Migration assistant

Replacing o4-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

o4-mini

OpenAI · o-series · Active
Input: $1.1/M Output: $4.4/M Context: 200K
View detail →
#1 RECOMMENDED

GPT-4.1 Mini

OpenAI · GPT-4.1
same provider; 64% cheaper input; 1M context (larger)
View full detail →
What changes if you switch
Field o4-mini → GPT-4.1 Mini Impact
Input price $1.1/M → $0.4/M Save 64%
Output price $4.4/M → $1.6/M Save 64%
Context window 200K tokens → 1M tokens +424% 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 → ✓ supported Preserved
Lifecycle Active → Active Same status
Other candidates
#2

GPT-5 Mini

OpenAI
same provider; 77% cheaper input; 400K context (larger); loses 1 capability
Details →
Input price: Save 77%Output price: Save 55%Context window: +100% largerFine-tuning: LOST — re-evaluate before switching
#3

GPT-5 Nano

OpenAI
same provider; 95% cheaper input; 400K context (larger); loses 1 capability
Details →
Input price: Save 95%Output price: Save 91%Context window: +100% largerFine-tuning: LOST — re-evaluate before switching
#4

GPT-5.4 Nano

OpenAI
same provider; 82% cheaper input; 400K context (larger); loses 1 capability
Details →
Input price: Save 82%Output price: Save 72%Context window: +100% largerFine-tuning: LOST — re-evaluate before switching
#5

GPT-5.4 Mini

OpenAI
same provider; 32% cheaper input; 400K context (larger); loses 1 capability
Details →
Input price: Save 32%Output price: +2% more expensiveContext window: +100% 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.