Gemini 3 vs Gemini 3 Pro vs Gemini 3 DeepThink: Understanding Google’s most advanced AI models

The introduction of Gemini 3 by Google can be considered one of the most successful AI rollouts that the company has had in recent months and which developers, researchers, and product teams that have tried its reasoning and multimodal features have received early praise. Officially announced on 18 November and sold as a new generation of intelligence, Gemini 3 is the next move in the goal of Google to create models not only powerful but more consistent, manageable, and based on real world applications.

The Gemini 3 family is composed of three quite different versions, Gemini 3, Gemini 3 Pro, and Gemini 3 DeepThink, adjusted to different levels of accuracy, reasoning, and complexity. To learners, information professionals, and engineers, the difference between these models is what enables them to select the appropriate tool to use in research processes, data applications, or other AI-related software. In this article, we deconstruct what each of the models does, how they compare, and what the benchmarks actually tell us.

Gemini 3: Its is the new baseline for multimodal reasoning

The base model in this new product lineup is Gemini 3 which is a foundational model of Google, yet its capabilities extend well beyond that. It consists of a multimodal thought with a combination of text understanding, spatial, vision, and multilingual abilities integrated into one system. Its one-million-token context window is one of its best properties, allowing the model to comprehend long and nuanced queries that involve multi-document as well as to reason over long sequences.

To developers, Gemini 3 supports complex instruction following, such as zero-shot software generation- Google states that it can create UI components or backend code even without explicit examples. The company focuses on this with its optimism about vibe-coding, the contentious workflow that lets users avoid the conventional coding and instead uses vibe-coding to almost exclusively create, prototype, and test software through an LLM.

On benchmarks, Gemini 3 achieved:

• WebDev Arena, which implies a great web-practical agency, is proposing 1487 Elo.

• 54.2% on Terminal-Bench 2.0 with an indication of better-than-anticipated tool-use performance.

• 76.2% on SWE-bench Verified, which is better at automated coding tasks than Gemini 2.5 Pro.

• First place on Vending-Bench 2, which is an assessment of long-horizon planning and decision-making.

These are the figures, which demonstrate that Gemini 3 is not only a text generator, it is also designed to behave, plan, and to interact with structured environments. Nonetheless, initial testers, like Andrej Karpathy, noted that there was sometimes brittle behaviour of the model, including the model not recognising it was the year 2025 because of a pre-training cutoff. At that time, however, he talked of the general ability as being impressive, with some moderate advancements in the richness of reasoning and resistance against mistakes.

Gemini 3 Pro: It is more accurate, more strategic, more capable

Gemini 3 Pro extends the ability of the fundamental model, but extends far further into the long-horizon thinking, mathematical rigor, and grounded factual basis. Google claims that the Pro variant is meant to perform a kind of a thought partner, which can translate scientific ideas into high-fidelity visualisations, explore abstract ideas, and create terse and correct answers.

On the benchmark front, Gemini 3 Pro performs significantly better than earlier models:

• Elo on LMArena 1501, one of the best results in history.

• 37.5 without tools on the Humanity Last Exam, which is a high score on knowledge-reasoning skills.

• 91.9 percent on GPQA Diamond, the most challenging graduate level physics and chemistry test at Google.

• MmmU-Pro 81%, and Video-MMMU 87.6%, which feature multimodal reasoning.

• SimpleQA Verified 72.1% which means more factual reliability.
  
  

Whereas Gemini 3 was oriented toward wide capability, Gemini 3 Pro is oriented towards making precise solutions to problems--in particular, in the fields of science, engineering, and quantitative research. The 23.4% score of the model on MathArena Apex indicates that it is making significant advances in mathematical reasoning, which is a traditionally weak area of most LLMs.

For data professionals, Gemini 3 Pro is better equipped for:

• Transforming datasets into narratives or visualisations

• Debugging or writing analytical scripts

• Understanding technical documentation

• Handling domain-specific reasoning in science and analytics

• Acting as a co-pilot for complex projects

Pro is also noticeably more concise and structured in its responses, making it better suited for research workflows.

Gemini 3 DeepThink: Google’s push toward advanced AGI-Level reasoning

The most experimental model in the series is Gemini 3 DeepThink. It facilitates a more deliberate mode of thinking that is assigned to tasks that demand slow thinking, multi-step systems of reasoning and cross-modal thinking. DeepThink is not only larger, but optimised to deliberate, just as OpenAI and Anthropic make their best models use a slow mode of reasoning.

Google’s tests show notable jumps in performance:

• 41% on Humanity's Last Exam without equipment.

• Optimal mark on GPQA Diamond 0.938 the best mark of Google of any model.

• 45.1% on ARC-AGI-2 (ARC Prize Verified), a challenge used to assess general intelligence and solving novel puzzles.

ARC-AGI-2 is also essential as it is calculated to discourage memorization and to encourage actual reasoning. The 45% mark is a significant improvement in tests that would measure abstract intelligence and not task performance.

DeepThink is not popular yet; Google claims that it is still in further safety, red-teaming, and robustness tests. It will then be restricted to subscriptions of Google AI Ultra as soon as it is released, meaning that Google considers this tier powerful enough to need stringent restrictions.

Which Gemini model should you use?

Gemini 3

Perfectly suited to be used by regular users, student researchers and developers requiring high multimodal performance, long context comprehension and agent-like behaviour. It is ideal in assisting with coding, summing documents and creativity.

Gemini 3 Pro

Optimal to data experts, engineers and technical inventors. Most stable with regard to factual accuracy, mathematical reasoning and domain knowledge. Good research, business analytics, and software development partner.

Gemini 3 DeepThink

Constructed by high-level researchers and AGI-oriented experimentation. Good in logic puzzles, multi-step reasoning, solving complicated scientific problems and high-stakes decision making. Not yet available in large amounts.

The road ahead

The Gemini 3 series points to the recent effort by Google to establish itself again in an increasingly competitive market, and in particular, the models such as Gemini 3 Pro allegedly caused OpenAI to experience economic headwinds, by being able to pass the leading performance standards. Although the use of benchmarks has been criticised as hackable or not representative of actual performance in the real world, initial user reports and developer feedback indicate real performance in response quality, reasoning stability, and cross-modal execution.

The difference between Gemini 3, Pro, and DeepThink will define the ways developers create AI tools, how organisations execute data processes, and how end users communicate with more capable AI assistants as the Gemini 3 ecosystem matures. To date, Gemini 3 Pro seems to be the most sensible combination of power and dependability- however DeepThink might be the future of slow, deliberate, AGI-driven thoughts.