Migration assistant

Replacing Gemini 3.1 Flash Image 🍌?

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 Image 🍌

Google Gemini · Active
Input: $0.5/M Output: $3/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 Image 🍌 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 2.5 Flash

Google Gemini · Gemini 2.5
same provider; 40% cheaper input
View full detail →
What changes if you switch
Field Gemini 3.1 Flash Image 🍌 → Gemini 2.5 Flash Impact
Input price $0.5/M → $0.3/M Save 40%
Output price $3/M → $2.5/M Save 17%
Context window — → 1M tokens One value not verified
Prompt caching ✓ supported → ✓ supported Preserved
Lifecycle Active → Active Same status
Other candidates
#2

Gemini 2.5 Flash-Lite

Google Gemini
same provider; 80% cheaper input
Details →
Input price: Save 80%Output price: Save 87%
#3

Gemini 3.1 Flash-Lite

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

Gemini 2.0 Flash

Google Gemini
same provider; 80% cheaper input; loses 1 capability
Details →
Input price: Save 80%Output price: Save 87%Prompt caching: LOST — re-evaluate before switching
#5

Gemini 3 Flash Preview

Google Gemini
same provider
Details →
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.