Migration assistant

Replacing gpt-3.5-turbo-instruct?

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-3.5-turbo-instruct

OpenAI · Active
Input: $1.5/M Output: $2/M Context: 4K
View detail →
#1 RECOMMENDED

GPT-4.1 Mini

OpenAI · GPT-4.1
same provider; 73% cheaper input; 1M context (larger)
View full detail →
What changes if you switch
Field gpt-3.5-turbo-instruct → GPT-4.1 Mini Impact
Input price $1.5/M → $0.4/M Save 73%
Output price $2/M → $1.6/M Save 20%
Context window 4K tokens → 1M tokens +25476% larger
Lifecycle Active → Active Same status
Other candidates
#2

GPT-4.1 Nano

OpenAI
same provider; 93% cheaper input; 1M context (larger)
Details →
Input price: Save 93%Output price: Save 80%Context window: +25476% larger
#3

GPT-4o Mini

OpenAI
same provider; 90% cheaper input; 128K context (larger)
Details →
Input price: Save 90%Output price: Save 70%Context window: +3025% larger
#4

GPT-5 Nano

OpenAI
same provider; 97% cheaper input; 400K context (larger)
Details →
Input price: Save 97%Output price: Save 80%Context window: +9666% larger
#5

GPT-5.4 Nano

OpenAI
same provider; 87% cheaper input; 400K context (larger)
Details →
Input price: Save 87%Output price: Save 38%Context window: +9666% 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.