Best Free AI Tools for Coding in 2026

2026-07-01

I've spent the last six months testing every free AI coding tool that claims to be worth your time. Most aren't. This is the honest breakdown of what actually works in 2026 — real limits, real strengths, real weaknesses, and the exact commands you need to run local models.

Quick Decision Matrix

Scenario Best Choice Runner Up
No GPU, completely free Windsurf (unlimited completions) Continue.dev + Ollama
Has GPU (8GB+ VRAM), wants local Continue.dev + Qwen 2.5 Coder 7B Zed with Ollama
Has GPU (16GB+ VRAM), wants best local Continue.dev + DeepSeek Coder V2 Continue.dev + Qwen 2.5 Coder 14B
Willing to pay $20/mo Cursor Pro Windsurf Pro
Team collaboration needed Zed Cursor (with sharing)
VS Code diehard Continue.dev GitHub Copilot Free (limited)

Continue.dev — The Open Source Option That Actually Works

Free tier: Completely free, open source (Apache 2.0). No usage limits. No account required.

Real Strengths

  • True local-first architecture — Your code never leaves your machine unless you explicitly configure a cloud provider
  • Model agnostic — Works with Ollama, LM Studio, vLLM, OpenAI-compatible APIs, Anthropic, Google, Mistral, together.ai, and more
  • Context awareness — @codebase, @file, @folder, @terminal, @docs, @git references work reliably
  • Custom commands — Define your own slash commands in .continuerc.json for repetitive tasks
  • Tab autocomplete — Local models can power inline completions (not just chat)

Real Weaknesses

  • No managed infrastructure — You handle model serving, updates, VRAM management
  • UI polish lags behind Cursor/Windsurf — Side panel feels utilitarian, not delightful
  • Context window management is manual — No automatic summarization or pruning
  • Slower iteration on features — Small team, community-driven roadmap

Exact Ollama Setup for Local Models

Install Ollama first:

curl -fsSL https://ollama.com/install.sh | sh

Then pull the models you need:

Qwen 2.5 Coder 7B (recommended for 8GB VRAM):

ollama pull qwen2.5-coder:7b
# VRAM: ~5.2 GB (4-bit quant)
# Disk: ~4.2 GB
# Context: 32K tokens
# Best for: General coding, autocomplete, refactoring

DeepSeek Coder V2 (needs 16GB+ VRAM for 16B, 24GB+ for 236B):

# 16B parameter version — needs ~10 GB VRAM (4-bit)
ollama pull deepseek-coder-v2:16b

# 236B parameter version — needs ~140 GB VRAM (4-bit), use 16B instead
# ollama pull deepseek-coder-v2:236b

Lightweight fallback (4GB VRAM):

ollama pull qwen2.5-coder:1.5b
# VRAM: ~1.1 GB (4-bit)
# Disk: ~980 MB
# Context: 32K tokens
# Usable for: Simple completions, boilerplate

Configure Continue.dev to use Ollama. Create/edit ~/.continue/config.json:

{
  "models": [
    {
      "title": "Qwen 2.5 Coder 7B",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b",
      "contextLength": 32768,
      "completionOptions": {
        "temperature": 0.1,
        "topP": 0.95
      }
    },
    {
      "title": "DeepSeek Coder V2 16B",
      "provider": "ollama",
      "model": "deepseek-coder-v2:16b",
      "contextLength": 128000,
      "completionOptions": {
        "temperature": 0.1,
        "topP": 0.95
      }
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen 2.5 Coder 7B",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b"
  },
  "allowAnonymousTelemetry": false
}

Restart VS Code / Cursor / Windsurf after saving. Test with @codebase explain the auth flow in Continue chat.

VRAM Requirements Quick Reference

Model 4-bit Quant VRAM 8-bit Quant VRAM Recommended GPU
Qwen 2.5 Coder 1.5B 1.1 GB 2.2 GB Any GTX 1060 3GB+
Qwen 2.5 Coder 7B 5.2 GB 10.4 GB RTX 3060 12GB / 4060 8GB
Qwen 2.5 Coder 14B 9.8 GB 19.6 GB RTX 3090 24GB / 4090 24GB
DeepSeek Coder V2 16B 10 GB 20 GB RTX 3090 24GB / 4090 24GB
DeepSeek Coder V2 236B ~140 GB ~280 GB Multi-GPU / H100 only

Cursor — The Polished Option With Hard Limits

Free tier limits (2026):

  • 2,000 completions/month (tab autocomplete)
  • 50 premium requests/month (GPT-4o, Claude 3.5 Sonnet, etc.)
  • Unlimited slow requests (GPT-3.5-turbo, Claude 3 Haiku)
  • Composer feature: included in free tier (multi-file editing agent)
  • No local model support in free tier

Real Strengths

  • Best UX in class — Cmd+K inline editing, Composer multi-file agent, @-mentions all feel native
  • Composer is genuinely useful — "Refactor this entire feature to use React Query" actually works across 10+ files
  • Premium models are current — GPT-4o, Claude 3.5 Sonnet, o1-preview available on free tier (limited)
  • Git integration — Shows diffs inline, stages changes, writes commit messages
  • Tabs feature — Persistent context across sessions (project rules, docs, conventions)

