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Pricing sourced from Aider · Last verified March 2026
Aider is terminal-based, open-source (MIT licensed), and supports any LLM with pay-per-use API pricing. Cursor is a GUI IDE fork of VS Code with a $20/month Pro subscription that bundles model access. Choose Aider for command-line workflows, clean Git history, and model flexibility; choose Cursor for visual inline suggestions, chat panels, and a traditional IDE experience. Aider also has no vendor lock-in — if Anthropic or OpenAI pricing changes, you switch providers with a flag.
Light developers typically spend $10-30/month on API calls; heavy users $50-100/month. A typical session costs $0.50-$2.00 with Claude 3.5 Sonnet, $1.50-$6.00 with GPT-4 Turbo, and just $0.02-$0.10 with DeepSeek Coder. There's no built-in cost tracking, so you'll need to monitor your Anthropic, OpenAI, or DeepSeek dashboard to avoid surprise bills. Users on Reddit and Hacker News have reported burning $15-20 in a single long refactoring session.
Yes — Aider supports local LLMs through Ollama and LM Studio, making it completely free to run if you have the hardware. You'll need at least 16GB of RAM, with 32GB+ recommended for larger models. Be aware that local models produce meaningfully lower quality edits than frontier cloud APIs like Claude 3.7 Sonnet or GPT-4o, especially for complex multi-file refactors. Most users run a cheap cloud model like DeepSeek for quality and keep local as a fallback.
Aider works well on projects under 50,000 lines thanks to its repo map feature, which builds a compressed understanding of your codebase structure. Projects above 100K lines routinely hit context window limits, causing the tool to miss relevant files or produce inconsistent edits. For massive monorepos, Sourcegraph Cody or Cursor's indexed codebase search tend to perform better. You can mitigate Aider's limits by manually adding specific files to the chat rather than relying on automatic discovery.
The 88% singularity metric means that roughly 88% of Aider's own source code was written by Aider itself — a self-referential benchmark showing the maintainers use their own tool in production. This is reported alongside 44K GitHub stars, 6.8M installs, and 15 billion tokens processed per week. It's a credibility signal: the tool is mature enough to build itself. For users, it suggests the workflow is battle-tested on a real, non-trivial Python codebase.
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