Magic.dev
Contact SalesFrontier code models to automate software engineering and research towards safe AGI.
By Tanmay Verma, Founder · Last verified 26 Jun 2026
4.5k views
Added 4/28/2026
68/100MonitorIn short
Magic.dev — Frontier code models to automate software engineering and research towards 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.
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Editorial Verdict
Best for
AI research labs needing advanced code generation with ultra-long contextEnterprise teams exploring AGI safety and alignment via code automationOrganizations with large-scale compute resources for RL-based model trainingResearchers interested in frontier model pre-training and inference systems
Not ideal for
Solo developers or small teams seeking a ready-to-use code assistantBudget-constrained users needing free or low-cost code generationUsers requiring public API or documented integration with existing toolsNon-research teams needing quick deployment with limited infrastructure
Magic is for well-funded AI labs pushing the frontier of long-context code generation. Unless you have deep pockets and research-grade infrastructure, look elsewhere—there is no public API or ready-to-use tooling yet. If you need a practical code assistant today, consider Cursor or GitHub Copilot instead.
Skip Magic.dev if Skip Magic if you need a ready-to-use code assistant today — it has no public API or self-serve access, only a waitlist.
Compare with: Magic.dev vs Claude, Magic.dev vs Poolside AI, Magic.dev vs Draftbit
Last verified: June 2026
What's new in Magic.dev
Updated 3 days agoAcross the latest 3 updates: 2 launches and 1 pricing change.
LaunchBlog·Aug 29Newest
100M Token Context Windows
Magic announces 100 million token context windows, partnership with Google Cloud, and new funding.
LaunchBlog·Jun 6
Introducing LTM-1
Magic's LTM-1 model has a 5 million token context window, targeting full codebase analysis.
PricingBlog·Feb 6
Magic’s $23M Series A and a note on finding meaning in an automated world
Magic raises $23M Series A to advance AGI research and long-context models.
Viability Score
68/100
MonitorHow likely is Magic.dev to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
38
funding runway
70
website health
90
wrapper dependency
100
Last calculated: June 2026
How we score →Key Features
- LTM-1 model with 5 million token context window
- 100 million token context window research
- Domain-specific reinforcement learning
- Frontier-scale pre-training
- Inference-time compute optimization
- AGI Readiness Policy and safety framework
- Vulnerability disclosure program
- Thousands of GB200s for training
- Partnership with Google Cloud
- Automated AI research and code generation
About Magic.dev
Contact SalesIntermediateAPI availableWeb · API · Desktop · CLI
Magic is building frontier code models aimed at automating software engineering and AI research, with a stated mission of achieving safe AGI. The company combines frontier-scale pre-training, domain-specific reinforcement learning, ultra-long context windows (up to 100 million tokens), and inference-time compute optimization. Backed by $515 million from investors including Nat Friedman and Daniel Gross, Magic operates thousands of GB200s and has partnered with Google Cloud. Key features include the LTM-1 model with a 5 million token context window, a 100 million token context research update, and an AGI Readiness Policy with a vulnerability disclosure program. Unlike general code assistants, Magic targets well-funded AI labs and researchers focused on scaling code automation toward AGI, rather than everyday developer productivity. There is no public API or ready-to-use product; access is currently via a design partner waitlist.
Behind the Verdict
Magic is not a typical code assistant; it's an ambitious research effort targeting safe AGI through code automation. Its strengths lie in ultra-long context (100M tokens), massive compute (thousands of GB200s), and a strong safety framework (AGI Readiness Policy). However, it has no public self-serve access—only a design partner waitlist. Pricing is unpublished, and long-context inference costs remain unproven at scale. For solo developers or teams needing immediate productivity, Magic is not the right fit. It excels for AI labs with large budgets and research infrastructure. The LTM-1 model with 5M token context is unique, but the lack of integrations beyond Google Cloud limits practical use. Magic's approach is high-risk, high-reward; if you're exploring AGI alignment via code automation, it's worth watching, but not ready for mainstream adoption.
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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.
AI research lab lead
A lab wants to refactor a 40-file monorepo with cross-service dependencies.
Outcome: Ingests the entire repo into Magic's LTM-1 model, gets a unified refactor plan, and applies changes with minimal manual intervention.
Enterprise AGI safety researcher
An organization needs to evaluate long-context models for alignment research.
Outcome: Uses Magic's 100M token context and vulnerability disclosure program to test and report safety issues, contributing to AGI readiness.
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
LTM-1 (5M token context)100M token context model (research)
Limitations
- No public self-serve access — design-partner / waitlist only as of 2026-Q1.
- Pricing not published.
- Long-context inference economics are still being proved at production scale; latency and cost per call on full-repo prompts remain open questions.
- Language coverage information is limited; assume Python / TypeScript / Go are best supported.
- No on-prem option disclosed.
Integrations
Google Cloud
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.
Magic's pricing is unpublished and contact-based, targeting well-funded AI labs. For smaller teams, Cursor or Copilot are far more cost-effective.
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.
As a design-partner product, setup is hands-on with Magic's team. Expect weeks to months of collaboration before achieving first meaningful results.
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
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