Magic.dev is live · custom pricing · last verified 7 Nov 2026 · viability monitor
Magic.dev
Magic.dev builds frontier code models with ultra-long context to automate software engineering and AI research.
Magic's September 2026 pretraining update is the most concrete public artifact: >10x pretraining efficiency plus progress toward trillion-parameter models changes what a lab can train on a fixed GPU budget. Long context stays the differentiator — 5M tokens in LTM-1, published work toward 100M. If you need working code assistance this quarter, pass; if you want a say in the model itself, this is one of the few labs to talk to.
Verdict generatedVerified 6d ago · liveness 63/100 · cite: rightaichoice.com/tools/magic-dev
- AGI-focused research labs exploring ultra-long-context code models
- Enterprises funding long-horizon code automation and model research R&D
- Organizations with large-scale compute and tolerance for research-stage partnerships
- Research teams tracking efficient pretraining and trillion-parameter scaling
- Solo developers or small teams needing a ready-to-use code assistant today
- Budget-constrained users looking for low-cost code generation
- Teams that need quick deployment without a design-partner relationship
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Skip Magic if you need a coding assistant your team can evaluate and deploy this quarter against a documented API, rather than a research partnership with a lab whose access runs through a design-partner waitlist.
Magic.dev's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
In short
Magic.dev — Magic.dev builds frontier code models with ultra-long context to automate software engineering and AI research. Best for AGI-focused research labs exploring ultra-long-context code models, Enterprises funding long-horizon code automation and model research R&D, Organizations with large-scale compute and tolerance for research-stage partnerships. Pricing is on request (custom quotes).
No head-to-head comparison for Magic.dev yet — the alternatives below are the closest matches we list.
What is Magic.dev?
Magic.dev — Magic.dev builds frontier code models with ultra-long context to automate software engineering and AI research. Best for AGI-focused research labs exploring ultra-long-context code models, Enterprises funding long-horizon code automation and model research R&D, Organizations with large-scale compute and tolerance for research-stage partnerships. Pricing is on request (custom quotes).
Magic is a research lab, not a product SKU. It builds frontier code models aimed at automating software engineering and AI research, on the explicit thesis that automating code and research is the most reliable route to safe AGI. Engagement runs through design-partner relationships and hiring, so what you get depends on the model you help shape. Four techniques carry the work: frontier-scale pretraining, domain-specific reinforcement learning, ultra-long context, and inference-time compute. Long context is the signature piece: the LTM-1 model reached a 5-million-token window, and Magic has published research on pushing context toward 100 million tokens — enough to reason over an entire codebase in one pass instead of chunked retrieval. In September 2026 the lab published a research update on compute-efficient pretraining, claiming more than 10x efficiency gains plus progress toward trillion-parameter models. Resources back the ambition: thousands of NVIDIA GB200s and $768 million raised from Nat Friedman, Daniel Gross, CapitalG, Elad Gil, Sequoia, Jane Street, Eric Schmidt and others. Safety is public too, through an AGI Readiness Policy framework and a vulnerability disclosure program. Who it's for: research organizations and well-funded engineering teams willing to co-develop a model with the vendor. If you want a coding assistant seat you can buy and deploy this quarter, Magic is the wrong aisle — hosted coding agents already occupy that shelf.
Key features
- Frontier-scale pretraining of code models
- Domain-specific reinforcement learning for code tasks
- Ultra-long context for whole-codebase analysis in one pass
- LTM-1 model with a 5-million-token context window
- Published research on extending context toward 100 million tokens
- Inference-time compute optimization
- Compute-efficient pretraining research claiming >10x efficiency gains (Sept 2026)
- Scaling research toward trillion-parameter models
- Training on thousands of NVIDIA GB200 GPUs
- $768 million raised from Nat Friedman, Daniel Gross, CapitalG, Elad Gil, Sequoia, Jane Street and Eric Schmidt
- AGI Readiness Policy framework
- Vulnerability disclosure program
- Design partner waitlist for model access
- Full-repository reasoning without chunked retrieval
- Research hiring across pretraining, kernels, inference & RL systems
Behind the verdict
Where Magic earns attention is the long-context research. 5M tokens in LTM-1 and published work toward 100M is a different bet from the retrieval-augmented pipelines most coding tools run. Whole-repository reasoning in a single pass matters if your codebase defeats chunking — monorepos, legacy migrations, cross-service refactors where the answer lives in six files nobody indexed together. The September 2026 pretraining update sharpens the pitch. Claiming >10x pretraining efficiency plus progress toward trillion-parameter models is a statement about training economics, and training economics are what separate a lab from an app. We'd read it as: less compute per capability, more headroom to scale. Pick Magic if you're a research org or a well-capitalized engineering team with a problem that genuinely needs frontier long-context code models, and you're comfortable engaging as a design partner rather than a customer. The $768 million and GB200 fleet mean the lab isn't going anywhere soon. Pass if you need an installable coding assistant with a published price this quarter. Nothing on the site suggests a self-serve product, and the public surface is research posts, a safety page, and job listings. Caveats worth naming: the headline efficiency and scaling numbers are the lab's own reported research, not independent benchmarks. And the design-partner model means your roadmap depends on someone else's research agenda. Closest alternative depends on your goal. For whole-codebase reasoning you can buy today, compare against long-context options from major model vendors. For a seat-licensed coding assistant, any hosted agent will get you shipping sooner.
Models under the hood
as of 2026-11-08
Limitations
- Magic presents itself as a research lab building frontier code models rather than a packaged product, so access and timelines are open-ended.
- The public evidence names LTM-1 with a 5-million-token context window and research toward 100M-token contexts, but the site provides no documentation of a customer-facing API, platform integrations, or product docs.
