o3 pricing for financial analysis

Quick start

Call o3 with the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="o3",
    max_tokens=4000,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0480 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 financial analysis benchmarks. Hover for details.

Cheapest viable option: Claude Sonnet 5 at $0.0560/call (-17% less, 90/100 quality).

What drives financial analysis cost

Financial analysis carries large structured inputs (statements, tables, time series) and produces detailed reasoning as output, so it is expensive on both sides. Tabular data is token-dense, and formatting choices in how you serialize a table can change input cost by a wide margin before you have changed anything about the model.

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 8,000/4,000 token split, output accounts for 67% of the $0.0480 per-call cost.

o3 quality benchmarks for financial analysis

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

Performance across all task types

o3 scores by task type. Financial Analysis 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