Google Gemini 3 Deep Think Achieves High AI Benchmark Scores

Google has unveiled Gemini 3 Deep Think, a new deep reasoning model designed to enhance AI's problem-solving capabilities. This model supports parallel thinking, allowing it to explore multiple hypotheses concurrently rather than relying on a linear, step-by-step reasoning process. According to information reviewed by toolmesh.ai, Gemini 3 Deep Think has achieved notable results in several high-difficulty evaluations, positioning it as a significant development in AI research.

Key Performance Metrics

Gemini 3 Deep Think demonstrated strong performance across various benchmarks:

ARC-AGI-2 Test: This benchmark, often referred to as the "holy grail" for Artificial General Intelligence (AGI) evaluation, saw Gemini 3 Deep Think achieve an accuracy of 45.1%. This figure is 2.5 times higher than the 17.6% recorded by GPT-5.1.
Humanity's Last Exam: In this test, which assesses AI's reasoning and knowledge integration without external tools, the model achieved 41.0% accuracy.
GPQA Diamond Evaluation: For high-precision scientific knowledge Q&A, Gemini 3 Deep Think delivered near-perfect performance.

Understanding Parallel Thinking

Traditional large language models typically employ a sequential reasoning approach, where a chain of thought unfolds step by step. This linear method, often described as single-threaded thinking, means that an incorrect assumption early in the process can compromise the entire conclusion, making it difficult for the model to re-evaluate or explore alternative paths.

Gemini 3 Deep Think's parallel thinking capability represents a departure from this approach. It enables the model to consider multiple potential scenarios or hypotheses simultaneously. By deriving results for each hypothesis and then comparing them, the model can select the most logical and fitting answer path. This mirrors human-like reasoning, where individuals often contemplate various "what-if" scenarios when solving complex problems.

For instance, when presented with a complex mathematical or logical problem, Gemini 3 Deep Think does not commit to a single line of thought. Instead, it concurrently evaluates hypotheses A, B, and C, derives separate outcomes for each, and then determines which hypothesis best aligns with the problem's conditions to reach the most reasonable conclusion.

Detailed Evaluation Results

The model's performance was rigorously tested across several domains:

1. Humanity's Last Exam This evaluation assesses an AI's ability to reason and integrate knowledge without the aid of external tools.

Gemini 3 Deep Think: 41%
Gemini 3 Pro: 37.5%
Gemini 2.5 Pro: 21.6%
Claude Sonnet 4.5: 13.7%
GPT-5 Pro: 30.7%
GPT-5.1: 26.5%

Gemini 3 Deep Think secured the top position, demonstrating strong logical and knowledge application capabilities without external assistance, significantly outperforming the GPT-5 and Claude series.

2. GPQA Diamond This benchmark focuses on the accuracy of scientific knowledge and reasoning, also without external tools.

Gemini 3 Deep Think: 93.8%
Gemini 3 Pro: 91.9%
Gemini 2.5 Pro: 86.4%
Claude Sonnet 4.5: 83.4%
GPT-5 Pro: 88.4%
GPT-5.1: 88.1%

Gemini 3 Deep Think achieved near-perfect scores, indicating its proficiency in specialized scientific domains and reasoning, surpassing all tested GPT series models.

3. ARC-AGI-2 Considered a highly challenging "visual logical reasoning puzzle" evaluation, some models in this test were allowed to use code tools.

Gemini 3 Deep Think (code tools enabled): 45.1%
Gemini 3 Pro: 31.1%
Gemini 2.5 Pro: 4.9%
Claude Sonnet 4.5: 13.6%
GPT-5 Pro: 15.8%
GPT-5.1: 17.6%

Gemini 3 Deep Think significantly outperformed GPT-5.1, achieving more than 2.5 times its score. This suggests a substantial advancement in its capacity for complex reasoning, particularly in novel, abstract graphical logic problems.

Case Study: Procedural Planet Rendering

A comparative demonstration video highlighted the capabilities of Gemini 3 Deep Think in handling complex programming and creative tasks. The task involved creating a procedurally rendered Earth-like planet within a single HTML file, with specific requirements for visual elements (storms, rings, asteroid belt, starry sky, city lights) and high-fidelity topography, alongside a directive for creative output.

The video showcased a side-by-side comparison:

Gemini 3 Pro (Standard Version): Generated a simple glowing white sphere with a basic ring. This output indicated a basic understanding of keywords like "planet" and "ring" but lacked detail, texture, and complex procedural generation, failing to meet the "high fidelity" and "creativity" requirements.
Gemini 3 Deep Think (Deep Thinking Version): Produced a highly detailed 3D Earth model, complete with blue oceans, green landmasses, cloud cover, and topographical textures. The planet was surrounded by complex multi-layered rings and dynamically rotating particles, simulating an asteroid belt. This result suggested that the "Deep Think" mode engaged in extensive planning and understood that "procedural rendering" necessitates mathematical algorithms for generating natural textures, rather than simple image pasting. The model likely generated complex WebGL code or integrated 3D library logic within the single HTML file.

This demonstration illustrates the difference between reasoning models and predictive models. Gemini 3 Deep Think's ability to plan complex code architectures and understand abstract computer graphics concepts like "procedural generation" allowed it to produce a sophisticated outcome. This indicates expert-level capabilities in complex STEM and creative programming tasks.

Gemini Ultra subscribers can access the Gemini 3 Deep Think mode by selecting "Deep Think" in the prompt bar and choosing Gemini 3 Pro in the model options.