LangChain vs LlamaIndex 2026: Response Time on 10 RAG Tasks

LlamaIndex Beats LangChain by 340ms on Average—But Not Where You'd Expect

Here's what surprised me: LlamaIndex 0.11 and LangChain 0.3 have nearly converged on simple retrieval. The real performance gaps show up in complex orchestration—multi-hop reasoning, hybrid search, and agentic workflows where one framework pulls ahead by 2-3x.

I ran both frameworks through 10 distinct RAG task types, each executed 50 times on identical hardware (M2 MacBook Pro, 32GB RAM, Python 3.12). The aggregate numbers tell one story, but the per-task breakdown reveals a much more nuanced picture. If you're building production RAG and optimizing for latency, the framework choice depends heavily on which RAG pattern you're implementing.

Photo by cottonbro studio on Pexels

Test Setup: Same Embeddings, Same LLM, Different Orchestration

Both frameworks used:

• OpenAI text-embedding-3-small for embeddings
• GPT-4o-mini for generation (to isolate orchestration overhead from LLM latency)
• Qdrant running locally via Docker
• 50,000 documents from the MS MARCO passage dataset

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