How to evaluate RAG pipelines
How to evaluate RAG pipelines. A short analysis built from the index, with the model-level data behind the claim.
What to compare in RAG pipelines
An evaluation is only useful if it answers a decision. For RAG pipelines, decide first whether you are choosing a model, validating a price or checking a quality claim.
A benchmark leaderboard is a starting point. It tells you which models are worth testing; it does not tell you which one fits your workflow.
How to score it
Use a small set of examples with clear pass/fail criteria. Run every candidate on the same set. Record quality, cost and latency for each run. GPT-5.2 Pro and GPT-5.1 Reasoning make a useful high/low pair for the first pass.
- Write the rubric before looking at outputs.
- Use blind review where possible.
- Include edge cases, not just happy paths.
- Re-run after any model or prompt change.
A simple evaluation plan
- Pick 20 representative examples.
- Define pass/fail for each.
- Run three candidates.
- Compare cost per pass.
- Choose the cheapest model that clears the bar.
Models relevant to RAG pipelines
| Model | Provider | Input / 1M | Context | Avg score |
|---|---|---|---|---|
| GPT-5.2 Pro | OpenAI | $21.32 | 2,000,000 | 89.6 |
| GPT-5.2 Max | OpenAI | $21.39 | 1,000,000 | 89.2 |
| Claude Opus 4.5 Pro | Anthropic | $20.98 | 200,000 | 89.1 |
| GPT-5.1 Reasoning | OpenAI | $20.27 | 400,000 | 88.3 |
| Claude Opus 4.5 Reasoning | Anthropic | $20.91 | 2,000,000 | 88.2 |
Related
FAQ
What is the main takeaway of this RAG pipelines analysis?
An evaluation is only useful if it answers a decision. For RAG pipelines, decide first whether you are choosing a model, validating a price or checking a quality claim.
Are the figures official?
No. The figures come from the AI & LLM Index demo dataset. Prices and scores are compiled for comparison; benchmark values are modelled estimates.
How should I use this in a buying decision?
Use it to build a shortlist, then test the shortlist on your own examples. Check the model pages for current price and context details.