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Compare these two on your own workload

Published rates rank differently once prompt caching, batch discounts and your own input-to-output ratio are applied. Run both against your numbers.

o3 vs o4-mini

Reasoning tiers where billed thinking tokens drive the bill.

Prices verified 12 Oct 2026

OpenAI retires o3 on 11 Dec 2026; the named replacement is GPT-5.6 Sol. OpenAI retired o4-mini on 23 Oct 2026; the named replacement is GPT-5.6 Terra.

Input / 1M$2.00
Output / 1M$8.00
Cached input / 1M$0.500
Batch50% off
Context200K tokens
Blended (3:1)$3.50
Input / 1M$1.10
Output / 1M$4.40
Cached input / 1M$0.275
Batch50% off
Context200K tokens
Blended (3:1)$1.93

Monthly cost by workload shape

The same volume costs very different amounts depending on the shape of the traffic. No caching or batch applied.

Workloado3o4-miniDifference
Support chat
200/day · 1500 in / 300 out
$32.88$18.08o4-minisaves $14.79Model it →
RAG / search answers
20K/day · 3000 in / 400 out
$5,600.96$3,080.53o4-minisaves $2,520.43Model it →
Agent / tool-use loop
8.0K/day · 2500 in / 600 out
$2,386.50$1,312.57o4-minisaves $1,073.92Model it →
Batch summarization
100K/day · 5000 in / 500 out
$42.6K$23.4Ko4-minisaves $19.2KModel it →
Code assistant
15K/day · 2000 in / 800 out
$4,748.64$2,611.75o4-minisaves $2,136.89Model it →

Frequently asked questions

Which is cheaper, o3 or o4-mini?

At a typical 3:1 input-to-output ratio, o4-mini is cheaper: $1.93 blended per 1M tokens against $3.50. o4-mini is cheaper at every input and output mix.

What is the price difference between o3 and o4-mini?

o3: $2.00 input, $8.00 output per 1M tokens. o4-mini: $1.10 input, $4.40 output. That is 1.8x on input and 1.8x on output.

Does prompt caching change the answer?

o3 caches input at $0.500/1M and o4-mini caches at $0.275/1M. For input-heavy workloads with repeated context, caching can matter more than the base rate difference, model both in the calculator rather than comparing rate cards.

This page compares published prices only. It makes no claim about which model performs better on any task; that depends entirely on your evaluations.

Rate cards decide nothing on their own.

Most teams run several models at once. Finitizer Tokenomics shows what each one actually costs you, read from your own bills and exports, which workloads could test a smaller model, what they would cost on an open-weight model, and, for models you serve yourself, the cost per token beside the API price. A routing decision starts from your spend rather than a price page.

See Finitizer Tokenomics