Magic.dev
Frontier code models with 5M-token context, built to automate software engineering and research toward safe AGI.
Magic is a serious research bet for AGI labs, not a practical tool for everyday developers. Its 5M-token context and 100M-token research are impressive, but without a public API or product, it's not actionable now. If you need a shipping code assistant, Cursor or Copilot are the practical choices.
Verified 1d ago · liveness 60/100 · cite: rightaichoice.com/tools/magic-dev
- AI research labs needing advanced code generation with ultra-long context
- Enterprise teams exploring AGI safety and alignment via code automation
- Organizations with large-scale compute resources for RL-based model training
- Researchers interested in frontier model pre-training and inference systems
- Solo developers or small teams seeking a ready-to-use code assistant
- Budget-constrained users needing free or low-cost code generation
- Users requiring a public API or documented integration with existing tools
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Skip Magic if you need a ready-to-use code assistant with a public API, published pricing, or immediate integration into your current workflow.
Since access is by design partner waitlist only, there's no published pricing, so you can't plan for costs.
Pricing is not published; Magic is funded by $515M in venture capital, so costs are likely negotiated individually for design partners—not suitable for startups or individuals wanting predictable pricing.
In short
Magic.dev — Frontier code models with 5M-token context, built to automate software engineering and research toward safe AGI. Best for AI research labs needing advanced code generation with ultra-long context, Enterprise teams exploring AGI safety and alignment via code automation, Organizations with large-scale compute resources for RL-based model training. Contact Sales pricing.
What's new in Magic.dev
Checked yesterdayAcross the latest 3 updates: 2 launches and 1 news mention.
100M Token Context Windows
Magic announces research on 100 million token context windows, a partnership with Google Cloud, and new funding to advance ultra-long context models.
Introducing LTM-1
Magic's LTM-1 model features a 5 million token context window, enabling full-codebase analysis in a single pass.
Magic's $23M Series A
Magic raises $23M in Series A funding to advance AGI research and long-context models.
Viability Score
How well maintained and how widely used is Magic.dev? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- 5M token context window (LTM-1)
- 100M token context window research
- Domain-specific reinforcement learning
- Frontier-scale pre-training
- Inference-time compute optimization
- AGI Readiness Policy framework
- Vulnerability disclosure program
- Thousands of GB200s for training
- Google Cloud partnership
- Design partner waitlist access
- Automated AI research and code generation
About Magic.dev
Magic is building frontier code models designed to automate software engineering and research, with the stated mission of achieving safe AGI. The company combines frontier-scale pre-training, domain-specific reinforcement learning, ultra-long context windows, and inference-time compute optimization. Backed by $515 million from investors including Nat Friedman, Daniel Gross, CapitalG, Elad Gil, Sequoia, Jane Street, Eric Schmidt, and others, Magic operates thousands of GB200s and has partnered with Google Cloud. Magic's key technical achievement is the LTM-1 model, which features a 5 million token context window, enabling full-codebase analysis. The company has also announced research on 100 million token context windows, which would allow models to process entire large-scale codebases or multi-file projects in a single pass. This positions Magic at the forefront of long-context AI research, with potential applications in comprehensive code understanding, automated bug fixing, and large-scale refactoring. Magic emphasizes safety through its AGI Readiness Policy, which outlines how the company evaluates, monitors, and reduces existential risks of AI capabilities. The policy includes a vulnerability disclosure program, reflecting a commitment to responsible development. Access to Magic's technology is currently via a design partner waitlist, with no public API or ready-to-use product available for individual developers. Who is this for? Magic is aimed at well-funded AI research labs and enterprises exploring the frontier of code automation and AGI safety. It's not a consumer code assistant. Compared to practical alternatives like Cursor or GitHub Copilot, Magic trades immediate usability for research depth and long-context capability. If your goal is to push the limits of what code models can do, Magic is a project to watch; if you need to ship code today, look elsewhere.
