Migration assistant

Replacing GPT-5.5?

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-5.5

OpenAI · GPT-5.5 · Active
Input: $5/M Output: $30/M Context: 1.1M
View detail →
#1 RECOMMENDED

GPT-4.1 Mini

OpenAI · GPT-4.1
same provider; 92% cheaper input
View full detail →
What changes if you switch
Field GPT-5.5 GPT-4.1 Mini Impact
Input price $5/M $0.4/M Save 92%
Output price $30/M $1.6/M Save 95%
Context window 1.1M tokens 1M tokens Smaller
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-5.5 Pro

OpenAI
same provider; same family; 500% more expensive input; same context window; loses 1 capability
Details →
Input price: +500% more expensiveOutput price: +500% more expensivePrompt caching: LOST — re-evaluate before switching
#3

GPT-4.1 Nano

OpenAI
same provider; 98% cheaper input; loses 1 capability
Details →
Input price: Save 98%Output price: Save 99%Context window: SmallerPrompt caching: LOST — re-evaluate before switching
#4

GPT-4o Mini

OpenAI
same provider; 97% cheaper input; smaller context (128K)
Details →
Input price: Save 97%Output price: Save 98%Context window: 88% smaller
#5

GPT-5 Mini

OpenAI
same provider; 95% cheaper input; smaller context (400K)
Details →
Input price: Save 95%Output price: Save 93%Context window: 62% smaller
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.