o3 Pro pricing for faq bot

Quick start

Call o3 Pro with the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="o3-pro",
    max_tokens=800,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0940 per call at standard rates
# Input:  $20.00/MTok
# Output: $80.00/MTok

How o3 Pro compares on cost

Per-call cost for all models scoring 50+ on faq bot benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.001260/call (99% less, 85/100 quality).

What drives faq bot cost

The most bounded question-answering shape: a short question against a fixed knowledge base, with a short answer. Because the knowledge base never changes between calls, cache reads apply to nearly the entire input, and the effective cost per answer falls well below the list rate. A fast-tier model is usually sufficient when the answers are already written.

For o3 Pro specifically, that means output tokens cost 4.0x more than input, at $80.00 vs $20.00 per MTok. At this use case’s 1,500/800 token split, output accounts for 68% of the $0.0940 per-call cost.

o3 Pro quality benchmarks for faq bot

Benchmark data for faq bot is pending for o3 Pro. Test against your own evaluation suite before moving to production.

Performance across all task types

o3 Pro scores by task type. FAQ Bot 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 o3 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 o3 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