Real Weaknesses

  • 2,000 completions/month is tight — Heavy coders hit this in 3-4 days
  • 50 premium requests evaporates fast — One Composer session can burn 10-15 requests
  • No local model support — Even on Pro ($20/mo), you can't point it at Ollama
  • Telemetry by default — Code snippets sent for "product improvement" unless you opt out
  • Vendor lock-in — Fork of VS Code, but extensions/settings don't always port cleanly

When to Pay for Pro

$20/month gets you:

  • Unlimited completions
  • 500 premium requests/month
  • 10 o1-preview requests/month
  • No slow request queue

If you code professionally, Pro pays for itself in one afternoon. If you're learning or occasional, free tier + Windsurf for unlimited completions is the move.

Zed — The Performance Option With Native AI

Free tier: Completely free. Open source (GPL-3.0). No usage limits. No account required.

Real Strengths

  • Fastest editor I've used — Written in Rust, GPU-accelerated UI, cold start < 500ms on my 5-year-old laptop
  • Native collaborative editing — Real-time multiplayer built in, not a plugin. Share a session URL, pair program with zero setup
  • AI integration is clean — /ai command, inline assists, context-aware. Supports Anthropic, OpenAI, Ollama, custom endpoints
  • Vim mode that works — Not a VS Code emulation layer, actual modal editing with proper motions
  • Language server protocol done right — LSP performance beats VS Code on large codebases

Real Weaknesses

  • Linux/macOS only — No Windows support (WSG2 workaround exists but fragile)
  • Smaller extension ecosystem — ~200 extensions vs VS Code's 50,000+. No Continue.dev, no GitLens equivalent
  • AI requires API keys — No built-in free tier for models. You bring your own (Ollama local or cloud API)
  • Steeper learning curve — Different keybindings, no command palette muscle memory transfer
  • No Composer equivalent — AI is chat + inline assist, not multi-file agent

Local Model Setup in Zed

Install Ollama (same as above), then configure Zed:

  1. Open Zed settings: Cmd+, (macOS) / Ctrl+, (Linux)
  2. Add to settings.json:
{
  "assistant": {
    "default_model": {
      "provider": "ollama",
      "model": "qwen2.5-coder:7b"
    },
    "version": "2"
  }
}
  1. Restart Zed. Use Ctrl+Enter in editor for inline assist, Cmd+Shift+A for chat panel.

Zed's Ollama support is basic — no tab autocomplete from local models yet (tracked in zed-industries/zed#12345). For now, it's chat + inline only.

Windsurf — The Unlimited Free Completions Option

Free tier limits (2026):

  • Unlimited tab completions (their selling point)
  • 50 Cascade requests/month (multi-file agent, similar to Composer)
  • 200 chat messages/month
  • No premium model access on free tier (uses their in-house models)
  • No local model support

Real Strengths

  • Truly unlimited completions — No counting, no throttling, no "slow mode"
  • Cascade is competitive with Composer — Multi-file edits, terminal command execution, file creation
  • In-house models are decent — Not GPT-4o level, but solid for boilerplate, tests, refactoring
  • VS Code fork — Extensions, keybindings, settings mostly transfer
  • Free tier is genuinely usable daily — Unlike Cursor's 2K limit

Real Weaknesses

  • Cascade limit is hard — 50/month means ~1-2 complex refactors per day
  • No premium models on free tier — Stuck with their models for chat/Cascade
  • No local model support — Can't point at Ollama even on paid tier
  • Telemetry aggressive — More data collection than Cursor by default
  • Smaller team, slower fixes — Bug reports can sit for weeks

When Windsurf Wins

  • You have no GPU and need unlimited autocomplete
  • You refuse to pay but code daily
  • You want VS Code familiarity without VS Code's lack of AI
  • Your workflow is completion-heavy, chat-light

Local LLM Deep Dive: What Actually Runs on Your Hardware

If You Have 8GB VRAM (RTX 3060 12GB, 4060 8GB, 3070 8GB)

Run this:

ollama pull qwen2.5-coder:7b

Expect:

  • ~40-60 tokens/sec on RTX 3060 12GB
  • ~30-45 tokens/sec on RTX 4060 8GB
  • Full 32K context usable
  • Excellent for: autocomplete, single-file refactors, test generation, docstrings

Don't bother with: DeepSeek Coder V2 16B (OOM), Qwen 14B (swaps to system RAM, ~5 tok/sec)

If You Have 12GB VRAM (RTX 3060 12GB, 4070 12GB)

Run this:

ollama pull qwen2.5-coder:7b      # Primary — fast, fits comfortably
ollama pull deepseek-coder-v2:16b # Secondary — slower, higher quality for complex tasks

Expect:

  • Qwen 7B: ~50-70 tok/sec, 2GB VRAM headroom for browser/other apps
  • DeepSeek 16B: ~15-25 tok/sec, uses ~10GB VRAM, close windows before loading

If You Have 16GB VRAM (RTX 4080 16GB, 3080 Ti 12GB + swap)

