Gemini 3 Deep Think: What Is It & How to Use It (2026)

Gemini 3 Deep Think: What Is It and How to Use It in 2026

Google's Gemini 3 introduced a powerful capability called Deep Think, a specialized reasoning mode designed for problems that require multi-step analysis, mathematical proofs, complex coding challenges, and scientific reasoning. Think of it as Gemini shifting into a slower, more deliberate thinking mode where it spends more time analyzing before responding.

This guide covers everything you need to know about Deep Think: what it is, when to use it, how to access it, and practical examples to get the best results.

What Is Deep Think?

Deep Think is an extended reasoning mode within Google's Gemini 3 model family. When activated, Gemini allocates significantly more compute to your request, breaking down complex problems into intermediate reasoning steps before producing a final answer.

Here is how it compares to standard Gemini responses:

Aspect	Standard Mode	Deep Think Mode
Response time	1-5 seconds	10-60+ seconds
Token usage	Standard	3-10x higher
Reasoning depth	Surface-level analysis	Multi-step chain of thought
Best for	General queries, creative writing	Math, logic, code, analysis
Cost (API)	Base pricing	Higher per-request cost
Thinking visibility	Hidden	Visible thinking steps

Deep Think is conceptually similar to OpenAI's o1/o3 reasoning models and Anthropic's Claude extended thinking, but with Google's own architecture and training approach.

How Deep Think Works Under the Hood

Without going into proprietary details, Deep Think uses a technique broadly known as chain-of-thought reasoning at inference time. Here is what that means in practice:

1. Problem decomposition: Gemini breaks the input into sub-problems
2. Sequential reasoning: Each sub-problem is solved step by step
3. Self-verification: The model checks its intermediate results
4. Synthesis: Final answer is assembled from verified steps

User Query: "Prove that the square root of 2 is irrational"

Standard Mode:
  -> Generates a proof sketch (may have gaps)
  -> ~2 seconds

Deep Think Mode:
  -> Step 1: State the assumption (assume √2 = p/q)
  -> Step 2: Derive p² = 2q²
  -> Step 3: Analyze parity of p
  -> Step 4: Derive contradiction about q
  -> Step 5: Verify each logical step
  -> Step 6: State conclusion with complete proof
  -> ~15 seconds

How to Access Deep Think

In Google AI Studio (Web Interface)

1. Navigate to aistudio.google.com
2. Select Gemini 3 from the model dropdown
3. Look for the "Deep Think" toggle in the settings panel (usually a brain or lightbulb icon)
4. Enable it before sending your prompt
5. Submit your query and wait for the extended response

In Gemini Chat (Consumer)

1. Open gemini.google.com
2. If you are on a Gemini Advanced subscription, look for the model selector
3. Choose the Deep Think variant from the model list
4. Type your question normally

Via the Gemini API

Using the Gemini API with the Google AI Python SDK:

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")

model = genai.GenerativeModel(
    model_name="gemini-3-deep-think",
)

response = model.generate_content(
    "Solve this step by step: Find all integer solutions to x³ + y³ = z³ where x, y, z > 0",
    generation_config=genai.GenerationConfig(
        temperature=0,
        max_output_tokens=8192,
    )
)

print(response.text)

Using the REST API directly with cURL:

curl -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-deep-think:generateContent?key=YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "parts": [{
        "text": "Analyze the time complexity of mergesort and prove it is O(n log n)"
      }]
    }],
    "generationConfig": {
      "temperature": 0,
      "maxOutputTokens": 8192
    }
  }'

Using the Vertex AI SDK (for Google Cloud users):

from vertexai.generative_models import GenerativeModel

model = GenerativeModel("gemini-3-deep-think")

response = model.generate_content(
    "Explain why P != NP is likely true based on current evidence",
    generation_config={
        "temperature": 0,
        "max_output_tokens": 8192,
    }
)

print(response.text)

When to Use Deep Think (and When Not To)

Deep Think excels at specific types of problems but is overkill for others. Here is a practical decision guide:

Use Deep Think for:

