OpenAI o3 API Pricing: What Reasoning Models Actually Cost in Practice (2026)
OpenAI's o3 is listed at $2.00/1M input tokens and $8.00/1M output tokens — the same price as GPT-4.1. But reasoning models work differently: before generating the final response, they produce a chain of internal reasoning steps called thinking tokens. Those thinking tokens count as output and are billed at the full output rate.
A task where GPT-4.1 generates 800 output tokens might cause o3 to produce 3,000 total output tokens — 800 in the visible response plus 2,200 in reasoning. You pay for all of it. Real-world o3 costs are often 2–5× higher than GPT-4.1 on equivalent tasks depending on complexity. The sticker price is the same. The bill is not.
o3 vs o4-mini: which reasoning model to default to
OpenAI has two reasoning models: o3 at $2.00/$8.00 per 1M tokens and o4-mini at $1.10/$4.40 per 1M tokens. o4-mini is about 45% cheaper on both input and output. OpenAI positions o4-mini as the better default for the majority of reasoning tasks — it handles multi-step logic reliably, with o3 reserved for tasks that need its higher capability ceiling.
The practical rule: start with o4-mini, validate on your actual prompts, and escalate to o3 only where quality falls short. Defaulting to o3 when o4-mini would suffice is the most common unnecessary expense in reasoning model deployments.
When reasoning models earn their premium
Reasoning models are worth the cost on tasks with multiple sequential steps where intermediate decisions affect later ones. This includes complex mathematical or symbolic reasoning, multi-step code debugging and architectural analysis, planning tasks where the model needs to check its own work, scientific or legal document analysis requiring careful multi-step inference, and competitive programming. In these cases, the quality improvement over a standard model like GPT-4.1 is substantial.
Reasoning models typically do not earn their cost on single-step tasks. If you are using o3 to classify support tickets, summarise short emails, or generate formatted output from structured data, you are paying 3–5× more than necessary. Those workloads belong on GPT-4.1 mini or GPT-4o mini.
The hidden cost: thinking tokens at scale
With standard models, output token counts are predictable — the response is roughly what you asked for. With reasoning models, the thinking token count depends on how hard the model works on the problem. An easy prompt generates few thinking tokens. A hard proof generates many more. This makes cost budgeting difficult.
At scale, discovering that a subset of your prompts are triggering 5,000+ thinking tokens can explain a bill that seems much larger than expected. If you want to see which features and customers are generating the most o3 spend, PerUnit breaks down AI cost by feature, customer, and pricing tier. Or model what o3 vs GPT-4.1 would cost at your request volume with our free AI cost calculator.