Is Magic.dev available as a self-serve product or API?

No, there is no public API, free tier, or self-serve product available. Access is restricted to research partnerships and enterprise engagements.

What is the context window size of Magic.dev's models?

Magic has publicly discussed a 100M token 'LTM' model designed to load entire monorepos, documentation, and past PRs in a single inference session.

Who is the intended user for Magic.dev?

The tool is aimed at research labs, enterprise engineering orgs, and investors. It is not designed for individual developers or small teams needing immediate IDE assistance.

Magic.dev

✓ Editorially verified

Frontier code models with ultra-long context aimed at automating software engineering

Enterprise· No public pricing. Access is via research partnerships and enterprise engagements; no self-serve tier or public API published as of writing.CodingIn-house frontier code models (including a long-term-memory 'LTM' model family with reported 100M-token context)

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In short

Magic.dev develops proprietary foundation models designed to automate software engineering end-to-end using an ultra-long context window. It targets research labs and enterprises seeking whole-repo reasoning capabilities rather than individual developer tools. Access is currently limited to research partnerships and enterprise engagements.

Best for

Research labs, enterprise engineering orgs, and investors tracking frontier code-model progress and willing to engage through direct partnership rather than a self-serve product.

Skip if

Individual developers or small teams who need a working IDE assistant this quarter — there is no shipping product, API, or free trial to adopt.

Magic.dev is a San Francisco research lab building frontier-scale code models with the stated ambition of automating software engineering end-to-end. Rather than shipping a consumer IDE plugin, Magic trains proprietary foundation models specialised for code synthesis, reasoning over huge codebases, and long-horizon engineering tasks. The team's headline technical claim is an ultra-long context window (they have publicly discussed a 100M token 'LTM' model), which in principle lets a single inference session load an entire monorepo, its documentation, past PRs, and reviewer comments, then reason across all of it without RAG stitching. That is a materially different design point from Copilot-style assistants that lean on retrieval and short prompts.

The intended user is not the individual developer looking for autocomplete today. Magic is currently a research-forward organisation whose outputs are their models and the eventual agentic products built on them; the site is heavy on hiring, capabilities research, and infrastructure (they run thousands of GB200 GPUs and have partnered with Google Cloud), and light on self-serve product access. In 2024 they announced $465M+ in fresh capital led by names including Nat Friedman, Daniel Gross, and CapitalG, bringing total funding above $500M, which puts them in the same funding tier as other frontier labs. Typical workflows envisioned are 'assign a large engineering task to a model that has the entire codebase in context' — refactors, migrations, spec-driven feature implementation, autonomous PR generation, and long-running debugging — as opposed to line-by-line completion. Because there is no public API, no free tier, and no shipping IDE integration at the time of writing, evaluation is currently limited to partners and hires; treat Magic as a bet on where coding models are going rather than a tool you can pick up this afternoon.

Editor's take

Magic is one of the more interesting bets in the coding-model space because it is not trying to be a Copilot clone — the 100M-token context pitch, if it holds up in practice, is a genuinely different capability. But as of this writing it remains a research organisation with no product you can buy, so we file it under 'watch closely, cannot yet recommend'.

— The AI Tool Bible editorial team

Pros

✅ Ambitious ultra-long context research (100M-token class) that could obviate retrieval for whole-codebase reasoning
✅ Heavyweight funding and compute (thousands of GB200 GPUs) backing sustained frontier training runs
✅ Focused solely on code and software engineering, not a general-purpose chatbot spread thin across use cases
✅ Backed by credible technical investors (Nat Friedman, Daniel Gross, Sequoia, CapitalG) and Google Cloud infrastructure partnership
✅ Research direction targets autonomous engineering agents rather than narrow autocomplete, which is the direction the market is moving

Cons

⚠️ No public API, no self-serve product, and no published pricing — you cannot try it today
⚠️ Very little third-party benchmarking or independent evaluation of the long-context claims
⚠️ Site is heavier on mission statements and hiring than shipping product documentation
⚠️ Small team relative to incumbents (OpenAI, Anthropic, Google) it must compete with on model quality
⚠️ Enterprise-only access model means no community, no plugin ecosystem, no learning-in-public momentum

Use cases

Whole-repo refactorsLong-horizon feature implementationAutonomous PR generationLarge-scale codebase migrationSpec-driven engineering agentsAI research on code modelsLong-context reasoning benchmarks

Frequently asked

Is Magic.dev available as a self-serve product or API?: No, there is no public API, free tier, or self-serve product available. Access is restricted to research partnerships and enterprise engagements.
What is the context window size of Magic.dev's models?: Magic has publicly discussed a 100M token 'LTM' model designed to load entire monorepos, documentation, and past PRs in a single inference session.
Who is the intended user for Magic.dev?: The tool is aimed at research labs, enterprise engineering orgs, and investors. It is not designed for individual developers or small teams needing immediate IDE assistance.