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Retrieval-Augmented Generation (RAG) pipelines benefit from large context windows. Models like Gemini 1.5 Pro (2M tokens) and Claude 3.5 Sonnet (200k tokens) can ingest entire document corpora in a single pass, reducing chunk overlap and retrieval errors. For most RAG workloads, a 128k+ context window is sufficient.
Agentic workloads require strong tool-use, function-calling, and instruction-following. GPT-4o and Claude 3.5 Sonnet consistently top agentic benchmarks like SWE-Bench and WebArena. For cost-efficient agents, GPT-4o mini and Claude 3.5 Haiku offer strong performance at a fraction of the price.
Open-weight models (Llama 3, Mistral, Gemma) offer zero data-retention and full customisation via fine-tuning — ideal for regulated industries. Proprietary APIs (GPT-4o, Claude) trade control for frontier performance and zero infrastructure overhead. The choice depends on latency, cost, and compliance requirements.