Apr 20, 2026 · 3 min read

Cheapest AI Coding Setup in 2026 — From $0 to $5/Month

You don’t need to spend $20/month on Cursor or Claude Code to get AI-assisted coding. Here’s how to build a complete setup for $0-5/month that covers autocomplete, chat, and autonomous coding.

The $0 setup (completely free)

What you need

A computer with 16GB+ RAM (32GB ideal)
Ollama installed
Continue.dev VS Code extension
Aider for terminal

Step 1: Install Ollama and models

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

# Autocomplete model (fast, small)
ollama pull codestral:22b    # 12GB — best autocomplete
# OR if low on RAM:
ollama pull qwen3.5:4b       # 2.5GB — decent autocomplete

# Chat/reasoning model
ollama pull qwen3.5:27b      # 16GB — excellent quality
# OR if low on RAM:
ollama pull qwen3.5:9b       # 5GB — good quality

Step 2: Configure Continue.dev

Install the Continue extension in VS Code, then set your config:

{
  "models": [
    {
      "provider": "ollama",
      "model": "qwen3.5:27b",
      "title": "Qwen 3.5 27B"
    }
  ],
  "tabAutocompleteModel": {
    "provider": "ollama",
    "model": "codestral:22b",
    "title": "Codestral"
  }
}

Step 3: Set up Aider for terminal

pip install aider-chat
cd your-project
aider --model ollama/qwen3.5:27b

What you get

✅ Autocomplete in VS Code (like Copilot)
✅ Chat with AI about your code
✅ Terminal-based pair programming
✅ Complete privacy — nothing leaves your machine
✅ No API keys, no subscriptions, no limits

Limitations

Slower than cloud models (depends on your hardware)
Quality is good but not Claude Opus level
No Agent Swarm or parallel processing
Needs decent hardware (16GB+ RAM minimum)

The $3/month setup

Everything from the $0 setup, plus cloud models for complex tasks:

Add DeepSeek API

export DEEPSEEK_API_KEY="your-key"

# Use DeepSeek for complex tasks in Aider
aider --model deepseek/deepseek-chat

DeepSeek Chat costs $0.27/1M input tokens. A heavy coding day uses maybe 500K tokens = $0.14. That’s ~$3/month for daily use.

Or add GLM Coding Plan

The GLM Coding Plan at $3/month gives you access to GLM-5.1 — the #1 model on SWE-Bench Pro.

What you get (on top of $0 setup)

✅ Frontier-class model for complex tasks
✅ Local models for routine work (free)
✅ Cloud models for hard problems ($3/mo)

The $5/month setup

The sweet spot. Local models for daily work + OpenRouter credits for accessing any model when needed.

Add OpenRouter

export OPENROUTER_API_KEY="your-key"

# Use Claude for the hardest tasks
aider --model openrouter/anthropic/claude-opus-4.6

# Use DeepSeek for routine work
aider --model openrouter/deepseek/deepseek-chat

# Use free models for simple questions
aider --model openrouter/qwen/qwen-3.6-plus-preview

$5 of OpenRouter credits gets you:

~18M tokens of DeepSeek Chat
~500K tokens of Claude Opus
Unlimited tokens of free models

The optimal workflow

Autocomplete: Codestral locally (free, instant)
Quick questions: Qwen 3.5 locally (free, fast)
Complex coding: DeepSeek via OpenRouter (cheap)
Hardest problems: Claude Opus via OpenRouter (expensive, use sparingly)

Cost comparison

Setup	Monthly cost	Quality
$0 local	$0	Good (80% of Claude)
$3 DeepSeek	$3	Very good (90% of Claude)
$5 OpenRouter	$5	Excellent (access to everything)
GitHub Copilot	$10	Good
Cursor Pro	$20	Excellent
Claude Code Pro	$20	Best
Cursor + Claude	$40	Best

The $5/month setup gives you access to the same models as the $40/month setup — you just pay per token instead of a flat subscription. For moderate use (2-3 hours of AI coding per day), $5 is enough.

Hardware recommendations

Budget	Hardware	Best local model
Already own	Any Mac/PC with 16GB+	Qwen 3.5 9B
$600	Mac Mini M4 16GB	Qwen 3.5 4B
$1,150	Mac Mini M4 32GB	Qwen 3.5 27B + Codestral
$300	Used RTX 3060 12GB	Codestral 22B

The Mac Mini M4 32GB at $1,150 is the best value — it runs both a chat model and autocomplete model simultaneously.

Bottom line

AI coding doesn’t have to cost $20-40/month. A $0 setup with local models covers 80% of use cases. Adding $3-5/month of API access covers the remaining 20%. The key insight: use cheap/free models for routine work and expensive models only for the hardest problems.