Migration assistant

Replacing Gemini 3.1 Pro 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 Pro Preview

Google Gemini · Gemini 3.1 · Preview
Input: $2/M Output: $12/M Context: 1M
View detail →
#1 RECOMMENDED

Gemini 2.5 Flash

Google Gemini · Gemini 2.5
same provider; 85% cheaper input; same context window
View full detail →
What changes if you switch
Field Gemini 3.1 Pro Preview → Gemini 2.5 Flash Impact
Input price $2/M → $0.3/M Save 85%
Output price $12/M → $2.5/M Save 79%
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
Prompt caching ✓ supported → ✓ supported Preserved
Batch API ✓ supported → ✓ supported Preserved
Lifecycle Preview → Active Generally available
Other candidates
#2

Gemini 2.5 Flash-Lite

Google Gemini
same provider; 95% cheaper input; same context window
Details →
Input price: Save 95%Output price: Save 97%Lifecycle: Generally available
#3

Gemini 2.5 Pro

Google Gemini
same provider; 38% cheaper input; same context window
Details →
Input price: Save 38%Output price: Save 17%Lifecycle: Generally available
#4

Gemini 3.5 Flash

Google Gemini
same provider; 25% cheaper input; same context window
Details →
Input price: Save 25%Output price: Save 25%Lifecycle: Generally available
#5

Gemini 3.1 Flash-Lite

Google Gemini
same provider; same family; 88% cheaper input; loses 1 capability
Details →
Input price: Save 88%Output price: Save 88%Context window: 5% smallerBatch API: LOST — re-evaluate before switchingLifecycle: Generally available
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.