Migration assistant

Replacing GPT-4o?

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-4o

OpenAI · GPT-4o · Active
Input: $2.5/M Output: $10/M Context: 128K
View detail →
#1 RECOMMENDED

GPT-4o Mini

OpenAI · GPT-4o
same provider; same family; 94% cheaper input; same context window
View full detail →
What changes if you switch
Field GPT-4o → GPT-4o Mini Impact
Input price $2.5/M → $0.15/M Save 94%
Output price $10/M → $0.6/M Save 94%
Context window 128K tokens → 128K tokens Same capacity
Vision input ✓ supported → ✓ supported Preserved
Function calling ✓ supported → ✓ supported Preserved
Structured output (JSON) ✓ supported → ✓ supported Preserved
Prompt caching ✓ supported → ✓ supported Preserved
Fine-tuning ✓ supported → ✓ supported Preserved
Lifecycle Active → Active Same status
Other candidates
#2

GPT-4.1 Mini

OpenAI
same provider; 84% cheaper input; 1M context (larger)
Details →
Input price: Save 84%Output price: Save 84%Context window: +718% larger
#3

GPT-5 Mini

OpenAI
same provider; 90% cheaper input; 400K context (larger); loses 1 capability
Details →
Input price: Save 90%Output price: Save 80%Context window: +212% largerFine-tuning: LOST — re-evaluate before switching
#4

GPT-5 Nano

OpenAI
same provider; 98% cheaper input; 400K context (larger); loses 1 capability
Details →
Input price: Save 98%Output price: Save 96%Context window: +212% largerFine-tuning: LOST — re-evaluate before switching
#5

GPT-5.4 Mini

OpenAI
same provider; 70% cheaper input; 400K context (larger); loses 1 capability
Details →
Input price: Save 70%Output price: Save 55%Context window: +212% largerFine-tuning: LOST — re-evaluate before switching
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.