AI Coding in the Terminal: OpenCode vs Continue.dev vs Aider vs Zed vs Void

The landscape of AI-assisted coding has exploded in 2026. But if you’re a developer who values open source, privacy, and terminal-first workflows, the options narrow fast. I tested five free and open-source tools that run on both macOS and Linux (Arch) to find the best setup for real daily work.

This is not a surface-level feature list. I’ve used all five tools on production projects — editing code, committing, reviewing, and deploying. Here is what each one actually feels like, where each one breaks, and how to build a cost-effective multi-provider setup.

The five contenders

All five are free, open source, and run on macOS + Linux. No SaaS subscription required — you bring your own API keys (Anthropic, OpenAI, Mistral, Google, or local models via Ollama).

1. OpenCode — The AI Orchestrator

OpenCode is a full terminal UI for AI-assisted development. But calling it “a chat in the terminal” misses the point entirely. OpenCode is an orchestration platform with multi-agent workflows, skill systems, and parallel task delegation.

What it does well

Multi-agent architecture. This is OpenCode’s killer feature and nothing else comes close. You define agents (Atlas for orchestration, Sisyphus for execution, Oracle for architecture consultation, Explorer for codebase search) that work in parallel. When you ask OpenCode to “add analytics to all 20 repos,” it fans out agents to each repo simultaneously — editing, committing, and verifying in parallel.

Skills and plugins. OpenCode has a skill system where you load specialized workflows — SEO audits, copywriting, frontend design, security reviews. Skills inject domain-specific instructions and tool access into agents. This means your AI doesn’t just know “how to code” — it knows how to do SEO audits, or write marketing copy, or review UI against accessibility guidelines.

Background agents. You can fire off exploration tasks in the background (run_in_background=true) and continue working. The system notifies you when results arrive. For large codebases, this is transformative — you can have 5 agents searching different parts of your code simultaneously.

Session continuity. Every agent session has a continuation ID. If an agent fails, you resume exactly where it left off with full context preserved. No re-reading files, no repeating setup.

Git-native. Conventional commits, PR creation via gh, branch management — all built in. OpenCode doesn’t fight your git workflow.

Where it struggles

No GUI. If you want visual inline diffs, syntax highlighting of proposed changes, or a file tree, you’re in a terminal. This is fine for terminal-native developers but can be disorienting if you’re used to VS Code’s AI experience.

Learning curve. The agent system, categories (quick, deep, ultrabrain, visual-engineering), skills, MCPs, and delegation patterns take time to internalize. Expect 2–3 days of feeling lost before it clicks.

Config-heavy. Between opencode.json, skill files, agent definitions, and MCP server configs, there’s a lot of JSON/YAML to manage. The flexibility is worth it, but the initial setup is not trivial.

Best for

Developers who want autonomous multi-agent workflows and live in the terminal. If you work across multiple repos, juggle parallel tasks, or want AI that can delegate to itself — this is the only tool that does it.

2. Continue.dev — AI Inside Your IDE

Continue is an extension for VS Code and JetBrains that brings AI chat, inline edits, and tab-autocomplete directly into your editor. Think of it as an open-source GitHub Copilot that works with any LLM.

What it does well

Tab autocomplete. This is Continue’s strongest feature. As you type, Continue suggests completions inline — just like Copilot. The difference: you can point it at any model (Codestral, DeepSeek, CodeLlama, or a local model via Ollama). The autocomplete is fast, context-aware, and gets smarter as you use it.

Chat sidebar + inline edits. Highlight code, press a shortcut, and Continue opens a chat with that code as context. Ask it to refactor, explain, or write tests. The response appears inline — you accept or reject with a single keypress. This workflow is smooth and doesn’t break your flow.

Codebase indexing. Continue builds embeddings of your codebase so the AI has context beyond just the file you have open. This means better answers about how different parts of your project connect.

Any provider. Continue works with every major LLM provider and local models. Your config is a set of JSON files (.continue/config.json) that you can version-control and share across a team.

