Migration assistant

Replacing Grok Build 0.1?

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.

XA
Migrating from

Grok Build 0.1

xAI · Grok Build · Active
Input: $1/M Output: $2/M Context: 256K
View detail →
#1 RECOMMENDED

Grok 4.20

xAI · Grok 4
same provider; 25% more expensive input; 1M context (larger)
View full detail →
What changes if you switch
Field Grok Build 0.1 → Grok 4.20 Impact
Input price $1/M → $1.25/M +25% more expensive
Output price $2/M → $2.5/M +25% more expensive
Context window 256K tokens → 1M tokens +291% larger
Vision input ✓ supported → ✓ supported Preserved
Function calling ✓ supported → ✓ supported Preserved
Structured output (JSON) ✓ supported → ✓ supported Preserved
Prompt caching ✓ supported → ✓ supported Preserved
Lifecycle Active → Active Same status
Other candidates
#2

Grok 4.3

xAI
same provider; 25% more expensive input; 1M context (larger)
Details →
Input price: +25% more expensiveOutput price: +25% more expensiveContext window: +291% larger
#3

Gemini 2.5 Flash-Lite

Google Gemini
90% cheaper input; 1M context (larger)
Details →
Input price: Save 90%Output price: Save 80%Context window: +310% larger
#4

Gemini 3.1 Flash-Lite

Google Gemini
88% cheaper input; 1M context (larger)
Details →
Input price: Save 88%Output price: Save 62%Context window: +291% larger
#5

GPT-4.1 Mini

OpenAI
60% cheaper input; 1M context (larger)
Details →
Input price: Save 60%Output price: Save 20%Context window: +309% larger
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.