Cost Calculator
Compare 14 AI models for rag pipeline. Prices shown at 10K calls/month with default token counts.
Question answering tasks pair a user query with retrieved context for answers. Token usage depends on the context window size and the response depth. Larger context windows allow more reference material but increase the per-call cost.
Retrieval-augmented calls are dominated by the retrieved passages, not the question. Retrieving eight chunks instead of three roughly triples input cost for an answer that is often no better, so retrieval tuning is cost tuning. The system prompt and instructions are identical on every call, which makes cache reads the first thing to enable.
Recommendations
Best quality
GPT 5.4
91/100 confidence
Best value
GPT 5.6 Luna
85/100 confidence · $16/mo
Monthly estimates assume 6,000 input / 1,500 output tokens per call. Use the detailed page for custom calculations.
Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
Retrieval-augmented calls are dominated by the retrieved passages, not the question. Retrieving eight chunks instead of three roughly triples input cost for an answer that is often no better, so retrieval tuning is cost tuning. The system prompt and instructions are identical on every call, which makes cache reads the first thing to enable. Across the 14 models we track, a typical call uses about 6,000 input and 1,500 output tokens.
GPT 5.6 Luna from GPT is the lowest-cost model we track for rag pipeline, at roughly $16.00 for 10,000 calls per month. It scores 85 out of 100 on this task type.
GPT 5.4 from GPT ranks highest for rag pipeline, scoring 91 out of 100. Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
These are estimates based on published rates. Track your real spend with the free dashboard.