Claude Opus 4.7 vs GPT-5.5 for 100M-Token RAG Pipelines

Scaling RAG Architecture for Enterprise Research

For healthcare administrators managing literature reviews and clinical documentation, the choice between Claude Opus 4.7 and GPT-5.5 often comes down to the balance between reasoning depth and retrieval efficiency. As you scale to 100 million tokens monthly for automated paper drafting and evidence synthesis, selecting the right model is critical for maintaining medical accuracy and reducing audit risks.

Claude Opus 4.7 is frequently favored for high-stakes research workflows. Its architecture excels at multi-step reasoning and maintaining long-form coherence, which is essential when synthesizing complex clinical data across dozens of patient studies or regulatory documents. If your pipeline relies on nuanced, context-heavy analysis where hallucination mitigation is the primary KPI, Claude Opus 4.7 provides a robust foundation for structured output.

GPT-5.5, conversely, is an architectural powerhouse for high-throughput RAG systems. In scenarios where you are indexing massive corpora of medical literature, GPT-5.5 demonstrates exceptional speed and latency optimization, making it suitable for real-time document processing and internal search interfaces. Its ability to navigate dense, technical terminology at scale allows for faster iteration on large-batch research tasks. However, administrators should ensure their RAG implementation includes rigorous verification layers—such as citation checking and source-grounding—as even the most performant models require structural guardrails in clinical settings. Choose Claude for reasoning-intensive literature reviews, and GPT-5.5 for high-velocity document processing pipelines where latency is a primary driver.