Nemotron 3 Super scores 60.47% on SWE-Bench Verified, a rigorous benchmark that evaluates the model’s ability to solve real software engineering problems requiring code generation, debugging, and system understanding.
SWE-Bench Verified tests the model on actual GitHub issues and pull requests, measuring whether the model can write correct code patches independently. A 60.47% score demonstrates strong capability for code generation, bug fixing, and architectural reasoning—tasks that require understanding complex interdependencies across files and systems.
When you integrate Nemotron 3 Super with Milvus for code-aware RAG, the model can leverage your vector-stored codebase, documentation, and previous solutions. You can store code embeddings, API documentation, and architectural patterns in Milvus, allowing Nemotron 3 Super to retrieve relevant context during code generation. Using Milvus with LangChain shows how to chain embeddings and retrieval with language model calls for coding assistants and development tools.