Reasoning LLMs An Overview of New Deep Search Models

Reasoning LLMs are the latest evolution of large language models, moving far beyond text generation to include logical deduction, deep search, and problem solving. This overview examines five standout models—Grok 3 from xAI, ChatGpt from OpenAI, Gemini from Google, Copilot from Microsoft, and Perplexity AI—showing how each expands AI cognitive power and promises to transform the way we work, learn, and interact with information.

Overview of Each Model

Grok 3 (xAI): Known for advanced reasoning, it uses “Think” and “Big Brain” modes, trained with 10x more computing power than Grok 2, leveraging synthetic datasets and reinforcement learning.
ChatGpt (OpenAI): The o1 series excels in mathematics, science, and coding, spending more time thinking before responding, with a Deep Research feature for comprehensive reports.
Gemini (Google): A multimodal model, Gemini 2.0 includes Flash Thinking Experimental, showing thought processes for transparency, ideal for diverse data types.
Copilot (Microsoft): Integrates with Microsoft 365, using “Think Deeper” powered by OpenAI’s reasoning models for detailed, step-by-step answers.
Perplexity AI: A search engine with Deep Research, using reasoning models for in-depth reports, available on a freemium model for broader access.

Survey Note: Detailed Analysis of Reasoning-Enhanced LLMs

The integration of reasoning models and deep learning into large language models (LLMs) marks a pivotal advancement in artificial intelligence, enhancing their ability to solve complex problems and understand nuanced contexts. This survey note provides an in-depth examination of five leading LLMs—Grok 3 from xAI, ChatGpt from OpenAI, Gemini from Google, Copilot from Microsoft, and Perplexity AI—focusing on their incorporation of reasoning and deep learning techniques. Each model’s unique features and capabilities are analyzed, offering a comprehensive overview for researchers, developers, and enthusiasts.

Background and Context

LLMs, trained on vast datasets, have traditionally excelled at generating human-like text. However, recent innovations have integrated reasoning models, enabling these systems to perform logical deductions and solve problems, and deep learning, which underpins their ability to learn from data. This evolution is driven by the need for AI to handle more complex tasks, such as mathematics, science, and coding, beyond pattern-based text generation. The models discussed here, as of March 5, 2025, represent the forefront of this trend, each offering distinct approaches to reasoning and deep learning.

Detailed Analysis of Each Model

Grok 3 from xAI

Grok 3, launched by xAI, is positioned as a highly capable LLM with advanced reasoning features. It is trained with 10 times more computing power than its predecessor, Grok 2, utilizing the Colossus supercluster with over 200,000 GPUs. The training approach includes synthetic datasets, self-correction mechanisms, and reinforcement learning, which enhance its performance.

Reasoning Capabilities: Grok 3 incorporates reasoning through features like “Think” and “Big Brain” modes, accessible via the Grok app. These modes allow the model to engage in step-by-step reasoning, particularly suited for mathematics, science, and programming. An X post by xAI highlights its ability to puzzle through complex scenarios, such as a story-based logic problem, in 67 seconds, outperforming competitors like DeepSeek R1 (xAI). It also shows its reasoning process in DeepSearch, a feature that acts as a next-generation search engine, analyzing web and X data to deliver answers.
Deep Learning Foundation: The model’s training on synthetic datasets and large-scale reinforcement learning ensures it can correct errors and explore alternatives, delivering accurate answers. Benchmarks, such as AIME for mathematical reasoning and GPQA for STEM knowledge, show Grok 3 outperforming models like Gemini 2 Pro and GPT-4o, with an Elo score of 1402 in the Chatbot Arena (CNET, Forbes, TechCrunch).

ChatGpt from OpenAI

OpenAI’s ChatGpt, particularly the o1 series, represents a significant advancement in reasoning capabilities. Introduced in September 2024, the o1 series is designed to spend more time processing before responding, using self-training processes to learn new strategies and recognize mistakes.

Reasoning Capabilities: The o1 series excels in complex tasks, such as competitive programming, mathematics, and scientific reasoning. It scored 83% on an International Mathematics Olympiad qualifying exam, compared to 13% for GPT-4o, and performs similarly to PhD students on physics, biology, and chemistry benchmarks (Wikipedia, PYMNTS). The Deep Research feature, based on the o3 model, combines advanced reasoning with web search to generate comprehensive reports, available to Pro users at $200/month (OpenAI).
Deep Learning Foundation: As an LLM, ChatGpt relies on deep learning for its training, with the o1 series leveraging additional compute to enhance reasoning, making it a leader in handling intricate problem-solving tasks.

