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Gemini Reasoning

Gemini's reasoning-focused models — the "Thinking" variants of the Gemini family — are designed to work through multi-step problems internally, showing their chain of thought before producing a final answer. They rank #6 among the most-used AI model lines, at roughly 25.6% usage share, and compete directly with OpenAI's o-series on math, logic, and coding benchmarks. Made by Google DeepMind, they combine Gemini's native multimodal strengths with deeper deliberation for harder problems.

Quick facts
Made by: Google DeepMind
Access: Proprietary — free tier in Google AI Studio + paid usage-based API (Gemini API / Vertex AI)
Context window: ~1 million tokens on current Thinking-class models (varies by exact version — check the model docs)
Strengths: Multi-step math and logic, competitive coding/debugging, transparent "thinking" traces, strong multimodal reasoning
Primary use case: Complex reasoning tasks — advanced math, scientific analysis, and multi-step coding problems, often combined with large documents or images
Jump to: ExampleGetting startedBest for

Example

Thinking-variant Gemini models are called through the same google-genai Python SDK, with an optional thinking config to control how much internal reasoning budget the model uses.

from google import genai
from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemini-2.5-pro",
    contents="A train leaves at 2pm going 60mph, another leaves 30 minutes later going 75mph on the same route. When does the second train catch up?",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_budget=2048)
    )
)

print(response.text)

Getting started

Get a free API key from Google AI Studio, install the SDK, then call a Thinking-capable Gemini model.

# 1. Get a free API key at aistudio.google.com/apikey

# 2. install the SDK
pip install google-genai

# 3. set your key and run a Python script targeting a "thinking" model
export GEMINI_API_KEY="your-key-here"
Best for: Hard multi-step problems where you also need Gemini's native multimodal reach — reasoning over a mix of text, images, and long documents together, such as analyzing a scientific paper's figures alongside its math, or debugging a large codebase with attached logs.