{}IDE extension· 12 models ranked

Best Local LLMs for Cline

Autonomous coding agent for VS Code (formerly Claude Dev)

Cline is an autonomous coding agent VS Code extension that reads files, edits code, runs terminal commands, and drives a browser through a tightly looped tool-call cycle. It is one of the most demanding agents to run locally — every step is a structured tool call over a long, growing context, so sub-7B and under-trained models routinely break its loop. This list is honest about what actually holds together.

Best pick

Qwen3 30B

Qwen3-Coder 30B is the community default — 256K context, agentic tuning, ~96% well-formed tool calls.

What Cline needs

Reliable structured tool-calling under a long, growing context window — this matters far more than raw benchmark coding skill.

Cline Local LLM Tier List

SS — Best in class

Qwen3 30B30B· 28GB RAM

Qwen3-Coder 30B is the community default — 256K context, agentic tuning, ~96% well-formed tool calls.

Llama 3.3 70B Instruct70B· 48GB RAM

Highest tool-call ceiling in benchmarks (~97%); strong instruction-following holds the loop, at heavy RAM cost.

AA — Strong, reliable

Qwen3.6 35B-A3B35B· 24GB RAM

Newest large Qwen3 MoE; strong reasoning, big enough to sustain Cline's loop.

Qwen3.5 35B-A3B Instruct35B· 24GB RAM

Same MoE class, proven agentic behavior and long context.

Gemma 4 31B31B· 32GB RAM

Benchmarked at ~95% tool-call reliability; best general-purpose pick for mid rigs.

Qwen3.5 27B Instruct27B· 20GB RAM

Dense Qwen3.5; reliable tool use and long context.

BB — Usable with caveats

Qwen3.5 9B Instruct9B· 14GB RAM

Fine for focused single-file tasks; tool calls get shaky on long multi-file sessions.

Mistral Small 22B22B· 26GB RAM

Solid instruction-following but less agent-tuned than Qwen3/Gemma peers.

Qwen2.5 Coder 14B14B· 22GB RAM

Strong code knowledge but weaker structured tool-calling than the Qwen3 generation.

CC — Works, but not recommended

Qwen2.5 Coder 7B7B· 10GB RAM

Reported to emit malformed/skipped tool calls; needs custom cline/tools variants to work at all.

DeepSeek-R1 Distill Qwen 14B14B· 22GB RAM

Over-thinks and dumps prose instead of structured tool calls.

Phi-4 14B14B· 22GB RAM

Not tool-call trained for agents; paraphrases rather than emitting clean tool calls.

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

Local setup notes

Set Cline's API provider to Ollama or LM Studio (OpenAI-compatible, localhost:11434). Enable "Use Compact Prompt", raise the model's context window, and budget 64GB+ RAM for the larger models. Q4_K_M is the practical quantization floor — Q3/Q2 degrade tool-call reliability.

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Frequently Asked Questions

What is the best local model for Cline?+

Qwen3-Coder 30B is the consensus default — 256K context and agentic tuning give roughly 96% well-formed tool calls. On a 48GB+ machine, Llama 3.3 70B has the highest tool-call ceiling at around 97%.

Why do small models fail in Cline?+

Cline relies on repeated structured tool calls over a long context. Sub-7B and untuned models emit malformed JSON or skip tool calls once the agent starts opening files and revising patches, which breaks the loop.

What quantization should I use for Cline?+

Q4_K_M is the practical floor. Q3 and Q2 quantization degrade tool-call reliability before they noticeably hurt chat quality, so they are risky for agentic use.