Magic Review, Pricing & Alternatives (2026)

Magic is a San Francisco AI company founded in 2022 by Eric Steinberger and Sebastian De Ro. It develops code-generating systems intended to help software engineers write, review, debug, and plan code changes. Magic’s public positioning now emphasizes frontier code models, software-engineering automation, and AI research. The company says it combines frontier-scale pretraining, domain-specific reinforcement learning, ultra-long context, and inference-time compute. A 2024 funding announcement described a $320 million round and nearly $465 million raised in total. Magic’s homepage now reports $768 million raised and thousands of GB200s. Those are separate company updates, so the homepage figure is the latest public funding claim rather than an amount that should be added to earlier totals.

Magic’s most distinctive published capability is repository-scale context. LTM-1 used a 5-million-token context window and was described as able to see an entire code repository. A later research update described models reasoning over up to 100 million tokens during inference. Magic says this could place code, documentation, libraries, and private project material in one context. A typical workflow would provide a large software repository, ask for code synthesis or changes, and use the model’s response as part of an engineering task. Magic’s current homepage does not offer a signup flow for a general developer product. Its research posts describe model capabilities more clearly than they describe an available customer workflow.

Magic develops proprietary models and training systems. Its research combines long-context modeling with pretraining, reinforcement learning, and inference-time computation. In September 2026, Magic said its pretraining recipe was more than ten times as compute-efficient as leading open-weight base models. The company also said it matched DeepSeek V4 Pro Base with about 50 times fewer FLOPs, based on its own evaluations. Those results covered private code repositories, research papers, and other held-out data, and remain company-reported research claims. Magic’s homepage says it has thousands of GB200s. Its 2024 update also described a Google Cloud partnership. The published 2025 and 2026 material centers on training efficiency and long-context research, rather than a named generally available coding model.

Magic does not publish a pricing page, per-token rate, per-seat subscription, free tier, API rate card, student plan, business plan, or checkout flow. The practical entry is therefore Custom commercial access, with price, billing interval, and usage limits undisclosed. Buyers should not turn funding totals, hardware scale, or research context windows into a subscription estimate. Magic’s public site emphasizes research and recruiting, and the company’s research-access-only Magic offering is not a generally available paid product. No public page describes a standard launch date, quota, service-level agreement, or data-residency option. Contact may be required, but Magic does not publish prices and offers no self-serve plan or public API.

Magic’s public site does not document current supported IDEs, production limits, a public API, or a self-hosting package. GitHub Copilot and Cursor offer clearer individual onboarding, while Sourcegraph Cody Enterprise provides a more explicit business product. Magic’s 100-million-token context work is a concrete research distinction, but it is not a documented production entitlement. The company is best suited to AI research teams and large engineering organizations willing to discuss a bespoke evaluation. Pick GitHub Copilot or Cursor when immediate coding access and clearer pricing matter. Pick Sourcegraph Cody when enterprise administration and deployment details are the priority. Teams should confirm product availability, evaluation access, and data-handling terms directly before committing repositories or planning adoption.