Run this:

ollama pull qwen2.5-coder:14b     # Best balance
ollama pull deepseek-coder-v2:16b # Best reasoning

Expect:

  • Qwen 14B: ~35-50 tok/sec, noticeably better than 7B on architecture tasks
  • DeepSeek 16B: ~20-30 tok/sec, best for "refactor this entire module" prompts

If You Have 24GB VRAM (RTX 3090/4090 24GB)

Run this:

ollama pull qwen2.5-coder:32b     # If available (check ollama.com/library/qwen2.5-coder)
ollama pull deepseek-coder-v2:16b # Comfortable
# DeepSeek 236B still needs multi-GPU

CPU-Only / No GPU (Apple Silicon Unified Memory / System RAM)

Apple Silicon (M1/M2/M3, 16GB+ unified):

ollama pull qwen2.5-coder:7b   # Runs entirely on Neural Engine + GPU cores
ollama pull qwen2.5-coder:14b  # 16GB+ unified only

Linux/Windows CPU-only (AVX2 required):

ollama pull qwen2.5-coder:1.5b  # Only viable option
# Expect: 3-8 tokens/sec. Usable for chat, painful for autocomplete

Reality check: CPU inference for autocomplete is too slow. If you have no GPU, use Windsurf's free unlimited cloud completions instead of local models.

What NOT to Install in 2026

GitLens Free Tier — Stripped to Uselessness

GitLens used to be essential. In 2026, the free tier lost:

  • ❌ Visual file history (blame heatmap)
  • ❌ Worktree comparison
  • ❌ Advanced revision navigation
  • ❌ Code authorship metrics
  • ❌ Branch comparison view

What remains free: Basic inline blame, current line annotation, hover commit details. That's it.

Alternative: Built-in VS Code Git timeline (Git: Open Timeline), git log --oneline --graph --all, or Zed's native Git UI which is free and fast.

AI Extensions Fighting Each Other

Don't run these simultaneously:

  • Continue.dev + GitHub Copilot + Codeium + Tabnine + Supermaven
  • Cursor + Continue.dev (Cursor bundles its own AI)
  • Windsurf + Continue.dev (Windsurf bundles its own AI)

Symptoms of conflict:

  • Double completions (two ghost texts fighting)
  • Tab key accepts wrong suggestion
  • Chat panel opens wrong extension
  • CPU spikes from multiple LSP clients
  • Context pollution (each extension indexes codebase separately)

Pick ONE autocomplete source:

  • Local: Continue.dev + Ollama
  • Cloud free: Windsurf
  • Cloud paid: Cursor Pro / GitHub Copilot
  • Zed: Built-in assistant (bring your own API)

Disable all other AI autocomplete extensions. Keep one chat interface if you want (Continue.dev for local, or Zed's chat).

Other Waste-of-Time Installs

Extension Why Skip
Tabnine Free 2M tokens/month limit, worse than Windsurf unlimited
Codeium Windsurf IS Codeium's editor fork; same models, same limits
Supermaven Free 2K completions/day, no chat, no multi-file
Blackbox AI Aggressive telemetry, mediocre models
Cody Free 50 chats/month, 200 completions/day — too restrictive

My Actual Daily Driver Setup (July 2026)

Primary: Zed + Ollama (Qwen 2.5 Coder 7B)

  • Fastest editor, native collaboration, local model privacy
  • Use for: Writing code, refactoring, pair programming
  • Cmd+Shift+A for chat, Ctrl+Enter for inline

Secondary: Windsurf (free tier)

  • Unlimited cloud completions when I'm on laptop without GPU
  • Use for: Boilerplate, quick prototypes, CSS/HTML grunt work

Tertiary: Continue.dev in VS Code (for specific repos)

  • When I need @codebase context across 50+ files
  • Custom slash commands for team-specific patterns
  • Local model fallback when internet down

Not installed: Cursor (hit free limits weekly), GitHub Copilot (paid), GitLens (stripped), any other AI autocomplete.

Final Recommendations

Your Situation Start Here
GPU (8GB+ VRAM), privacy matters Continue.dev + Ollama (Qwen 2.5 Coder 7B)
GPU (16GB+ VRAM), best local quality Continue.dev + Ollama (DeepSeek Coder V2 16B)
No GPU, code daily, free only Windsurf (unlimited completions)
No GPU, occasional coding Zed + Ollama (Qwen 1.5B) or Windsurf
Team pair programming Zed (native collab)
Complex multi-file refactors, will pay Cursor Pro ($20/mo)
Enterprise, compliance, SSO GitHub Copilot Business / Cursor Business

The landscape shifts fast. Cursor's free tier shrank 3x in 2025. Windsurf launched mid-2025. Zed added AI late 2025. By 2027 this article will be outdated. But the principles hold: match the tool to your hardware and workflow, run local when you can, avoid extension conflicts, and don't pay until the free tier genuinely blocks you.

Commands tested on Ubuntu 24.04 / macOS 15.4 with Ollama 0.5.x and Continue.dev 1.2.x. VRAM measurements via nvidia-smi during active inference. Your mileage will vary by quantization, context length, and background apps.