GitHub Copilot Alternatives Worth Trying in 2026

Copilot got a lot of people hooked on AI assistance, but plenty of teams outgrow it. The autocomplete is good, the chat is fine, and the deep GitHub integration is convenient. The friction shows up elsewhere: you want a dedicated AI-first editor, stronger agentic multi-file editing, a different pricing model, the freedom to pick your own model, privacy or self-hosting guarantees, or a tool that fits your specific stack better. None of those are reasons to quit AI coding. They are reasons to shop around.

Here is a category-by-category look at the alternatives worth your time, with an honest read on who each one suits.

AI-first editors #

These replace your editor rather than bolt onto it, which lets the AI touch the whole project.

Cursor: The default recommendation for most people leaving Copilot. It is a VS Code fork, so your extensions and keybindings carry over, but the agent understands your codebase and edits across many files in one pass. Best for developers who want the closest thing to a pair programmer without changing habits. If you are weighing the two directly, we compared Cursor vs GitHub Copilot in detail.

Windsurf: Similar shape to Cursor with a heavier lean toward autonomous flows. Its agent keeps context as it works through a task, which suits people who like handing off a chunk of work and reviewing the result. Good for teams that want agentic editing without much prompt babysitting.

Zed (with AI): A native, Rust-built editor that is genuinely fast, with AI features layered in. Best for developers who care about editor performance and low latency, and who want AI on their terms rather than as the centerpiece.

Terminal and agent tools #

If you live in the shell, these meet you there.

Claude Code: A terminal-based agent that reads your repo, runs commands, edits files, and works through multi-step tasks. It shines on larger refactors and jobs that span many files, and it fits developers who are comfortable driving from the command line. Strong for anyone who wants an agent that plans and executes rather than just completes lines.

Aider: Open-source, terminal-native, and model-agnostic. It commits changes to git as it goes, so every edit is reviewable and reversible. Point it at GPT, Claude, or a local model. Best for developers who want an open tool, full git discipline, and freedom over which model runs the show.

Cloud-vendor assistants #

If your infrastructure already lives with one cloud, its assistant knows that world.

Amazon Q Developer: Deeply tied to AWS. It answers questions about your account, helps with infrastructure code, and handles tasks like upgrades and migrations. Best for teams already committed to AWS who want an assistant fluent in their services.

Gemini Code Assist: Google's offering, with tight ties to Google Cloud and a generous individual tier. Best for developers on GCP or those who want a low-cost entry point backed by Google's models.

IDE-native assistants #

These stay inside the IDE you already trust.

JetBrains AI Assistant: Built into IntelliJ, PyCharm, and the rest of the JetBrains lineup. It understands the IDE's own refactoring and navigation tools, so its suggestions respect how those editors work. Best for JetBrains loyalists who want AI that feels native rather than pasted on.

Tabnine: The privacy-first pick. It offers self-hosting, air-gapped deployment, and models that can be trained on your code without it leaving your walls. Best for regulated industries and security teams that cannot send source to a third party.

Supermaven: Laser-focused on fast, accurate autocomplete with a very large context window. It is not trying to be an agent. Best for developers who mostly want completions that keep up with them and stay out of the way.

Open-source and local #

Run the whole stack yourself when policy or preference demands it.

Continue.dev: An open-source assistant that plugs into VS Code and JetBrains and lets you wire in any model, including local ones. Best for teams that want a customizable, self-hosted setup they control end to end.

Aider plus Ollama: Pair Aider with a local model served by Ollama and you get a coding agent that never touches the cloud. Best for offline work, strict privacy, or anyone who wants to experiment with local models on real tasks.

How they stack up #

Tool | Autocomplete | Agentic editing | Privacy / self-host | Cost
Cursor | Strong | Strong | Limited | Paid, mid
Windsurf | Strong | Strong | Limited | Paid, mid
Zed | Good | Growing | Limited | Free / paid
Claude Code | N/A | Strong | Cloud | Usage-based
Aider | N/A | Strong | Yes (local models) | Free (pay model)
Amazon Q | Good | Good | AWS-scoped | Free / paid
Gemini Code Assist | Strong | Good | Cloud | Free tier / paid
JetBrains AI | Good | Good | Limited | Paid
Tabnine | Strong | Basic | Yes, full | Paid
Supermaven | Excellent | N/A | Limited | Free / paid
Continue.dev | Good | Good | Yes | Free (pay model)

How to pick #

Start from your hardest constraint, not the feature list.

Privacy is non-negotiable: Tabnine, Continue.dev, or Aider with a local model. These keep code in-house.

You want the best agent: Cursor, Windsurf, or Claude Code. Each handles multi-file work that Copilot still fumbles.

Autocomplete is all you need: Supermaven or Gemini Code Assist. Fast, cheap, minimal.

You are locked into a cloud: Amazon Q on AWS, Gemini Code Assist on GCP.

You want model choice and open tooling: Aider or Continue.dev. Swap models as the field moves.

You love your current IDE: JetBrains AI Assistant, or stay in VS Code with Continue.dev.

The call we'd make #

For most teams leaving Copilot, Cursor is the safest upgrade: it keeps your VS Code muscle memory and gives you agentic editing that actually lands. If you work from the terminal, add Claude Code or Aider for the heavy refactors. If privacy rules your world, Tabnine or a local Aider-plus-Ollama setup is the honest answer, even if the raw quality trails the hosted leaders slightly.

The larger point: Copilot is no longer the only serious option, and it is rarely the best one for a specific need. Pick the tool that matches your constraint, try it on a real task for a week, and switch without guilt if it underdelivers. For the full comparison across every category, see our guide to the best AI coding assistants.