Continue.dev Review 2026: The Free Copilot That Runs Offline
GitHub Copilot costs $19 per user per month. Continue.dev costs nothing and runs on models you host yourself. The question is not whether Continue is cheaper — it obviously is — but whether it is good enough to replace Copilot for day-to-day coding work.
After extended use in both VS Code and JetBrains on local and cloud-hosted models, the honest answer is: yes, for most developers, and especially for anyone working in a security-conscious environment where code cannot touch an external API.
What Continue.dev is
Continue is an open-source AI coding assistant that runs inside your IDE. It provides inline code completion, a chat panel, and (since 2026) an agent mode for multi-step task execution. You bring your own models — either a cloud API key (OpenAI, Anthropic, Gemini) or a local runner (Ollama, llama.cpp, LM Studio).
The core value proposition: Copilot’s feature set, your choice of model, zero data leaves your machine if you want it that way.
License: Apache 2.0. Repository: github.com/continuedev/continue.
IDEs supported: VS Code, JetBrains (IntelliJ, PyCharm, WebStorm, GoLand, and others).
Installation
VS Code:
- Open Extensions panel, search “Continue”
- Install the Continue extension
- Open the Continue sidebar — it walks you through model setup
JetBrains:
- Settings → Plugins → search “Continue”
- Install, restart IDE
- Configure models in the Continue settings pane
First-time setup prompts you to choose a model. Selecting Ollama auto-detects running local models. For cloud APIs, paste an API key. Multiple providers can be configured simultaneously — useful for routing different tasks to different models.
Models and configuration
Continue supports 20+ LLM access methods. Common setups:
Local (offline, free):
{
"models": [{
"title": "Llama 3.1 8B (local)",
"provider": "ollama",
"model": "llama3.1:8b"
}]
}Cloud API:
{
"models": [{
"title": "Claude 3.5 Sonnet",
"provider": "anthropic",
"model": "claude-sonnet-4-5",
"apiKey": "YOUR_KEY"
}]
}Multi-model (best of both):
{
"models": [
{ "title": "Local - fast completions", "provider": "ollama", "model": "qwen2.5-coder:7b" },
{ "title": "Claude - deep reasoning", "provider": "anthropic", "model": "claude-opus-4-7" }
]
}The multi-model setup is worth understanding. Fast local models handle tab completions with near-zero latency. A cloud model handles complex chat questions where reasoning depth matters. You switch between them with a dropdown — no restart required.
Context providers
The @ mention system is Continue’s most underrated feature. Type @ in the chat panel:
| Context provider | What it includes |
|---|---|
@Codebase | Semantic search across your full repo |
@File | Contents of a specific file |
@Folder | All files in a directory |
@Terminal | Current terminal output |
@Git Diff | Uncommitted changes |
@Docs | Fetched documentation for a package |
@Problems | Current IDE error list |
@URL | Fetched content from a URL |
@Codebase is the one that most distinguishes Continue from simpler autocomplete tools. It indexes your project and retrieves relevant context chunks before sending to the model — functionally a RAG pipeline over your codebase. On a 50,000-line project, this makes the difference between getting useful answers and getting generic responses.
Agent mode (2026)
Agent mode was added in 2026 and turns Continue from a “chat and suggest” tool into one that can execute multi-step tasks autonomously:
- Read a requirement → plan steps → edit multiple files → run terminal commands → verify output
- Autonomous multi-file refactoring (rename a type across an entire codebase, update all usages)
- CI-triggered workflows: run on pull request open, scheduled cron, or GitHub Actions pipeline
Agent mode connects to the IDE’s terminal and file system. It is not sandboxed by default — treat it like you would treat any automated script that can write files and run commands. Review what it is doing before confirming destructive operations.
