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AI / Google Antigravity Gemini Fundamentals Interview Questions

What are model version types in the Gemini API and which should you use in production?

Gemini model IDs follow a structured naming convention that indicates the model's version type. Choosing the right version type affects stability, rate limits, and billing behaviour.

Gemini model version types
TypeExampleBehaviourProduction use?
Stablegemini-2.5-flash-001Pinned; does not change; most predictableRecommended for production
Previewgemini-3.1-pro-previewMay change; billing enabled; deprecated with >= 2 weeks noticeAcceptable for production with risk
Latest alias-latest suffixHot-swapped to newest release automaticallyAvoid in production - can break without notice
Experimental...-exp suffixMay change; often restricted rate limitsDevelopment/testing only
# GOOD: use a pinned stable model in production
response = client.models.generate_content(
    model="gemini-2.5-flash-001",   # stable, versioned, won't change
    contents="...",
)

# ACCEPTABLE: preview with awareness of deprecation risk
response = client.models.generate_content(
    model="gemini-3.1-pro-preview",  # preview: >= 2 weeks deprecation notice
    contents="...",
)

# AVOID in production: auto-updated aliases
response = client.models.generate_content(
    model="gemini-flash-latest",  # will silently upgrade to new model releases!
    contents="...",
)

# WARNING: Gemini 3 Pro Preview was deprecated March 9, 2026
# with minimal migration window - a real-world example of preview risk.
# Always use versioned IDs for customer-facing applications.

Why should you avoid using -latest alias model IDs in production applications?
What is the minimum deprecation notice period for Gemini preview models?

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