GPT-5 mini token cost & pricing
GPT-5 mini is the tier most production chat products should default to. It keeps the 400K context window and the reasoning framework of the flagship at $0.25 per million input tokens and $2 per million output — a fifth of GPT-5 on input and a fifth on output, which makes the blended per-request cost roughly 80% lower.
GPT-5 mini specifications
| Vendor | OpenAI |
|---|---|
| Input price | $0.25 / 1M tokens |
| Output price | $2 / 1M tokens |
| Cached input price | $0.025 / 1M tokens (90% cheaper) |
| Context window | 400,000 tokens |
| Max output | 128,000 tokens |
| Open weights | No |
| Price rank | 5 of 17 (cheapest first) |
| Best for | budget, high-volume |
What GPT-5 mini costs in practice
Four common workloads, priced at list rates. Monthly figures assume 30 days.
| Workload | Per request | Requests/day | Per month |
|---|---|---|---|
| Chatbot · 1K in / 500 out | $0.0013 | 10,000 | $375.00 |
| RAG Q&A · 8K in / 400 out | $0.0028 | 5,000 | $420.00 |
| Doc summary · 30K in / 1K out | $0.0095 | 1,000 | $285.00 |
| Code agent · 60K in / 3K out | $0.02 | 500 | $315.00 |
How many requests does $100 buy?
Assuming a 1,000-token prompt and a 500-token response per request.
How GPT-5 mini pricing actually works
The pricing structure mirrors GPT-5 exactly, just scaled down: 8x from input to output, cached input at $0.025 per million. Because scale-down is uniform, the routing decision between GPT-5 and GPT-5 mini is purely about quality on your specific task, not about where the money goes. Test on your real data, not on benchmarks.
When to use GPT-5 mini
Use it for customer support chat, summarization, structured extraction, moderation assistance and most RAG question answering. It is the right starting point and often the right ending point — many teams only discover they need the flagship after a measurable quality gap on a specific task class.
How to cut your GPT-5 mini bill
The mini tier already removes most of the savings pressure, so the remaining wins are architectural: cache the static part of your prompt, keep conversation history truncated to what the model actually needs, and batch offline jobs rather than calling per-item.
Other OpenAI models
Cheaper alternatives to GPT-5 mini
- Llama 4 MaverickMeta50% cheaper
- GPT-4o miniOpenAI67% cheaper
- DeepSeek V3.2 (chat)DeepSeek69% cheaper
- GPT-5 nanoOpenAI80% cheaper
Run the numbers on your own prompt
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