Gemini from Google

Google’s Gemini is a family of multimodal AI models, capable of processing text, images, video, and audio. The latest, Gemini 2.0, includes models like Pro and Nano, with Pro optimized for complex tasks and Flash Thinking Experimental for reasoning.

Reasoning Capabilities: Gemini’s multimodal nature allows it to reason across different data types, with the Flash Thinking Experimental model explicitly showing its thought process for improved performance and explainability. This model, built on Gemini 2.0 Flash, uses runtime reasoning techniques to break down problems into smaller tasks, enhancing outcomes (The Verge, Ars Technica). Deep Research, another feature, generates comprehensive reports by analyzing hundreds of sources, leveraging Google’s web search expertise and a 1M token context window (Google Blog).
Deep Learning Foundation: Trained natively on multimodal data and fine-tuned for effectiveness, Gemini uses deep learning to achieve state-of-the-art performance across domains, with its reasoning capabilities refined through advanced prompting techniques like Chain of Thought (Google DeepMind).

Copilot from Microsoft

Microsoft’s Copilot is an AI assistant integrated with Microsoft 365, leveraging LLMs like GPT-4 for various tasks. The “Think Deeper” feature, introduced in Copilot Labs, enhances its reasoning capabilities using OpenAI’s o1 model.

Reasoning Capabilities: The Think Deeper feature allows Copilot to handle complex problems, providing detailed, step-by-step answers, particularly useful for math, project planning, and deep topic exploration. It uses the o1 reasoning model, initially limited but expanded for free and Pro users, offering access comparable to ChatGpt Pro (TechRadar, Microsoft Copilot). This feature is part of Copilot’s broader integration, enhancing productivity in apps like Word and Excel.
Deep Learning Foundation: Copilot coordinates LLMs, including pretrained models like GPT-4, using deep learning techniques to understand, summarize, and generate content, with reasoning enhanced by additional compute for the o1 model (Microsoft Learn).

Perplexity AI

Perplexity AI is a conversational search engine using LLMs to answer queries with web-sourced, cited responses. Its Deep Research feature, available for free, leverages reasoning models for detailed reports.

Reasoning Capabilities: Deep Research conducts dozens of related searches, analyzes hundreds of resources, and uses reasoning models to logic out prompts step-by-step, compiling comprehensive reports in a white paper style. This feature, limited to five queries daily for free users, competes with paid offerings like ChatGpt’s Deep Research (Lifehacker). Perplexity also integrates models like DeepSeek R1 for enhanced reasoning, using a Mixture-of-Experts architecture for efficiency (Forbes).
Deep Learning Foundation: Powered by LLMs, including its standalone model based on GPT-3.5 and others like Claude 2 via Amazon Bedrock, Perplexity relies on deep learning for training, with Amazon SageMaker HyperPod reducing training time by up to 40% (Perplexity AI, AWS).

Comparative Insights

Comparisons reveal each model’s strengths: Grok 3 excels in logic and non-mathematical reasoning, taking 67 seconds for complex story problems, faster than DeepSeek R1 (Decrypt). ChatGpt’s o1 series leads in math and coding, with 83% on IMO qualifiers. Gemini’s multimodal reasoning suits diverse data, while Copilot’s Think Deeper integrates well with productivity tools. Perplexity’s free Deep Research offers accessibility, competing with paid features elsewhere (Tom’s Guide).

Unexpected Detail: Accessibility and Pricing

An unexpected aspect is Perplexity AI’s free Deep Research, contrasting with ChatGpt’s $200/month Pro tier and Grok 3’s beta access for Premium+ users at $30/month, highlighting varied accessibility models in the AI market (Lifehacker, Tom’s Guide).

Conclusion

These LLMs, as of March 5, 2025, showcase the integration of reasoning and deep learning, each with unique strengths. Grok 3’s powerful reasoning, ChatGpt’s problem-solving, Gemini’s multimodal capabilities, Copilot’s productivity integration, and Perplexity’s accessible search redefine AI’s potential, promising transformative impacts across industries.