Cline vs. Continue: Which AI Coding Assistant Wins for Local LLM Support in 2025?

The AI coding assistant landscape has shifted dramatically. In early 2024, the conversation was dominated by cloud-based giants like GitHub Copilot and ChatGPT. But by 2025, a quieter revolution has taken hold: local-first development. Developers are increasingly running open-source models like Llama 3.1, Qwen 2.5, and DeepSeek Coder directly on their own hardware, driven by concerns over data privacy, per-token costs, and the desire for offline functionality.

At the center of this shift are two open-source IDE extensions: Cline (formerly Claude Dev) and Continue. Both promise to turn your editor into an autonomous coding agent, but they take fundamentally different approaches to local LLM integration. After testing both extensively against local models (specifically Qwen 2.5 Coder 32B and Llama 3.1 8B via Ollama), I’ve broken down exactly where each shines—and where they fall short.

The Core Philosophical Difference

Before diving into benchmarks, you need to understand the architectural split.

Cline is an agentic assistant. It operates like a junior developer with a terminal. It reads your files, writes code, executes terminal commands, and even opens a browser to test the output. It plans a multi-step task and executes it until completion, asking for permission at critical junctures. This autonomy is its selling point.

Continue is a conversational assistant on steroids. It focuses on inline code completion, chat-based Q&A about your codebase, and quick edits. It does not autonomously run your tests or debug a failing build without you explicitly driving each step. It’s more like a highly intelligent pair programmer who waits for your instructions.

This distinction becomes critical when using local models, which are generally less capable than GPT-4 or Claude Opus.

Local LLM Setup: The Friction Test

I tested both tools using Ollama as the backend, running Qwen 2.5 Coder 32B (the current sweet spot for local code generation) on an M2 Ultra Mac Studio with 128GB of unified memory.

Cline’s Configuration

Cline’s setup is straightforward. In the settings, you select “Ollama” as the provider, pick your model from a dropdown, and you’re done. The extension automatically detects the local API endpoint (localhost:11434).

The friction point appears in context management. Cline sends your entire conversation history, file contents, and terminal output to the model with every request. With a 32B model, this quickly eats into the context window. I found that after about 15 minutes of complex refactoring, Cline began to “forget” earlier instructions because the context window was saturated. You must manually use the “Clear Context” button frequently, which breaks the agentic flow.

Continue’s Configuration

Continue requires a bit more YAML configuration. You need to edit a config.yaml file to define your local model as the “chat” model and optionally a separate “autocomplete” model. It’s not difficult, but it’s less plug-and-play than Cline.

However, Continue handles context more gracefully. It uses a retrieval-augmented generation (RAG) approach. Instead of dumping your entire codebase into the prompt, it indexes your repo and only pulls in relevant code snippets when you ask a question. This is a massive advantage for local models with smaller context windows (typically 8k-32k tokens). It keeps the prompt lean, which directly translates to faster inference times.

Winner: Continue for long sessions. Cline’s context bloat is a usability killer when you’re not using a massive 128k context cloud model.

Code Generation Quality: The Reality Check

This is where the hype meets the pavement. Local models are good, but they are not GPT-4. They make more syntax errors, struggle with obscure library APIs, and occasionally hallucinate functions that don’t exist.

Cline’s Agentic Loop

Cline’s strength is its self-correction loop. When it writes a script that fails to run, it reads the error output from the terminal, analyzes the stack trace, and attempts a fix without you intervening.

I tasked both tools with a complex refactor: converting a monolithic Python script into a modular package with unit tests.

Cline took 14 minutes to complete the task. It hit three errors along the way. In each case, it read the traceback, modified the import statements, and reran the tests. The final output was functional. However, the code was verbose. It added unnecessary comments and over-engineered some functions with redundant type hints, likely because the local model was trying to “look” smart.

Continue’s Manual Loop

Continue is not designed for this autonomous loop. I had to manually copy the error messages into the chat and ask for fixes. This is slower in terms of wall-clock time, but the resulting code was cleaner. Because I was driving the edits, I could reject a verbose solution immediately and ask for a simpler one.

For inline autocomplete, Continue is significantly better. It predicts your next few lines with low latency (under 200ms on my hardware). Cline does not offer inline autocomplete at all—it only works in a chat/agent mode.

Winner: Cline for “set it and forget it” tasks. Winner: Continue for code quality control and daily typing assistance.

Resource Consumption and Speed

Local LLMs are resource hogs. If you are on a 16GB MacBook or a modest Windows PC, this matters more than feature lists.

  • Cline is a resource monster. Because it maintains a massive context and executes multiple tool calls, it keeps the GPU pegged. During a long agentic run, my system monitor showed 40GB+ of memory in use and the fans spinning at full speed. It also blocks the editor UI while processing, which is annoying if you want to read code while it works.
  • Continue is lightweight. The autocomplete model runs a small 1.5B parameter model (like Qwen 2.5 Coder 1.5B) which uses minimal RAM. The chat model only activates when you send a prompt. It does not lock the UI.

If you are running a 7B or 8B model on consumer hardware, Cline will feel sluggish. Continue remains snappy.

Winner: Continue for hardware accessibility.

The “Open Source vs. Open Weights” Trap

Both tools are open-source, but they have different philosophies regarding model support.

Cline is aggressively pushing MCP (Model Context Protocol) support and supports a wider array of exotic local runtimes (LM Studio, KoboldCpp, etc.). However, it is heavily optimized for Claude models. When using local models, you often have to tweak the system prompt to stop the model from “acting like Claude” (which results in weird formatting).

Continue is more agnostic. It treats local models as first-class citizens. The UI is cleaner, and the prompts are more neutral, which suits smaller models better.

Security and Privacy: The Real Reason You Go Local

The primary reason developers choose local LLMs is data privacy. You don’t want your proprietary codebase sent to a third-party API.

Both tools handle this well. When configured with Ollama, zero data leaves your machine. However, there is a caveat with Cline: if you use its “Plan” mode with a cloud model for high-level strategy and a local model for execution, your plan data goes to the cloud. Continue has a similar hybrid feature, but it is less prominent in the UI.

If you are 100% air-gapped, both work. But Cline’s default settings push you toward cloud models for better performance, which is a temptation that defeats the purpose of local use.

Verdict: Which Should You Pick?

There is no universal winner. It depends on your workflow:

Choose Cline if:

  • You want to automate multi-step tasks (e.g., “Refactor this module and update all tests”).
  • You have a high-end GPU (RTX 4090, M2 Max/Ultra) with 64GB+ RAM.
  • You are willing to babysit the context window and clear it frequently.
  • You value autonomy over code style.

Choose Continue if:

  • You want a fast, low-latency autocomplete that feels like Copilot but runs offline.
  • You have limited hardware (16-32GB RAM).
  • You prefer to review and approve every change before it is applied.
  • You work on a massive codebase and need RAG to find relevant code without blowing up the context.

In my experience, the pragmatic choice for 2025 is Continue for daily driving, with Cline installed as a secondary tool for occasional “fire-and-forget” tasks. The future of local coding is bright, but the current generation of local models (even 32B) is not yet reliable enough to run fully autonomously without constant supervision.

The bottom line: Cline is a self-driving car that occasionally crashes. Continue is a reliable manual transmission. Choose your risk tolerance accordingly.