Cost by workload size
Price comparison
| Feature |  GPT-4o | o3 |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input / M tokens | $2.50 | $2 |
| Output / M tokens | $10 | $8 |
| Cached input read | $1.25 | $1 |
| Context window | 128K | 200K |
| Batch discount | −50% | −50% |
| Vision | Yes | Yes |
| Audio | Yes | — |
| Reasoning tokens | — | Yes |
What your workload actually costs
Prices per token hide the real picture. Here are three common workloads computed with AITokenCalculator's own engine (medium reasoning effort, no batch, no cache):
| Workload | GPT-4o | o3 | Cheaper |
|---|---|---|---|
| 1,000 in / 300 out | $0.0055 | $0.0068 | GPT-4o |
| 10,000 in / 2,000 out | $0.045 | $0.052 | GPT-4o |
| 100,000 in / 20,000 out | $0.45 | $0.52 | GPT-4o |
| Monthly @ 1,000 req/day (standard) | $1,369.69 | $1,582.75 | GPT-4o |
Analysis
On input price, o3 wins at $2/M — roughly 1.3× cheaper than its rival. Output tells a sharper story: o3 charges $8/M , about 1.3× less than GPT-4o — and output is where chatty and agentic workloads bleed money.
Context capacity differs too: o3 fits 200K tokens (~150,000 words) versus 128K. If you feed whole documents or codebases, that gap decides feasibility before price even matters.
Generalist vs reasoner. o3 spends hidden reasoning tokens to solve math, science and planning problems GPT-4o fumbles — but every reasoning token is billed as output, so costs can balloon. Use o3 selectively behind a router; default to GPT-4o for conversation and summarization.
Numbers shift as providers reprice — check the full AI model pricing table, then run your own prompt through the calculator preloaded with GPT-4o or with o3.