Migration assistant

Replacing GPT-5 Nano?

Ranked replacement candidates, each shown with exactly what changes if you switch. Same-provider matches surface first because switching cost is lower; capability regressions are flagged in red.

OP
Migrating from

GPT-5 Nano

OpenAI · GPT-5 · Active
Input: $0.05/M Output: $0.4/M Context: 400K
View detail →
#1 RECOMMENDED

GPT-5 Mini

OpenAI · GPT-5
same provider; same family; 400% more expensive input; same context window
View full detail →
What changes if you switch
Field GPT-5 Nano → GPT-5 Mini Impact
Input price $0.05/M → $0.25/M +400% more expensive
Output price $0.4/M → $2/M +400% more expensive
Context window 400K tokens → 400K tokens Same capacity
Vision input ✓ supported → ✓ supported Preserved
Function calling ✓ supported → ✓ supported Preserved
Structured output (JSON) ✓ supported → ✓ supported Preserved
Prompt caching ✓ supported → ✓ supported Preserved
Lifecycle Active → Active Same status
Other candidates
#2

GPT-5 Pro

OpenAI
same provider; same family; 29900% more expensive input; same context window; loses 1 capability
Details →
Input price: +29900% more expensiveOutput price: +29900% more expensivePrompt caching: LOST — re-evaluate before switching
#3

GPT-4.1 Mini

OpenAI
same provider; 700% more expensive input; 1M context (larger)
Details →
Input price: +700% more expensiveOutput price: +300% more expensiveContext window: +162% larger
#4

GPT-5.4 Mini

OpenAI
same provider; 1400% more expensive input; same context window
Details →
Input price: +1400% more expensiveOutput price: +1025% more expensive
#5

GPT-5.4 Nano

OpenAI
same provider; 300% more expensive input; same context window
Details →
Input price: +300% more expensiveOutput price: +212% more expensive
Methodology

How candidates are ranked

Candidates are ranked by how much of the source model's profile each one preserves — weighing switching cost (same provider or model family surface first), capability parity (what you keep versus what you'd lose), context window, and pricing direction. A candidate that is itself deprecated is pushed down the list; one that's already retired is never recommended.

Ranking is purely algorithmic — no editorial weighting, no paid placement. Every value is pulled from each provider's own documentation; click any model name to see the source-linked detail.