o1 Pro pricing for rag pipeline

Quick start

Call o1 Pro with the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="o1-pro",
    max_tokens=1500,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$1.8 per call at standard rates
# Input:  $150.00/MTok
# Output: $600.00/MTok

How o1 Pro compares on cost

Per-call cost for all models scoring 50+ on rag pipeline benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.003000/call (100% less, 85/100 quality).

What drives rag pipeline 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.

For o1 Pro specifically, that means output tokens cost 4.0x more than input, at $600.00 vs $150.00 per MTok. At this use case’s 6,000/1,500 token split, output accounts for 50% of the $1.8 per-call cost.

o1 Pro quality benchmarks for rag pipeline

Benchmark data for rag pipeline is pending for o1 Pro. Test against your own evaluation suite before moving to production.

Performance across all task types

o1 Pro scores by task type. RAG Pipeline highlighted.

Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 10 weeks old, so treat the scores as directional and validate against your own evals.

About o1 Pro

Backfilled on 2026-09-23. Prices confirmed by the provider pricing page and the LiteLLM cross-check. Release date not published in a machine-readable source. Not yet benchmarked, so never recommended.

Other models from GPT

You can compare o1 Pro head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions