Migration assistant

Replacing o3?

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

o3

OpenAI · o-series · Active
Input: $0.06/M Output: $0.06/M Context: 200K
View detail →
#1 RECOMMENDED

GPT-5 Nano

OpenAI · GPT-5
same provider; 17% cheaper input; 400K context (larger)
View full detail →
What changes if you switch
Field o3 → GPT-5 Nano Impact
Input price $0.06/M → $0.05/M Save 17%
Output price $0.06/M → $0.4/M +567% more expensive
Context window 200K tokens → 400K tokens +100% larger
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-4.1 Mini

OpenAI
same provider; 567% more expensive input; 1M context (larger)
Details →
Input price: +567% more expensiveOutput price: +2567% more expensiveContext window: +424% larger
#3

GPT-5 Mini

OpenAI
same provider; 317% more expensive input; 400K context (larger)
Details →
Input price: +317% more expensiveOutput price: +3233% more expensiveContext window: +100% larger
#4

GPT-5.4 Mini

OpenAI
same provider; 1150% more expensive input; 400K context (larger)
Details →
Input price: +1150% more expensiveOutput price: +7400% more expensiveContext window: +100% larger
#5

GPT-5.4 Nano

OpenAI
same provider; 233% more expensive input; 400K context (larger)
Details →
Input price: +233% more expensiveOutput price: +1983% more expensiveContext window: +100% larger
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.