DataLLM LabGPT-5 mini vs GPT-5 nano: Which Cheap Tier to Use
GPT-5 mini and GPT-5 nano are the two budget tiers of the GPT-5 family, and they're both shockingly cheap — but they're not interchangeable. Nano is roughly 5x cheaper than mini and built for the simplest, highest-volume tasks; mini steps up for slightly harder work where nano starts to slip. This guide compares them on price, modeled cost, and the kind of task each handles, so you put the right one on each job.
The short answer
Nano for the simplest, highest-volume tasks; mini when nano isn't quite enough. Nano is ~5x cheaper and excellent at classification, extraction, and routing; mini steps up for harder reasoning, longer instructions, and light coding. Both are a rounding error next to a flagship — the question is which clears your quality bar.
Side by side
| GPT-5 nano | GPT-5 mini | |
|---|---|---|
| Price (in/out) | $0.05 / $0.40 | $0.25 / $2 |
| Relative cost | ~5x cheaper | Baseline |
| Speed | Fastest | Fast |
| Best tasks | Classification, extraction, routing | Light reasoning, longer instructions, light code |
| Struggles on | Multi-step reasoning, nuance | Hard reasoning, complex code |
What they cost to run
Both are tiny next to the flagship; nano is the cheaper of the two:
| Monthly workload | GPT-5 mini | GPT-5 nano | GPT-5 | GPT-5.4 |
|---|---|---|---|---|
| Support chatbot | $34.0 | $6.80 | $170 | $280 |
| RAG / knowledge base | $90.0 | $18.0 | $450 | $800 |
| Coding agent | $70.0 | $14.0 | $350 | $575 |
| Batch extraction | $53.5 | $10.7 | $268 | $495 |
| Content generation | $85.0 | $17.0 | $425 | $650 |
When nano is enough
- Classification & tagging — sentiment, intent, category labels.
- Extraction — pulling fields from text, simple structured output.
- Routing — deciding which downstream model or path a request takes.
- Short generation — titles, summaries, simple replies.
On these, nano's quality usually holds and its price is unbeatable — it's the right default for high-volume, well-scoped work.
When to step up to mini
- Multi-step reasoning — tasks needing a chain of small inferences.
- Longer / nuanced instructions — where nano drifts off-task.
- Light coding — small functions, edits, and explanations.
- Customer-facing text — where small quality gaps are visible.
Route between them
The best pattern isn't picking one — it's routing. Send the simplest tasks to nano, escalate to mini (or a flagship) only when a validation check fails or the task is clearly harder. Through a gateway you apply this per request, capturing nano's price on the easy majority while keeping quality where it counts:
client.chat.completions.create(model="openai/gpt-5-nano", messages=msgs) # default: simplest tasks
# escalate to mini (or a flagship) only when a check fails or difficulty risesRoute nano, mini, and flagships from one key
GPT-5 nano, mini, and the full GPT-5 family plus 300+ more — one OpenAI-compatible endpoint, escalate per request.
FAQ
What is the difference between GPT-5 mini and nano?
Nano ($0.05/$0.40) is cheapest/fastest for the simplest tasks; mini ($0.25/$2) is ~5x pricier but handles harder reasoning, longer instructions, and light coding.
Is GPT-5 nano good enough?
For well-scoped simple tasks — classification, extraction, routing, short generation — yes. It struggles on multi-step reasoning and complex code. Test on your task.
How much cheaper is nano than mini?
~5x. A chatbot is ~$7/mo on nano vs $34 on mini — both negligible next to a flagship's $280-560.
When should I use mini over nano?
When nano's quality slips — multi-step reasoning, nuanced instructions, light coding, customer-facing text. Still far cheaper than the flagship.
Can I route between nano and mini?
Yes — default to nano, escalate to mini (or a flagship) on failure or difficulty. A gateway does this per request.
How do I call them?
Native OpenAI format — set the model id to gpt-5-nano or gpt-5-mini, or call via a gateway. Switching is a one-line change.
Is nano good for coding?
Only light code (small snippets). For real coding, step up to mini or a coding-tuned model; nano makes mistakes on complex code.
Which is best for high-volume classification?
Nano — its quality holds on classification/tagging and its price is unbeatable at scale.