Where it struggles

Requires VS Code or JetBrains. Continue is not a standalone tool — it’s an extension. If your editor isn’t VS Code or a JetBrains IDE, you’re out of luck. This also means Electron-level RAM usage.

Autocomplete can be noisy. Sometimes the suggestions are wrong or distracting. You can tune the model and context, but finding the right balance takes effort.

Single-agent only. No parallel work, no background tasks, no agent delegation. Continue is one AI having one conversation at a time.

Best for

Developers who want AI inside their existing IDE with Copilot-style autocomplete. If you already live in VS Code and just want to replace Copilot with an open-source alternative that works with any model — Continue is the answer.

3. Aider — The CLI Pair Programmer

Aider is a command-line tool that lets you chat with an AI to edit code in your local git repo. It is the simplest, most focused tool on this list. You run aider, tell it which files to edit, and have a conversation.

What it does well

Dead simple to start. Install with pip install aider-chat, run aider main.py, and start talking. No config files, no agent system, no plugins. Just you and the AI.

Best-in-class git integration. Every change Aider makes is automatically committed with a descriptive message. Want to undo? git reset HEAD~1. This is a detail that every other tool gets wrong — Aider gets it right. Your code history always tells the story of what the AI changed.

Repo map. Aider builds a tree of your codebase (functions, classes, imports) and sends it as context. This means it can reason about multi-file changes without you manually specifying every file.

Multi-file editing. Aider can edit multiple files in a single request. “Move the auth logic from server.ts to auth.ts and update all imports” — it handles the whole thing.

Architect mode. Before making changes, you can ask Aider to propose a plan. Review the plan, then tell it to execute. This two-step workflow prevents unwanted edits.

Where it struggles

Single-agent only. One conversation, one model, one task at a time. No parallel work, no background agents, no delegation.

Aggressive edits. Aider sometimes modifies more code than you expected. The git commits make rollback easy, but the initial surprise can be frustrating.

No plugin system. What you see is what you get. There’s no way to add domain-specific workflows, skills, or custom tools.

Token costs on large repos. The repo map is useful but can consume a lot of tokens on large codebases. Monitor your API usage.

Best for

Developers who want the simplest possible AI pair programmer in the terminal. If “chat → code → commit” is your preferred workflow and you don’t need multi-agent orchestration, Aider is perfect.

4. Zed — The Fast Editor with AI

Zed is a Rust-native code editor (not a VS Code fork) that’s built for speed. It has a built-in AI panel that works with Anthropic, OpenAI, Ollama, and Google models.

What it does well

Blazing fast. This cannot be overstated. Zed opens instantly, scrolls through 10,000-line files without stuttering, and uses ~50MB of RAM. Compared to VS Code’s ~500MB+, the difference is dramatic. If you’ve ever been frustrated by Electron sluggishness, Zed is the cure.

Beautiful editing experience. Multiple cursors, project-wide search, integrated terminal, git integration — Zed has all the editor fundamentals done right. The typography is crisp, the keybindings are intuitive, and everything feels native because it is.

Built-in AI panel. No extension needed. Open the assistant panel, pick your model, and chat. You can attach files, highlight code for context, and inline-edit directly. The AI experience is lightweight but functional.

Collab editing. Real-time multiplayer editing like Google Docs. Share a link, and teammates can edit the same file simultaneously. Unique among editors in this list.

Where it struggles

AI is basic. No agents, no multi-step workflows, no skill system. The AI panel is a chat — ask a question, get an answer, apply edits. That’s it. If you’re used to OpenCode’s agent orchestration or Cursor’s multi-file reasoning, Zed’s AI feels limited.

No inline autocomplete (yet). Zed is working on Copilot-style tab completion, but as of this writing it’s not production-ready. This is the biggest gap versus Continue or Void.

Limited extension ecosystem. Zed is young. The extension system exists but has far fewer extensions than VS Code. You’ll miss some language servers, themes, and niche tools.

Linux support is newer. Zed started on Mac. Linux support works but has occasional edge cases with Wayland, IME input, and some keybindings.