Tab completion
Tab completion is the feature you interact with most. Continue’s behavior:
- Suggestions appear as greyed-out inline text (same pattern as Copilot)
- Accept with Tab, dismiss with Escape
- Partial accept (accept to end of word) with Ctrl+Right
Speed reality check:
| Setup | Typical suggestion latency |
|---|---|
| Local 7B model (RTX 4060) | 200–500 ms |
| Local 7B model (CPU only) | 2–8 seconds — too slow for tab completion |
| Cloud API (Anthropic/OpenAI) | 500–1500 ms |
| GitHub Copilot (cloud) | 300–800 ms |
On a GPU with a quantized 7B coding model (Qwen 2.5 Coder 7B or DeepSeek Coder 6.7B), local completion speed is within the comfortable range for most developers. An RTX 4060 on Amazon is the budget entry point — 8 GB VRAM handles 7B models at the latency numbers above. CPU-only is too slow for responsive tab completion — use a cloud API if you do not have a GPU, or use Continue primarily for chat rather than inline completion. If you want to experiment with larger models before buying hardware, RunPod rents GPU instances by the hour.
The quality gap between a local 7B model and Copilot’s backend (GPT-4-class) is real for complex completions. On boilerplate, imports, and common patterns, the difference is minimal. On complex algorithmic logic, a cloud model wins.
Continue vs GitHub Copilot
| Continue.dev | GitHub Copilot Business | |
|---|---|---|
| Cost | Free (bring your own model) | $19/user/month |
| Model choice | Any (20+ providers, local or cloud) | GPT-4o only (no choice) |
| Data privacy | Full control — local models send nothing | Code sent to GitHub servers |
| Offline use | Yes (with local model) | No |
| Agent mode | Yes (free) | Yes (paid tier) |
| Context depth | @Codebase semantic search + 10+ providers | File-level context |
| IDE support | VS Code, JetBrains | VS Code, JetBrains, Neovim, others |
| License | Apache 2.0 (open source) | Proprietary |
| Setup time | 15–30 min | 5 min |
The privacy point is not theoretical. Finance, healthcare, government, and defense teams regularly have policies preventing code from leaving the local network. For these use cases, Continue with Ollama is the only viable path — Copilot is disqualified by the data flow alone.
For teams without data restrictions, the cost math is straightforward: a 10-developer team saves $2,280/year by switching to Continue with local models, or somewhat less if using cloud API keys. The cloud API cost for typical Copilot-equivalent usage through Anthropic or OpenAI runs $5–15/month per developer depending on volume — still cheaper than $19/month with more model flexibility.
When NOT to use Continue.dev
You want zero setup. Continue requires more initial configuration than Copilot — choosing a model, setting up Ollama if going local, configuring context providers. If a five-minute install is the deciding factor, Copilot wins.
Your team needs enterprise SSO/audit logging. Copilot Enterprise has centralized management, audit logs, and policy controls. Continue has none of this out of the box.
Your primary workflow is CPU-only and you want fast tab completions. Local models on CPU are too slow for responsive inline suggestions. Use a cloud API in this case, which brings the cost advantage down.
You are on a model that struggles with your tech stack. Some smaller local models are weak on specific languages (older Rust patterns, niche frameworks). Verify model performance on your actual codebase before committing to a local-only setup.
Verdict
Continue.dev is a serious Copilot replacement, not a poor substitute. For developers who can run a local GPU model or are willing to use a cloud API key directly, it matches Copilot’s core features at lower cost with better data control.
The @Codebase context provider and multi-model setup are meaningfully better than what Copilot offers. Agent mode is on par. Tab completion quality is equal on boilerplate, slightly behind on complex code when using smaller local models.
Start here: install Continue, connect it to Ollama with qwen2.5-coder:7b, use it for a week. If the completions are good enough for your workflow, you have just saved $19/month indefinitely. If you need a stronger model for complex tasks, add a cloud API key as the secondary model and route heavy questions there.
For setting up the full local stack — Continue + Ollama + a coding model — see the setup guide (coming soon). For hardware to run local models, see runaihome.com’s local LLM hardware guide.
Reviewed on Continue extension v1.x against VS Code 1.90+ and JetBrains 2026.1. License confirmed Apache 2.0 at github.com/continuedev/continue. Performance figures from community benchmarks and personal testing.