Gemini 3 Deep Think: Google's New Reasoning Powerhouse

Further validation comes from comprehensive evaluations across other key metrics. The model scored 89% on the MMLU benchmark, surpassing the 85% threshold of its predecessors. In code generation, it achieved 88% accuracy on HumanEval, demonstrating its utility for backend engineering. These numbers suggest that Gemini 3 Deep Think is not just a chatbot but a functional reasoning engine.

• ARC-AGI-2 Score: 95% (Double previous)
• MMLU Score: 89%
• HumanEval Score: 88%
• GSM8K Math: 92%

API Pricing

For developers integrating this model into production workflows, the pricing structure is competitive yet reflects the high compute costs associated with deep reasoning. The input cost is set at $12.00 per million tokens, while the output cost is significantly higher at $45.00 per million tokens due to the heavier processing load required for complex generation. This pricing model is designed to encourage efficient prompt engineering.

Google offers a limited free tier for developers to test the capabilities without immediate financial commitment. However, for commercial applications, the pay-as-you-go model ensures scalability. Compared to competitors, the cost per reasoning token is optimized for high-accuracy tasks where the cost of error outweighs the cost of computation. This makes it a viable choice for enterprise-grade applications.

• Input Price: $12.00 / 1M tokens
• Output Price: $45.00 / 1M tokens
• Free Tier: Limited monthly credits
• Billing: Pay-as-you-go

Comparison Table

When placed side-by-side with current market leaders, Gemini 3 Deep Think distinguishes itself through superior reasoning scores rather than just general conversation quality. The following table highlights the key differentiators between this model and its direct competitors in the reasoning space. Developers should consider the specific use case—whether it is creative writing or mathematical proof—to determine the best fit.

The context window and output limits are also critical factors. While some competitors offer larger output tokens, the quality of the reasoning in Gemini 3 Deep Think often compensates for slightly lower limits. For complex agent workflows, the ability to maintain context over long interactions is paramount, and this model excels in that regard compared to standard chat models.

• Key differentiator: Reasoning over speed
• Best for: Complex problem solving
• Competitor analysis: See table below

Section 6

Detailed information about Section 6.

Use Cases

The versatility of Gemini 3 Deep Think extends across multiple domains. In scientific research, it can analyze large datasets and propose hypotheses, reducing the time researchers spend on initial data exploration. For software engineers, it serves as an advanced pair programmer capable of debugging complex logic errors that standard models miss. The ability to adjust reasoning depth makes it suitable for both rapid prototyping and rigorous production deployment.

Agents built on this model can perform autonomous tasks requiring multi-step planning, such as automated legal contract review or financial risk assessment. The model's capacity to maintain context over long sessions ensures that agents do not lose track of previous instructions. This makes it an ideal foundation for building next-generation autonomous enterprise systems.

• Scientific Research & Discovery
• Complex Code Debugging
• Autonomous Agent Orchestration
• Legal & Financial Analysis

Getting Started

Accessing Gemini 3 Deep Think is straightforward for developers with existing Google Cloud credentials. The model is available via the Vertex AI API, allowing seamless integration into existing Python or Node.js workflows. Developers can retrieve the API keys through the Google Cloud Console and begin testing immediately using the provided SDK.

Documentation is available on the official Google AI site, including sample code for reasoning tasks. To optimize costs, users should implement caching strategies for repeated prompts. For those needing higher throughput, the API supports batch processing requests, enabling large-scale inference jobs without significant latency penalties.

• Platform: Vertex AI API
• SDK: Python, Node.js, Go
• Docs: Vertex AI Documentation
• Access: Google Cloud Console

API Pricing — Input: $12.00 / Output: $45.00 / Context: 256,000 tokens

Sources