o3 pricing for data extraction

Quick start

Call o3 with the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="o3",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0160 per call at standard rates
# Input:  $2.00/MTok
# Output: $8.00/MTok

How o3 compares on cost

Per-call cost for all models scoring 50+ on data extraction benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.002000/call (88% less, 89/100 quality).

What drives data extraction cost

Extraction is input-heavy and output-light (the raw document goes in, structured fields come out), which makes it one of the cheapest tasks per unit of value on this list. Output is small enough that output pricing is nearly irrelevant. Cost tracks document size, so the lever is sending only the pages that contain the fields you want.

For o3 specifically, that means output tokens cost 4.0x more than input, at $8.00 vs $2.00 per MTok. At this use case’s 4,000/1,000 token split, output accounts for 50% of the $0.0160 per-call cost.

o3 quality benchmarks for data extraction

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

Performance across all task types

o3 scores by task type. Data Extraction 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

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 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