Migration assistant

Replacing gemini-3.1-flash-lite-preview?

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.

GG
Migrating from

gemini-3.1-flash-lite-preview

Google Gemini · Active
Input: $0.25/M Output: $1.5/M Context: —
View detail →
🔔
Don't get caught off guard next time.
This is the scramble AI Stack Watch is built to prevent. Put gemini-3.1-flash-lite-preview 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

Gemini 2.0 Flash

Google Gemini · Gemini 2.0
same provider; 60% cheaper input
View full detail →
What changes if you switch
Field gemini-3.1-flash-lite-preview → Gemini 2.0 Flash Impact
Input price $0.25/M → $0.1/M Save 60%
Output price $1.5/M → $0.4/M Save 73%
Context window — → 1M tokens One value not verified
Lifecycle Active → Active Same status
Other candidates
#2

Gemini 2.5 Flash-Lite

Google Gemini
same provider; 60% cheaper input
Details →
Input price: Save 60%Output price: Save 73%
#3

Gemini 3.1 Flash-Lite

Google Gemini
same provider; 50% cheaper input
Details →
Input price: Save 50%Output price: Save 50%
#4

Gemma 4 26B A4B IT

Google Gemini
same provider; 76% cheaper input
Details →
Input price: Save 76%Output price: Save 78%
#5

Gemma 4 31B IT

Google Gemini
same provider; 52% cheaper input
Details →
Input price: Save 52%Output price: Save 75%
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.