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

Claude Code told me the web page was done. The folder was empty. 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. Setup RTX 3060 Laptop 6GB + 64GB RAM, WSL2 Ubuntu 24.04 llama.cpp 9655061: llama-server -c 32768 -ngl 99 -fa on -ctk q8_0 -ctv q8_0 --jinja; experts on CPU (-ncmoe 36 for LFM, -ncmoe 40 for Qwen). Fully offline. Models: LFM2.5-24B-A2B Q4_0 (3 runs per task), Qwen3.6-35B-A3B UD-Q4_K_XL (1 run, control) Tasks: A: fix two bugs in calc.py until pytest passes B: single-file timer page, checked by a script against 8 items Pelican: Generate an SVG of a pelican riding a bicycle 900 s cap per run Results Agent Passed LFM bug-fix median First prompt pi 12/12 47 s ~1,400 tokens Codex 12/12 85 s ~6,400 tokens Claude Code 7/12 132 s ~15,800 tokens The prompt is the problem Every request starts with the agent's system prompt and tool definitions. First prompt eval in the llama-server log: Agent Tokens Time pi 1,442 5,666 ms Codex 6,429 18,567 ms Claude Code ~15,800 48,837 ms With a 32K context, Claude Code fills about half of it before doing anything, and reading that prompt takes about 8.6× longer than pi's. pi 12/12 on both models, zero tool errors in the logs, fastest bug fix (47 s median). I also trimmed its prompt to about 750 tokens with only read/edit/bash enabled. Still 12/12, same speed. Harder tasks might separate the two; these didn't. Codex 12/12, but LFM made 12 tool-argument errors, mostly integers sent as strings: failed to parse function arguments: invalid type: string "96787", expected i32 It retries and recovers. The cost is time: 85 s median for the bug fix, 599 s on the first run. Claude Code LFM passed the bug fix once in three runs. The other two died with: Prompt is too long · automatic compaction failed: summarization produced empty response Timer page: 3/3, but an 881 s median, and one run hit the 900 s cap. Qwen: 0/3. For the page and the pelican it replied that the task was complete. The working folder had no files. An error tells you something failed. A false "done" only shows up when you check the folder yourself. Pelicans Called directly, LFM's pelican floats above the bike; inside an agent it actually sits on it. Qwen's drawings are more complete. Claude Code + Qwen produced no SVG. Takeaways Small local model, small context: pick the agent with the smallest prompt. Here that was pi, then Codex. If you must use Claude Code locally, give it more context (Ollama's docs suggest 64K+) and look at Unsloth's notes on trimming its prompt (--bare). I didn't test those here. Always ls after an agent says it's done. Limits VRAM was sampled once per second, so I only saw resident usage (~2,641 MiB LFM, ~3,111 MiB Qwen), not peaks. Tool-error counts come from log keyword matching and are approximate. Qwen got one run per task. Three small tasks, not a real project. Full data and configs: https://kunbuilds.com/en/posts/agents-local-model-6gb/ Sources Ollama, Claude Code integration: https://docs.ollama.com/integrations/claude-code Unsloth, Claude Code with local LLMs: https://unsloth.ai/docs/basics/claude-code