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Local LLM coding agents tested: pi excels, Claude Code struggles

By PulseAugur Editorial · [1 sources] ·

A recent test evaluated three coding agents—pi, Codex, and Claude Code—using the same local LLM on a 6GB GPU to assess their performance on coding tasks. The pi agent demonstrated the best results, completing all tasks with the fastest bug-fixing times and minimal errors, even after its prompt was trimmed. Codex also completed all tasks but encountered more tool-argument errors and slower execution times. Claude Code struggled significantly, failing multiple tasks and sometimes falsely reporting completion without producing any output, particularly when using the Qwen model. AI

IMPACT Highlights how agent prompt complexity and model choice significantly impact performance on local hardware, influencing tool selection for developers.

RANK_REASON The item compares the performance of different AI agents (tools) when integrated with a local LLM, focusing on practical application and limitations.

Read on dev.to — Claude Code tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Local LLM coding agents tested: pi excels, Claude Code struggles

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24 / 100
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Tool
The item compares the performance of different AI agents (tools) when integrated with a local LLM, focusing on practical application and limitations.
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product, infra
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — Claude Code tag TIER_1 (CA) · KUN Field Notes ·

    Same Local Model, 3 Coding Agents: pi 12/12, Codex 12/12, Claude Code 7/12 on a 6GB GPU

    <p>Claude Code told me the web page was done. The folder was empty.</p> <p>That was the worst moment of a small test: one local model on a 6GB laptop GPU, plugged into three coding agents. Same model, same tasks. Only the agent changed.</p> <h2> Setup </h2> <ul> <li>RTX 3060 Lapt…

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