o3 pricing for invoice parsing

Quick start

Call o3 with the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="o3",
    max_tokens=600,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.008800 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 invoice parsing benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.001120/call (87% less, 89/100 quality).

What drives invoice parsing cost

Invoices are short, highly repetitive documents processed in bulk: the ideal batch API workload. Per-document cost is low; total cost is set by volume and by whether you are paying full rate for a job that could have run asynchronously overnight at a discount. The extraction schema is constant, so it should never be paying full input rate either.

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 2,000/600 token split, output accounts for 55% of the $0.008800 per-call cost.

o3 quality benchmarks for invoice parsing

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

Performance across all task types

o3 scores by task type. Invoice Parsing 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