Best for

Developers who want a fast, native editor with light AI assistance. If you prioritize editing speed above AI features, and just want a chat panel for occasional questions, Zed is ideal.

5. Void — Open-Source Cursor

Void is a fork of VS Code with built-in AI features — autocomplete, chat, and inline edits. Think of it as Cursor but fully open source.

What it does well

Familiar VS Code experience. Since Void is a VS Code fork, everything you know about VS Code works. Extensions, settings, keybindings, themes — all carry over. The learning curve is zero if you’re a VS Code user.

Built-in AI, no extension needed. Void ships with autocomplete, chat, and inline editing. No need to install Continue or Copilot — it works out of the box.

Privacy-focused. Void is designed to be local-first. Data stays on your machine by default. This is a meaningful difference from Cursor, which sends telemetry.

Open source. Fully auditable codebase. You can see exactly what happens with your code and data.

Where it struggles

Behind Cursor in features. Cursor has agent mode, multi-file reasoning, automatic context selection, and a polish that comes from a well-funded team. Void is missing most of these. It’s a solid VS Code fork with basic AI, not a Cursor clone.

Small community. Fewer contributors, fewer updates, less testing across edge cases. If you hit a bug, you might be on your own.

Electron baggage. Since it’s a VS Code fork, it inherits all of Electron’s downsides — high RAM usage, slower startup, non-native feel on macOS.

Thin documentation. Getting started is easy, but configuring advanced workflows or troubleshooting issues requires digging into GitHub issues.

Best for

Developers who want an open-source Cursor alternative without a subscription. If you like VS Code’s AI experience but want it open source and privacy-respecting, Void is the closest match.

Comparison Matrix

The Setup I Recommend: OpenCode + Multi-Provider Routing

After months of testing, here is the setup that gives you maximum AI capability at minimum cost. The strategy: use the cheapest model that can handle the task, reserve expensive models for hard problems.

Provider Mix

┌─────────────────────────────────────────────────────┐
│                    PROVIDER MIX                      │
│                                                      │
│  ┌─────────────┐  ┌──────────┐  ┌───────────────┐  │
│  │  Anthropic   │  │ Mistral  │  │  Ollama (local)│  │
│  │  (min budget)│  │ (daily)  │  │  (free/offline)│  │
│  └──────┬──────┘  └────┬─────┘  └───────┬───────┘  │
│         │              │                │           │
│  Claude Sonnet    Mistral Large    DeepSeek-Coder   │
│  (complex only)   Codestral        Qwen 2.5 Coder  │
│                   (daily driver)    (offline)       │
└─────────────────────────────────────────────────────┘

Full opencode.json

{
  "provider": {
    "anthropic": {
      "apiKey": "sk-ant-YOUR_KEY",
      "models": {
        "claude-sonnet-4-20250514": {
          "thinking": true,
          "contextWindow": 200000
        }
      }
    },
    "mistral": {
      "apiKey": "YOUR_MISTRAL_KEY",
      "models": {
        "mistral-large-latest": {},
        "mistral-medium-latest": {},
        "codestral-latest": {}
      }
    },
    "ollama": {
      "models": {
        "deepseek-coder-v2:16b": {},
        "qwen2.5-coder:7b": {}
      }
    }
  },
  "agents": {
    "atlas": {
      "model": "anthropic:claude-sonnet-4-20250514"
    },
    "quick": {
      "model": "mistral:codestral-latest"
    },
    "writing": {
      "model": "mistral:mistral-large-latest"
    },
    "visual-engineering": {
      "model": "mistral:mistral-large-latest"
    },
    "deep": {
      "model": "anthropic:claude-sonnet-4-20250514"
    },
    "ultrabrain": {
      "model": "anthropic:claude-sonnet-4-20250514"
    },
    "unspecified-low": {
      "model": "mistral:codestral-latest"
    },
    "unspecified-high": {
      "model": "mistral:mistral-large-latest"
    },
    "explore": {
      "model": "mistral:codestral-latest"
    },
    "librarian": {
      "model": "mistral:mistral-large-latest"
    },
    "oracle": {
      "model": "anthropic:claude-sonnet-4-20250514"
    }
  }
}

