Migration assistant

Replacing Gemini 2.0 Flash?

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 2.0 Flash

Google Gemini · Gemini 2.0 · Active
Input: $0.1/M Output: $0.4/M Context: 1M
View detail →
#1 RECOMMENDED

Gemini 2.5 Flash-Lite

Google Gemini · Gemini 2.5
same provider; same context window
View full detail →
What changes if you switch
Field Gemini 2.0 Flash → Gemini 2.5 Flash-Lite Impact
Input price $0.1/M → $0.1/M Same price
Output price $0.4/M → $0.4/M Same price
Context window 1M tokens → 1M tokens Same capacity
Vision input ✓ supported → ✓ supported Preserved
Audio input ✓ supported → ✓ supported Preserved
Video input ✓ supported → ✓ supported Preserved
Function calling ✓ supported → ✓ supported Preserved
Structured output (JSON) ✓ supported → ✓ supported Preserved
Lifecycle Active → Active Same status
Other candidates
#2

Gemini 2.5 Flash

Google Gemini
same provider; 200% more expensive input; same context window
Details →
Input price: +200% more expensiveOutput price: +525% more expensive
#3

Gemini 2.5 Pro

Google Gemini
same provider; 1150% more expensive input; same context window
Details →
Input price: +1150% more expensiveOutput price: +2400% more expensive
#4

Gemini 3.1 Pro Preview

Google Gemini
same provider; 1900% more expensive input; same context window
Details →
Input price: +1900% more expensiveOutput price: +2900% more expensiveLifecycle: Preview / limited access
#5

Gemini 3.5 Flash

Google Gemini
same provider; 1400% more expensive input; same context window
Details →
Input price: +1400% more expensiveOutput price: +2150% more expensive
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.