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AI / Google Antigravity Gemini Fundamentals Interview Questions

How does thinking/reasoning work in Gemini models and what is thinking_level?

Gemini 3 series models use dynamic thinking by default - they automatically decide how much internal reasoning to apply before responding, calibrated to the task complexity. Developers can influence this via the thinking_level parameter.

from google import genai
from google.genai import types

client = genai.Client()

# Dynamic thinking is ON by default for Gemini 3 models
# Use thinking_level to control depth:
response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents="Design a thread-safe cache with O(1) operations.",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(
            thinking_level="high",  # "none" | "low" | "medium" | "high"
        )
    )
)
# Thinking steps are visible as execution steps in the Interactions API
interaction = client.interactions.create(
    model="gemini-3.1-pro-preview",
    input="Find all race conditions in this code: ...",
)
for step in interaction.steps:
    if step.type == "thought":
        print(f"Thinking: {step.signature[:50]}...")  # encrypted thought
    elif step.type == "model_output":
        print(f"Output: {step.content[0].text}")

thinking_level values
ValueBehaviourUse case
noneNo internal reasoning; direct responseSimple factual queries, fast responses
lowMinimal reasoningBasic analysis tasks
mediumBalanced reasoning (typical default)Most general tasks
highDeep reasoning; slower but more accurateComplex coding, math, architecture design

Legacy note: thinking_budget is still supported for backward compatibility but Google recommends migrating to thinking_level for more predictable performance. Do not use both parameters in the same request.

What does dynamic thinking in Gemini 3 models mean?
What is the key difference between thinking_budget and thinking_level in the Gemini API?

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