OpenAI model pricing detail
This page answers the GPT-5 mini pricing query directly. It keeps the live standard and batch rows in one view, then shows where the cheap row is a real answer and where context or tool fit still pushes the decision upward.
Price rows
GPT-5 mini looks simple on the pricing page, but the real question is whether the low row survives context, tool, and hosted-tool pressure.
| Mode | Context | Input | Cached input | Output | Decision read |
|---|---|---|---|---|---|
| Standard | Standard | $0.25 per 1M tokens | $0.03 per 1M tokens | $2.00 per 1M tokens | This is the baseline live GPT-5 mini row for direct API usage before hosted tools are added. |
| Batch | Standard | $0.13 per 1M tokens | $0.01 per 1M tokens | $1.00 per 1M tokens | Batch is the cheapest published GPT-5 mini path for large-volume extraction or classification work. |
Fit signals
GPT-5 mini becomes the right answer when the workload stays inside the published context ceiling and does not depend on the broader flagship tool set.
The current OpenAI limits summary lists GPT-5 mini at 400,000 tokens. That is still large, but it is materially smaller than the flagship path and should be treated as a real fit constraint.
Sources
OpenAI currently lists GPT-5 mini with Functions, web search, file search, MCP. If the workflow needs a broader tool surface, the low token row can still be the wrong operating choice.
Sources
When the workload is high-volume and repeatable, the batch row can matter more than the standard row. That makes GPT-5 mini a serious extraction or classification baseline even before more aggressive cheap rows are considered.
Sources
Workload snapshots
These snapshots keep the arithmetic simple so the mini row can be judged without opening the full model matrix first.
When this row is misleading
These are the places where GPT-5 mini usually gets over-credited in a budget conversation.
File search, web search, and container runtime can stay expensive even when the base model row gets much cheaper. A model swap is not the same thing as a full workflow optimization.
Sources
If the extraction or reasoning path depends on longer prompts, larger retrieval payloads, or richer context carry-forward, GPT-5 mini should not be priced as if it were a drop-in replacement.
Sources
If the workflow needs built-in image generation, code interpreter, or skills on the same model path, the cheap GPT-5 mini row no longer answers the whole decision by itself.
Sources
Official sources
This page keeps the source set narrow so a pricing row can stay auditable instead of drifting into summary-only advice.
Source of record for GPT-5 mini standard and batch token pricing.
Source of record for GPT-5 mini context window, max output tokens, and current built-in tool support.
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