A frontier LLM built to act as a long-horizon pair programmer — trained on code, tuned for engineers who need real depth, not just autocomplete.
Magic.dev (Magic.dev, Inc.) is one of the more ambitious bets in AI coding: build a frontier model whose entire purpose is to reason over long sessions with an engineer — across many files, many turns, and many hours. Their 2024 LTM-2-mini demo showed 100M-token context. In 2025–2026 they shipped research previews aimed at engineering teams who feel Copilot-class autocomplete is hitting a ceiling.
Who it's for: Senior engineers and teams working in large, unfamiliar codebases where a 200K-token window isn't enough — think platform migrations, multi-repo refactors, and deep debugging sessions that span hours.
Engineered for million-token-plus context windows with retrieval tuned for code rather than prose. Useful for "read the whole monorepo, then answer" workflows.
Designed to keep state across many turns — you can reference a decision made 90 minutes ago without re-pasting context. Closer to a junior engineer's working memory than a chatbot.
Built specifically for code understanding and generation rather than a general-purpose LLM fine-tuned for code. Their pitch: tooling matters less when the base model is right.
Free during research preview (waitlisted). No public paid tier pricing as of July 2026 — Magic.dev is targeting enterprise and team contracts rather than individual subscriptions. Expect this to be priced like an engineering seat, not like a $20/mo chatbot.