Local LLM Coding Assistant Comparison: Why We Switched From Tabby to Continue

What is a local LLM coding assistant?

A local LLM coding assistant runs entirely on your machine. It provides autocomplete suggestions, inline code chat, and sometimes full agentic capabilities — all without sending your code to a remote server. Companies handling proprietary codebases, developers working offline, and anyone tired of Copilot subscription fees are the core audience.

We spent two weeks testing four local LLM coding assistant options on the same codebase (a mid-size Next.js + Python monorepo). Here is what happened.

The four tools we tested

• -Continue.dev — Open-source VS Code and JetBrains extension. Supports any LLM backend (Ollama, LM Studio, OpenAI-compatible APIs, Anthropic). The most flexible option.
• -Tabby — Purpose-built for tab autocomplete. Designed specifically for code completion. Lightweight, fast, and opinionated. Runs its own model server.
• -FauxPilot — Open-source Copilot replacement. Wraps a local model in a Copilot-compatible API. Less active development in 2026 but still works for basic inline suggestions.
• -Ollama + Custom Prompts (DIY) — Running Ollama in the background with custom shell functions or editor integration. Maximum control, minimum polish.

Autocomplete accuracy comparison

We measured suggestion acceptance rate — the percentage of inline autocomplete suggestions we accepted without modification — over 40 hours of real development work on the same project.

Numbers that jump out: Continue with Qwen3-Coder hit 71% acceptance, which is close to what we see with cloud Copilot. Tabby is faster at 45ms but less accurate. The DIY approach is too slow for real-time autocomplete but works fine for chat-based coding.

Chat vs inline suggestions vs agentic mode

Inline autocomplete (Tabby, Continue tab completion): Best for writing boilerplate, completing function calls, filling in obvious patterns. Speed matters more than reasoning depth. A 1B model is often enough.

Chat mode (Continue chat, Ollama): Best for explaining code, planning features, debugging. You need a larger model (14B+) for good results. Latency is less critical since you are waiting for a full answer.

Agentic mode (Continue agentic, Claude Code with local proxy): The model reads multiple files, plans changes, and edits code across your project. You need the most capable model you can fit in VRAM. This is where picking the right local LLM for coding matters most.

Why we switched from Tabby to Continue

Tabby is faster. Pure latency, nothing beats a 1B model responding in 45ms. But we found ourselves rewriting too many suggestions. The local LLM autocomplete quality just was not there for anything beyond simple completions.

Privacy and security advantages

All four options keep your code on your machine. This matters for teams working on proprietary codebases, contractors under NDA, and developers in regulated industries (healthcare, finance, defense). Your code never leaves your GPU.

Compared to cloud alternatives: Copilot sends code snippets to Microsoft servers. Cursor routes requests through their infrastructure. Even "private" cloud options still transmit your code over the internet.

Which assistant should you use?

Choose Continue.dev if you want the best local LLM code assistant with maximum flexibility. It works with any model, any backend, and supports autocomplete + chat + agentic mode.

Choose Tabby if pure autocomplete speed is your priority and you do not need chat or agentic features.

Skip FauxPilot unless you specifically need Copilot API compatibility.

Skip the DIY approach unless you enjoy building things from scratch and do not mind the extra setup time.

Once you pick your tool, head to our VS Code and Cursor setup guide to get it running. And check our best local LLM for coding rankings to pair it with the right model. For deeper testing data on code generation and debugging quality, we break down model performance by task type.

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