Magic vs Sourcegraph
Two Coding AI tools, side by side. Both are verified against their own live sites. Here is what each does well and who it is for, so you can choose what fits.
Magic
www.magic.dev
Frontier code models to automate software engineering
Best forOrganizations exploring frontier models for large-context code automation
What it doesMagic builds frontier code models aimed at automating software engineering and research. Its approach combines frontier-scale pre-training, domain-specific reinforcement learning for code, and ultra-long context windows for handling large codebases and complex problems.
Capabilities
Visit Magic →- Frontier code models
- Domain-specific reinforcement learning
- Ultra-long context windows
- Inference-time compute optimization
Sourcegraph
www.sourcegraph.com
Code search and AI context across the whole codebase
Best forEnterprise engineering teams managing large multi-repository codebases who want reliable AI context
What it doesSourcegraph indexes entire codebases to give humans and AI agents complete context for search, oversight, and large-scale change. It supports natural-language and deterministic code search plus cross-repository batch changes.
Capabilities
Visit Sourcegraph →- Natural-language Deep Search with citations
- Deterministic code search across repositories
- MCP server for AI agent code intelligence
- Batch Changes for cross-repo refactors
- Code Insights analytics for migrations and risk
How to choose
Choose Magic if you are organizations exploring frontier models for large-context code automation. Choose Sourcegraph if you are enterprise engineering teams managing large multi-repository codebases who want reliable ai context. Both sit in Coding; the right pick depends on your exact workflow and budget.
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