automated coding workflows on local hardware without requiring massive cloud-hosted LLMs. It demonstrates that effective tool usage and error recovery can be more important for practical developer workflows than raw single-prompt code output. Running at Q8 quantization with a 131K context window on a laptop with 8GB VRAM, the model achieved inference speeds around 40 tokens per second via llama-server. While it produced poor results on standalone projects like browser OS or Minecraft clones, it successfully navigated an existing codebase to modify game logic and executed multi-step terminal tasks like converting screen recordings into GIFs.
## BACKGROUND
Mellum2.1 is an open-source 12-billion parameter Mixture-of-Experts (MoE) model developed by JetBrains, designed specifically for low-latency coding agent workflows with only 2.5 billion active parameters per token. Pi is an open-source terminal-based coding agent framework that allows AI models to inspect files, execute CLI commands, and perform multi-step software development tasks.