Capability · Comparison

GPT-5 Nano vs GPT-5 Mini

Both sit under GPT-5 proper as cost-optimised tiers, but they target different workloads. Nano is the 'touch every row in the database' tier — classification, extraction, simple answers at massive scale. Mini is the general-purpose workhorse most GPT-5 pipelines actually run on.

Side-by-side

Criterion GPT-5 Mini GPT-5 Nano
Tier within GPT-5 family Mid Smallest
Latency on short prompts Fast Fastest
Reasoning capability Strong — close to GPT-4o class Deliberately limited, GPT-4o-mini class
Tool-call reliability Good Basic — plan tool use upstream
Context window 400,000 tokens 400,000 tokens
Pricing ($/M input) $2 $0.40
Pricing ($/M output) $8 $1.60
Best fit General chat, RAG, agents Classification, extraction, router models

Verdict

If you're building an actual product on GPT-5, Mini is almost always the right default — it's closer to flagship behaviour than to Nano, and its cost is still modest. Nano wins only when volume is huge and every prompt is trivially simple (routing, classification, tagging, sentiment). A common pattern is Nano as a first-stage router and Mini (or GPT-5 full) as the follow-on worker.

When to choose each

Choose GPT-5 Mini if…

  • You're building a chat assistant, RAG stack, or agent.
  • You need tool-use, JSON outputs, or multi-turn reasoning.
  • You want a clear quality upgrade path to GPT-5 flagship.
  • Prompt length is moderate and cost isn't the single constraint.

Choose GPT-5 Nano if…

  • You're doing classification, extraction, routing, or tagging at scale.
  • You can prompt away most reasoning requirements.
  • You want the cheapest GPT-5-family call possible.
  • You're stacking Nano as a cheap first pass in front of Mini / GPT-5.

Frequently asked questions

How small is GPT-5 Nano compared to GPT-5?

OpenAI doesn't publish parameters, but Nano behaves as a much smaller, faster, and cheaper model. Expect roughly GPT-4o-mini-class reasoning with GPT-5 API behaviour.

Can GPT-5 Nano do tool calling?

Yes, but with noticeably less reliability than Mini. For agent-style loops, prefer Mini; use Nano for single-shot classification plus routing.

How should students split workloads between them?

A typical pattern: Nano tags and routes incoming requests; Mini handles normal reasoning; GPT-5 flagship only handles escalated hard cases. That tier-down pattern keeps costs sane at demo scale.

Sources

  1. OpenAI — GPT-5 family pricing — accessed 2026-04-20
  2. OpenAI — Models — accessed 2026-04-20