Migration assistant

Replacing GPT-4.1?

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-4.1

OpenAI · GPT-4.1 · Active
Input: $2/M Output: $8/M Context: 1M
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🔔
Don't get caught off guard next time.
This is the scramble AI Stack Watch is built to prevent. Put GPT-4.1 in a monitored client workspace and we'll tell you — with the source — the moment it's deprecated, repriced, or out-shipped by a candidate, while there's still time to plan the move.
Monitor this in a client workspace →
#1 RECOMMENDED

GPT-4.1 Mini

OpenAI · GPT-4.1
same provider; same family; 80% cheaper input; same context window
View full detail →
What changes if you switch
Field GPT-4.1 → GPT-4.1 Mini Impact
Input price $2/M → $0.4/M Save 80%
Output price $8/M → $1.6/M Save 80%
Context window 1M tokens → 1M tokens Same capacity
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-4.1 Nano

OpenAI
same provider; same family; 95% cheaper input; same context window; loses 2 capabilities
Details →
Input price: Save 95%Output price: Save 95%Prompt caching: LOST — re-evaluate before switchingFine-tuning: LOST — re-evaluate before switching
#3

GPT-4o Mini

OpenAI
same provider; 92% cheaper input; smaller context (128K)
Details →
Input price: Save 92%Output price: Save 92%Context window: 88% smaller
#4

Gemini 2.5 Flash

Google Gemini
85% cheaper input; 1M context (larger); loses 1 capability
Details →
Input price: Save 85%Output price: Save 69%Context window: LargerFine-tuning: LOST — re-evaluate before switching
#5

Gemini 2.5 Flash-Lite

Google Gemini
95% cheaper input; 1M context (larger); loses 1 capability
Details →
Input price: Save 95%Output price: Save 95%Context window: 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.