When to Use What

Cost Optimization Rules

1. Mistral is your daily driver — route 80% of tasks there.
2. Anthropic only for orchestration, architecture decisions, and hard bugs that Mistral can’t crack.
3. Ollama as fallback — when you hit rate limits, want privacy, or are offline.
4. Use category="quick" in OpenCode — it routes to the cheapest model automatically.
5. Avoid ultrabrain/deep unless necessary — these always burn your most expensive credits.

Going Fully Local: Ollama Setup

If you want AI coding without any cloud dependency — for privacy, cost, or offline work — Ollama runs models locally on your machine.

Hardware Requirements

Recommendation: 16 GB RAM is the sweet spot. DeepSeek Coder V2 at 16B parameters gives you production-quality code assistance locally. If you’re on 8 GB RAM, use Qwen 2.5 Coder 7B.

Installation

macOS:

brew install ollama

Arch Linux:

yay -S ollama

Pull and Run Models

# Start the Ollama server
ollama serve

# Pull the models you want
ollama pull deepseek-coder-v2:16b
ollama pull qwen2.5-coder:7b
ollama pull llama3.1:8b

# Test it
ollama run deepseek-coder-v2:16b "Write a Python function that validates email addresses"

Configure Ollama as a Systemd Service (Arch)

# Create user-level systemd service
mkdir -p ~/.config/systemd/user

cat > ~/.config/systemd/user/ollama.service << 'EOF'
[Unit]
Description=Ollama AI Server
After=network.target

[Service]
Type=simple
ExecStart=/usr/bin/ollama serve
Restart=always
RestartSec=3

[Install]
WantedBy=default.target
EOF

# Enable and start
systemctl --user daemon-reload
systemctl --user enable --now ollama

Using Local Models with Each Tool

Quick-Start: Arch Linux Install Commands

# OpenCode
yay -S opencode

# Aider (Python)
pip install aider-chat

# Zed
yay -S zed-editor

# Continue (inside VS Code)
code --install-extension continue.continue

# Void
yay -S void-editor

# Ollama (local models)
yay -S ollama
ollama pull deepseek-coder-v2:16b

Decision Guide

You want…

Maximum AI autonomy with multi-agent workflows: → OpenCode. Nothing else does parallel agent delegation, skill injection, and background tasks. If you work across multiple repos and want AI to handle entire workflows (not just edit files), this is it.

AI inside your existing IDE with tab autocomplete: → Continue.dev. Copilot-quality autocomplete, works with any model, lives inside VS Code or JetBrains. The smoothest IDE integration in this list.

The simplest terminal AI — edit, commit, done: → Aider. pip install, aider file.py, talk. Every change is a git commit. Zero config. Maximum focus.

The fastest editor with light AI: → Zed. Rust-native speed, beautiful editing, built-in AI panel. For developers who prioritize editor performance over AI features.

Open-source Cursor without the subscription: → Void. VS Code fork with built-in AI, privacy-focused, zero config. The easiest transition if you’re coming from Cursor.

Fully local, zero cloud: → OpenCode or Aider + Ollama. Complete offline AI coding with no API keys, no data leaving your machine.

The Bottom Line

There is no single “best” tool. The right choice depends on your workflow:

• If you want AI to work for you autonomously across multiple projects — OpenCode.
• If you want Copilot in your IDE with your own models — Continue.
• If you want simple, fast, git-integrated AI coding — Aider.
• If you want a blazing-fast editor with occasional AI help — Zed.
• If you want Cursor without the subscription — Void.

My setup: OpenCode for orchestration (the multi-agent workflows are unmatched), Aider for quick one-off edits (the git integration is perfect), and Ollama for offline coding (free, private, runs on my laptop). Three tools, three use cases, zero subscriptions.