Behind the Verdict
Magic is a frontier research lab, not a typical dev tool. Its LTM-1 model with a 5M-token context window is a genuine technical breakthrough that enables entire codebases to be processed in a single pass—something that sets it apart from incremental improvements seen in mainstream code assistants. The company's research direction toward 100M-token contexts, announced in August 2024, signals a clear roadmap for handling even larger repositories. However, the practical reality for most developers is that Magic is not a product you can use today. Access is strictly via a design partner waitlist, there's no public API, and pricing isn't published. This makes it nearly impossible for individual developers or small teams to evaluate it against tools like Cursor, GitHub Copilot, or Devin. The 'waitlist only' model also means you're essentially a research subject, not a customer. For well-funded research labs and enterprises exploring AGI safety and long-context automation, Magic's approach is compelling. Its partnerships with Google Cloud and significant compute resources (thousands of GB200s) indicate serious infrastructure investment. But the lack of a productized offering and unproven long-context inference economics (latency, cost) are significant risks for adoption. If you're a developer who needs to ship code today, Magic is not for you. If you're a research organization with the patience and budget to pilot frontier models, Magic is worth watching. But don't expect a plug-and-play experience anytime soon.
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Real-world workflow fit
Concrete scenarios for the personas Magic.dev actually fits — and what changes day-one when you adopt it.
Enabling the lab to apply for Magic's design partner program to test LTM-1 on real codebases.
Outcome: You gain early access to frontier long-context models, allowing you to benchmark against existing tools and potentially automate complex refactors.
Exploring whether Magic's 100M-token context research could help with cross-repo changes at scale.
Outcome: You evaluate the feasibility of using Magic for enterprise-scale code automation, understanding that it's still research-stage and may require significant infrastructure commitment.
Deciding whether to pursue Magic for long-context code understanding versus using established tools like Cursor.
Outcome: You conclude that Magic is not yet practical due to lack of API and waitlist access, and you stick with current tools for immediate needs.
Use Cases
- Feed an entire monorepo into a single prompt for a refactor that touches 40 files.
- Pilot whether long-context outperforms RAG-based Cursor on your real repository.
- Generate cross-service changes where call sites span multiple repos.
- Benchmark LTM-1 against Devin and Cursor on a fixed internal eval suite.
- Automate AI research and code generation to improve models and solve alignment.
Models Under the Hood
as of 2026-08-14
Limitations
- Magic is a research-oriented company not offering a public product; access is via design partner waitlist only.
- No public API is available.
- Model capabilities are evolving with ongoing research into ultra-long context windows (e.g., 100M tokens).
- Pricing details are not published on the website.
as of 2026-08-13
Verification history
We have re-verified Magic.dev 17 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 17 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Magic.dev's pricing actually pencils out — and where peers do it cheaper.
Pricing is not published; Magic is funded by $515M in venture capital, so costs are likely negotiated individually for design partners—not suitable for startups or individuals wanting predictable pricing.
Setup time & first value
How long it actually takes to get something useful out of Magic.dev — broken out by persona, not the marketing-page minute.
Design partner onboarding is not public; expect a multi-week or longer process given the waitlist and bespoke nature. For individual developers, there's no self-serve setup—it's an application process, so first value comes only after acceptance and collaboration.
Switching to or from Magic.dev
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Devin or Cursor: you can't directly migrate; you'd pilot Magic alongside your existing stack and decide based on results.
- ↗To Cursor or GitHub Copilot: if Magic doesn't meet production needs, switching is straightforward since it's not yet integrated into your workflow.
Integrations
Resources & Guides
- Resourcemagic.dev
Blog — Magic
Blog posts from the Magic team. Read about the latest in our AI research and engineering efforts.
- Resourcemagic.dev
Careers at Magic
We are a small team with a shared belief in the positive potential of responsibly deployed AGI. We value innate drive, creativity, and the ability to find clarity in uncharted domains.
- Resourcemagic.dev
Safety at Magic
Our goal is to automate software engineering and research towards safe superintelligence
Tutorials & Learning
Official links
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