Migration assistant

Replacing GPT-4o Mini?

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 Mini

OpenAI · GPT-4o · Active
Input: $0.15/M Output: $0.6/M Context: 128K
View detail →
#1 RECOMMENDED

GPT-5 Nano

OpenAI · GPT-5
same provider; 67% cheaper input; 400K context (larger); loses 1 capability
View full detail →
What changes if you switch
Field GPT-4o Mini → GPT-5 Nano Impact
Input price $0.15/M → $0.05/M Save 67%
Output price $0.6/M → $0.4/M Save 33%
Context window 128K tokens → 400K tokens +212% larger
Vision input ✓ supported → ✓ supported Preserved
Function calling ✓ supported → ✓ supported Preserved
Structured output (JSON) ✓ supported → ✓ supported Preserved
Prompt caching ✓ supported → ✓ supported Preserved
Fine-tuning ✓ supported → ✗ not supported LOST — re-evaluate before switching
Lifecycle Active → Active Same status
Other candidates
#2

GPT-4.1 Mini

OpenAI
same provider; 167% more expensive input; 1M context (larger)
Details →
Input price: +167% more expensiveOutput price: +167% more expensiveContext window: +718% larger
#3

GPT-4.1 Nano

OpenAI
same provider; 33% cheaper input; 1M context (larger); loses 2 capabilities
Details →
Input price: Save 33%Output price: Save 33%Context window: +718% largerPrompt caching: LOST — re-evaluate before switchingFine-tuning: LOST — re-evaluate before switching
#4

GPT-5.4 Nano

OpenAI
same provider; 33% more expensive input; 400K context (larger); loses 1 capability
Details →
Input price: +33% more expensiveOutput price: +108% more expensiveContext window: +212% largerFine-tuning: LOST — re-evaluate before switching
#5

Gemini 2.5 Flash-Lite

Google Gemini
33% cheaper input; 1M context (larger); loses 1 capability
Details →
Input price: Save 33%Output price: Save 33%Context window: +719% 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.