• Mathematical proofs and derivations
• Multi-step logic puzzles
• Complex code debugging (analyzing a 500-line function for subtle bugs)
• Scientific reasoning (chemistry reaction mechanisms, physics problems)
• Legal or contractual analysis requiring careful reading
• Data analysis with multiple dependent steps

Do NOT use Deep Think for:

• Simple factual questions ("What is the capital of France?")
• Creative writing (stories, poems, marketing copy)
• Quick translations
• Summarization tasks
• Casual conversation
• Tasks where speed matters more than depth

Practical Examples

Example 1: Debugging Complex Code

Prompt (Deep Think):
"Find all bugs in this Python function and explain why each is a bug:

def merge_intervals(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = [intervals[0]]
    for i in range(len(intervals)):
        if intervals[i][0] <= merged[-1][1]:
            merged[-1][1] = max(merged[-1][1], intervals[i][1])
        else:
            merged.append(intervals[i])
    return merged
"

Deep Think will identify:

• The loop starts at index 0 instead of 1 (comparing first element with itself)
• No empty input handling (will crash on intervals[0] if list is empty)
• Mutating the original intervals since lists are mutable (should use copies)

Example 2: Mathematical Problem Solving

Prompt (Deep Think):
"Prove that for any positive integer n, the sum 1² + 2² + 3² + ... + n² = n(n+1)(2n+1)/6.
Use mathematical induction and verify with a concrete example."

Deep Think produces a complete induction proof with base case, inductive hypothesis, inductive step, and verification.

Example 3: System Design Analysis

Prompt (Deep Think):
"I have a system that processes 10,000 API requests per second. Each request
needs to check a blocklist of 1 million entries, query a database, and return
a response within 50ms. What architecture would you recommend? Analyze the
tradeoffs of at least 3 approaches."

Deep Think Pricing (API)

Deep Think uses more tokens and compute than standard requests. Here is what to expect:

Usage Tier	Standard Gemini 3	Deep Think
Input tokens	Base rate	Base rate
Output tokens	Base rate	Base rate
Thinking tokens	N/A	Charged at output rate
Avg. request cost	~$0.001-0.01	~$0.01-0.10

The thinking tokens (the intermediate reasoning steps) are billed as output tokens even if they are not shown in the final response. This can increase costs substantially for complex queries.

Tips for Getting the Best Results

1. Be specific in your prompt. Deep Think benefits from clearly stated problems. Instead of "help me with math," say "Prove that every continuous function on [a,b] is Riemann integrable."

2. Ask for step-by-step reasoning explicitly. Even though Deep Think does this by default, prompting "show your work step by step" can improve the structure of the output.

3. Set temperature to 0 for reasoning tasks. You want deterministic, logical output, not creative variation.

4. Use it iteratively. If the first response misses something, follow up with "Check step 3 again" rather than re-prompting from scratch.

5. Combine with standard mode. Use standard Gemini for quick drafts, then switch to Deep Think to verify or refine critical sections.

Deep Think vs. Other Reasoning Models

Feature	Gemini 3 Deep Think	OpenAI o3	Claude Extended Thinking
Provider	Google	OpenAI	Anthropic
Visible thinking	Optional	Summary only	Visible (redacted partially)
Max thinking tokens	~32K	~100K	~128K
Multimodal reasoning	Yes (images, video)	Yes (images)	Yes (images)
API availability	Yes	Yes	Yes
Streaming support	Yes	Partial	Yes

Wrapping Up

Gemini 3 Deep Think is a significant capability for anyone working on problems that demand rigorous, multi-step reasoning. It is not a replacement for standard Gemini but rather a complementary mode for when you need the model to slow down and think carefully.

The key is knowing when to reach for it: mathematical proofs, complex debugging, scientific analysis, and multi-step logic problems are where Deep Think truly shines.

If you are working on AI-powered applications that involve image generation, video creation, or media processing, Hypereal AI offers a unified API platform where you can access dozens of AI models through a single integration, making it easy to combine LLM reasoning with creative media generation.