#>IDE extension· 12 models ranked

Best Local LLMs for Roo Code

Autonomous VS Code agent with modes (a Cline fork)

Roo Code is an autonomous VS Code coding agent forked from Cline, adding selectable modes (Architect, Code, Ask, Debug). It runs any local model via Ollama, LM Studio, or an OpenAI-compatible endpoint, and because it shares Cline's diff/tool-use protocol it has the same low tolerance for weak tool-callers and small context windows. The strong agentic Qwen MoE models lead.

Best pick

Qwen3.6 35B-A3B

Newest large Qwen3.x MoE; strongest tool-use + repo-scale reasoning in the catalog.

What Roo Code needs

Reliable structured tool-calling sustained across a large multi-turn context window — the model must follow the diff/tool protocol turn after turn without drifting.

Roo Code Local LLM Tier List

SS — Best in class

Qwen3.6 35B-A3B35B· 24GB RAM

Newest large Qwen3.x MoE; strongest tool-use + repo-scale reasoning in the catalog.

Qwen3.5 35B-A3B Instruct35B· 24GB RAM

Prior-gen of the same MoE class; proven agentic tool-calling, fast active-param inference.

AA — Strong, reliable

Qwen3 30B30B· 28GB RAM

Direct lineage of Cline/Roo's recommended Qwen3 Coder 30B; the safe default local pick.

Qwen2.5 Coder 14B14B· 22GB RAM

The most-reported "actually works in Roo/Cline" dense coder; community Modelfiles target it.

Qwen3.5 27B Instruct27B· 20GB RAM

Dense 27B with current-gen instruction-following; reliable tool calls, ample context.

Mistral Small 22B22B· 26GB RAM

Mistral Small (Devstral) lineage built for agentic tool-use; solid mid-size performer.

BB — Usable with caveats

Qwen3.5 9B Instruct9B· 14GB RAM

Handles tool calls but occasionally drifts on long agentic chains.

Qwen3 14B14B· 20GB RAM

Decent tool-use; weaker repo-scale memory than 27B+.

Gemma 4 31B31B· 32GB RAM

Strong reasoning, but Gemma tool-calling is less battle-tested in Roo/Cline than Qwen.

CC — Works, but not recommended

Qwen3.5 4B Instruct4B· 8GB RAM

Too small for reliable multi-turn tool-calling; fine only for quick Ask mode.

DeepSeek-R1 Distill Qwen 14B14B· 22GB RAM

Verbose chain-of-thought interferes with the strict tool-call protocol.

Llama 4 Scout109B· 80GB RAM

Large MoE but inconsistent tool-calling in coding agents.

Tiers weigh tool-calling reliability, context window, and coding quality for Roo Code specifically — a model can rank higher for one tool than another. RAM figures are for Q4 quantization. Sources are listed below.

Local setup notes

Select Ollama or LM Studio as the provider. Roo Code defers to your model's Modelfile num_ctx — use 16K for ~8GB VRAM, 32K for ~16GB, and 64K+ for 24GB+. With LM Studio, switch the model to the OpenAI Compatible mode for clean tool calls.

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Frequently Asked Questions

What is the best local model for Roo Code?+

Cline, Roo Code's upstream, recommends Qwen3 Coder 30B at 4-bit with a large context window. In our catalog the closest matches are the Qwen3.x 35B-A3B MoE models and Qwen3 30B.

What context window does Roo Code need for local models?+

Roo Code uses your Ollama Modelfile's num_ctx. Practical guidance is 16K for ~8GB VRAM, 32K for ~16GB, and 64K+ for 24GB+. Coding agents benefit from 32K minimum.

Why do small local models fail in Roo Code?+

They lose the multi-turn tool-call protocol and run out of context tracking prior tool responses, so they emit malformed diffs or loop. Roo's agentic loop needs both strong structured tool-calling and a large context.