- The September 2026 pretraining update (>10x efficiency and progress toward trillion-parameter models) is a research claim published by the lab, not an independently benchmarked result you can verify on your own repository.
as of 2026-10-19
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Is Magic.dev still active?
Magic.dev is live · custom pricing · last verified 7 Nov 2026 · viability monitor.
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: November 2026
How we score →What's new in Magic.dev
Checked 5 days agoAcross the latest 1 update: 1 news mention.
What do users say about Magic.dev?
What people actually say about Magic.dev — is it worth it?
We scanned public community sources for Magic.dev on Oct 11, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 4 of the posts we fetched could be positively tied to Magic.dev. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Magic.dev — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 7 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Magic.dev 26 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.
- — changed our verdict, who should skip it, summary, description, features
- — changed limitations, pricing page
- — changed limitations, features, switching in, summary, switching out, who should skip it, our verdict, description, setup time, pricing fit
- — changed summary, description, features, who it suits, our verdict
- — changed switching in, limitations, description, use cases, who should skip it, our verdict, setup time, pricing fit, features, summary, switching out, hidden costs
- — changed our verdict, who it suits, description, features, who should skip it, summary
Showing the 6 most recent of 26 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Magic.dev cost in 2026?
Magic.dev: Pricing is on request (custom quotes). Pricing last verified 7 Nov 2026.
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.dev's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
Has Magic.dev's pricing changed?
No plan price for Magic.dev has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Magic.dev?
Pick Magic.dev if you are AGI-focused research labs exploring ultra-long-context code models, Enterprises funding long-horizon code automation and model research R&D, Organizations with large-scale compute and tolerance for research-stage partnerships, Research teams tracking efficient pretraining and trillion-parameter scaling.
Look elsewhere if you are Solo developers or small teams needing a ready-to-use code assistant today, Budget-constrained users looking for low-cost code generation, Teams that need quick deployment without a design-partner relationship, Buyers who require independently benchmarked performance claims before committing.
Skip it if Skip Magic if you need a coding assistant your team can evaluate and deploy this quarter against a documented API, rather than a research partnership with a lab whose access runs through a design-partner waitlist.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Magic.dev actually fits — and what changes day-one when you adopt it.
You have a 3-million-token monorepo and a RAG-based assistant that keeps missing cross-file dependencies. You engage Magic through the design-partner waitlist and feed the whole repository in a single pass.
Outcome: You get a same-context comparison of whole-repo reasoning versus chunked retrieval on your own code, which is the only evaluation that matters for your architecture decision.
You want a stake in the model class that comes next rather than another seat license, and you have GPU budget and a research team to spend on it.
Outcome: A research partnership where your team shapes the model and gets early access, in exchange for open-ended timelines instead of a delivery date.
You are tracking whether automating AI research actually accelerates alignment work, and you need the lab's stated framework rather than marketing copy.
Outcome: You can read Magic's AGI Readiness Policy and vulnerability disclosure program alongside its published pretraining research to judge whether the safety posture matches the ambition.
Use cases
- Feed an entire monorepo into a single prompt for a refactor that touches 40 files.
- Test whether ultra-long context outperforms a RAG-based assistant on your real repository.
- Generate cross-service changes where call sites span multiple repositories.
- Benchmark LTM-1 against hosted coding agents on a fixed internal eval suite.
- Automate AI research and code generation to improve models and work on alignment.
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.
There is no signup-to-first-value clock here. Access is gated by a design-partner waitlist, so the realistic timeline is however long it takes to scope an R&D partnership with the lab — weeks to months of conversation, not an afternoon of onboarding.
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 a RAG-based coding assistant: replace chunk-and-retrieve with a single whole-repository pass once design-partner access is granted.
- →From an in-house long-context prototype: benchmark your model against LTM-1's documented 5-million-token window on a fixed internal eval suite.
- ↗To a hosted coding agent: if you need working code assistance before a partnership delivers, run a hosted agent in parallel rather than waiting.
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
Magic.devレビュー：このAIは本当にアプリを構築できるのか？
TWiz
Magic.dev CEO Eric Steinberger氏、開発者のバイオニック化について語る | E1744
This Week in Startups
Codenvy Magic - "Dev Mode" a Runtime
Codenvy
YouTube returned 6 videos for “Magic.dev”, and we withheld 1: 1 did not mention Magic.dev. Showing the 5 we can prove are about Magic.dev.
Official links
What are the best Magic.dev alternatives?
The closest alternatives to Magic.dev we list are The LLM Data Company, Falcon LLM and Zhipu GLM. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| The LLM Data Company | Contact Sales | Custom pricing | — |
| Falcon LLM | Free | Free | — |
| Zhipu GLM | Freemium | Pricing not published | — |
Alternatives to Magic.dev
View all
The LLM Data Company
Open-weight frontier models trained with reinforcement learning for domain knowledge work in finance, law, consulting and healthcare.
Falcon LLM
Falcon LLM: Apache 2.0 open-weight models for Arabic, Emirati and multimodal AI you self-host
Tools that pair well with Magic.dev
Common stack mates teams adopt alongside Magic.dev, with the specific reason each pairing earns its keep.
The LLM Data Company
Open-weight frontier models trained with reinforcement learning for domain knowledge work in finance, law, consulting and healthcare.
Falcon LLM
Falcon LLM: Apache 2.0 open-weight models for Arabic, Emirati and multimodal AI you self-host
Zhipu GLM
Zhipu GLM delivers open-weight GLM-5.3 models with 1M-token context, MaaS APIs, and shipped computer-use agents.
Frequently asked questions
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