OpenAI: GPT-5 Image
GPT-5 Image is an image generation model from OpenAI released on Oct 14, 2025. It reads text, images and files and writes text and images, with a 400K token context window.
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It costs $10 per million input tokens and $10 per million output tokens. Cached input bills at $1.25, 88% less than fresh input.
It supports prompt caching.
- Modalities
- In / out price
- $10 / $10 per 1M
- Context
- 400K
- Max output
- 128K
- Released
- Oct 14, 2025
- Knowledge cutoff
- Oct 2024
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
GPT-5 Image pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $10 / 1M tokens | |
|---|---|---|
| Output | $10 / 1M tokens | |
| Cached input (read) | $1.25 / 1M tokens | 88% off input |
What GPT-5 Image costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call GPT-5 Image through an OpenAI compatible API.
- Reasoning
reasoning - Temperature
temperature
Alternatives to GPT-5 Image
Models that take the same inputs, closest in price, cheaper ones first.
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by openaiOct 16, 2025$2.50/M input tokens$2/M output tokens
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GPT-5 Image questions
How much does the GPT-5 Image API cost?
$10 per million input tokens and $10 per million output tokens, with cached input at $1.25. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of GPT-5 Image?
400,000 tokens of input, and it can write up to 128,000 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Is GPT-5 Image open source?
No. OpenAI serves it through an API only, so you cannot download the weights or host it yourself.
When was GPT-5 Image released?
Oct 14, 2025, with training data up to Oct 2024.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on GPT-5 Image?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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