Choosing between Claude Opus 4.5 and other frontier models is mostly a question of workload, constraints, and ecosystem fit rather than a single benchmark score. First, look at task profile: if your workloads are heavy on long-context reasoning (e.g., big codebases, long technical documents, multi-step agents) or you care about strong performance on benchmarks like SWE-bench Verified and complex tool use, Opus 4.5 is a strong candidate. Evaluate it with a realistic set of tasks — not just synthetic prompts — and measure correctness, editing quality, and iterative refinement.
Second, evaluate integration and governance. If you’re already on an ecosystem such as Microsoft Foundry, Anthropic’s own platform, or an enterprise provider that offers managed Opus 4.5 endpoints, that can simplify auth, observability, and compliance. Also consider how well the model plays with your existing retrieval stack, job queues, and monitoring. For example, if you rely heavily on RAG with a vector database like Milvus or Zilliz Cloud, test how easily Opus 4.5 can consume your retrieved chunks (schema, length, style) and how robustly it grounds answers in those documents.
Finally, weigh cost, latency, and operational risk across candidates. Run side-by-side experiments with the same prompts, same retrieved context, same tool-calling environment, and measure not just raw accuracy but also things like: how often you need manual intervention, how predictable the model is under slightly modified prompts, and how easy it is to constrain. Often the “best” choice ends up being a portfolio: Opus 4.5 for complex agent and long-context tasks, plus lighter models for bulk, low-risk workloads. The key is to base your decision on structured experiments that reflect your real production flows rather than marketing claims.