How to Use Ollama for Free AI Coding in Cursor and Cline (2026)

With the free Gemini CLI retiring and hosted free tiers shrinking across the board, the most durable way to keep coding with AI for $0 is to stop renting a cloud model and run one yourself. Ollama makes that genuinely easy — and it plugs into the editors you already use.

But there's a catch most guides skip, so let's be clear up front: you can run a local model for chat and agent work in Cline and Cursor, but Cursor's Tab autocomplete cannot use a local model. Here's the honest, working setup for each.

Step 1: Get Ollama Running

Install Ollama from ollama.com, then pull a coding model. Qwen2.5-Coder is the current best all-rounder for local coding:

# the 7B size is a good default for chat + agent work
ollama pull qwen2.5-coder:7b

That's it — Ollama runs a local server at http://localhost:11434 that the editors below connect to. No API key, no account, no bill.

Cline + Ollama — The Easiest Fully-Local Setup

Cline is an open-source VS Code agent (you give it tasks like "add a contact form and wire it to the API"), and it has native Ollama support — this is the smoothest local setup of the three.

1. Install the Cline extension in VS Code.
2. Open Cline's settings and set API Provider to Ollama.
3. Pick your pulled model (qwen2.5-coder:7b) from the list.

Done. Every Cline task now runs entirely on your machine at $0. Because it's open-source and the model is local, this is also the cleanest setup if you care about keeping code off third-party servers.

The trade-off is the usual local-model one: a 7B model is capable but not Claude- or GPT-5-class, so expect to steer it more on complex, multi-file work.

Cursor + Ollama — Great for Chat and Cmd+K (Not Autocomplete)

Cursor can use a local model too, through its OpenAI-compatible override — but only for Chat and Cmd+K, not Tab autocomplete.

1. In Cursor, open Settings → Models.
2. Enable the OpenAI API Key section and turn on Override Base URL.
3. Set the base URL to http://localhost:11434/v1 and the API key to ollama (any non-empty string works).
4. Add your model name (qwen2.5-coder:7b) and click Verify.

Now Cursor's chat panel and Cmd/Ctrl+K inline edits run on your local model for free. Cursor Tab autocomplete will keep using Cursor's cloud models regardless — that feature isn't configurable to a local backend, so don't expect "free autocomplete" out of this. It's a common misconception worth getting right before you set expectations.

One more caveat: Cursor is still a closed-source editor with its own indexing and telemetry, so a local model makes inference free and private but doesn't make the whole editor offline.

Want Real Local Tab Autocomplete? Use Continue

If free, local Tab autocomplete is what you're actually after, the tool for the job is the Continue VS Code extension, which supports a dedicated local autocomplete model via Ollama.

1. Pull a small, fast model for autocomplete (speed matters because it fires on every keystroke):

ollama pull qwen2.5-coder:1.5b

2. Install the Continue extension in VS Code.
3. In Continue's config, add qwen2.5-coder:1.5b with the provider set to ollama and the role set to autocomplete (keep the 7B model for chat).

Now you get genuinely free, fully-local inline completions. (Note: Continue has since shifted its flagship focus to a CI-based product, but the editor extension still installs and runs locally — fine for this use.)

Picking a Model (and the num_ctx Gotcha)

Good local coding models in 2026:

• Qwen2.5-Coder — the best all-round pick; 7b for chat/agent, 1.5b for autocomplete.
• DeepSeek Coder — strong on code reasoning.
• Codestral — Mistral's code model, good completions.

The single most common reason a local setup "feels broken": Ollama defaults to a small 4–8k context window, but Cursor and Cline send 30k+ tokens. The model silently truncates and produces nonsense, and you blame the model. Raise the context window to at least 32,768 tokens for your coding model (via a Modelfile PARAMETER num_ctx 32768) and most of those quality complaints disappear.

The Honest Tradeoffs

Running a model locally is genuinely free per request and as private as it gets — your prompts never leave your machine. The costs are real but one-time: a capable computer (≈8GB RAM for a 7B model, much faster with a GPU) and a bit more setup than a cloud login. And the output won't match a frontier cloud model on hard, sprawling tasks.

For a beginner that math usually works out well: you learn on a tool that will never surprise you with a bill or a shrinking free tier. If your machine can't handle it, that's the honest signal to use a paid cloud tool rather than chase a free tier that's being cut.

For where local fits in the bigger picture, see our roundup of free terminal AI coding agents (Goose and Aider also run great on Ollama), and LM Studio vs Ollama if you'd rather manage local models through a desktop app than the command line.