o3 vs o3-mini: A Practical Routing Guide

o3-mini costs $1.10 per million input tokens and $4.40 per million output tokens. o3 costs $2.00 per million input tokens and $8.00 per million output tokens. o3 is roughly 1.8x more expensive on input and output alike, which is the normal relationship you'd expect between a flagship reasoning model and its smaller sibling.

The practical question isn't which one is cheaper, o3-mini is, it's which tasks actually need o3's extra reasoning depth versus which ones o3-mini already handles fine at a lower cost.

Reasoning Tokens Change the Real Cost More Than the Listed Price Does

Both models use internal chain-of-thought reasoning that doesn't appear in the visible output but is billed as output tokens. A complex math or coding problem can generate 2,000 to 5,000 reasoning tokens internally before producing the final visible response, all charged at the output rate.

For a task that generates 3,000 reasoning tokens plus 500 visible output tokens: on o3-mini, that's (3,000 + 500) / 1,000,000 × $4.40 = $0.0154. On o3, it's (3,000 + 500) / 1,000,000 × $8.00 = $0.028. The gap in dollar terms grows with task difficulty, because harder tasks generate more reasoning tokens on both models, but the per-token rate gap stays constant at roughly 1.8x.

This means the real question isn't "which is cheaper per token" (o3-mini always is), it's "does this task need enough extra reasoning quality from o3 to justify paying 1.8x for it."

A Practical Routing Framework

For hard math problems, competitive programming, formal verification, and multi-step logical deduction where being wrong is expensive, route to o3. The quality gap over o3-mini is real on these task types, and paying 1.8x more is cheap insurance against a wrong answer.

For moderate coding assistance, single-step reasoning, and tasks where the answer can be quickly verified, o3-mini is usually the better default. It's also worth comparing against non-reasoning models for tasks that don't need chain-of-thought at all: GPT-5.4 mini ($0.75 input / $4.50 output) is often competitive on quality for tasks that don't genuinely require reasoning, and cheaper than either o3 model.

For simple classification or extraction tasks that ended up on a reasoning model out of habit rather than necessity, re-evaluate whether you need a reasoning model at all. GPT-4o-mini ($0.15/$0.60) or Claude Haiku 4.5 ($1.00/$5.00) will often do the job without paying for reasoning tokens you don't need.

Keeping Routing Logic Current

The core mistake teams make isn't picking the wrong model once, it's hardcoding a cost assumption into routing logic and never revisiting it. Provider pricing changes often enough that a routing table built six months ago can be sending traffic to the wrong tier today, in either direction. The OpenAI API cost calculator and pricing guide is a useful reference, but treat any static pricing reference, including this one, as something that needs periodic re-verification, not a permanent fact.

This is also a good example of why defaulting to one model forever without revisiting that default as prices and model lineups change is a costly habit. The cross-provider LLM routing guide covers the tooling needed to keep routing logic synchronized with current pricing automatically, rather than relying on a number someone hardcoded once.

PromptUnit maintains a live pricing table across providers and routes each request to the cheapest model that still clears your quality bar, so this kind of drift between "what you assumed" and "what's actually true today" doesn't silently cost you money. Start with PromptUnit to see exactly what your current reasoning-task routing is costing you.