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)

Visit website →

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