Claude Opus 4.5’s biggest headline for developers is its coding performance. On the SWE-bench Verified benchmark, which measures real-world GitHub issue resolution, Opus 4.5 reaches around 80.9% task success, the highest publicly reported score to date. That means, in practice, it can reliably read issues, understand repositories, edit code, and fix bugs at a level that is competitive with strong human engineers for many day-to-day engineering tasks. Anthropic’s launch post also highlights strong results on other programming benchmarks (SWE-bench Multilingual, Aider Polyglot, etc.), indicating breadth across languages and problem types.
Beyond raw benchmark numbers, Opus 4.5 is tuned for long-horizon coding workflows. Anthropic’s internal case studies describe the model coordinating multi-agent refactors across dozens of files and commits, handling everything from planning and architecture notes to implementing changes and fixing tests. The effort parameter is especially useful here: for a quick small fix, you can run Opus at lower effort; for a complex refactor or hard bug, you can bump the effort level and let it reason more deeply, often ending up with fewer total iterations and better tests. Reports from early adopters mention fewer tool-calling failures, fewer build/lint errors, and shorter agent runs when swapping to Opus 4.5 from earlier models.
In a RAG or code-assistant stack, these strengths compound. You can index your codebase, design docs, and runbooks into a vector database like Milvus or Zilliz Cloud, then let Opus 4.5 use that retrieved context to ground its edits. A typical flow is: retrieve relevant files/snippets via Milvus, ask Opus to propose a patch or refactor plan, then have it generate concrete diffs. Because Opus 4.5 is more token-efficient, you can afford to include more context (e.g., tests, design docs) in each call, which improves correctness and reduces “out-of-context” mistakes. Combined with automated tests and CI, this setup can handle a surprising amount of engineering work with relatively little human supervision.