Exploring Reasoning Models in AI Marketplace - Feb’2025Permalink

What Makes a Model a “Reasoning Model”?Permalink

The term “reasoning model” is not strictly defined but generally refers to models that explicitly demonstrate structured problem-solving abilities, such as:

• Logical inference (deductive/inductive reasoning)
• Multi-step problem-solving (e.g., math, coding, puzzles)
• Common-sense reasoning (understanding implicit context)
• Causal reasoning (connecting causes and effects).

Modern models achieve this through architectural innovations (e.g., chain-of-thought prompting, sparse attention) and training on datasets enriched with reasoning tasks (e.g., math problems, logic puzzles).

Can we say earlier models like Phi-2, Mistral, and Llama 2 are Called “Reasoning Models”Permalink

These models are explicitly optimized for reasoning tasks through:

• Training Data: Curated datasets with math, code, and logic problems.
• Architecture: Techniques like sparse attention (Mistral) or small-but-efficient designs (Phi-2).
• Fine-Tuning: Instruction-tuning to follow complex, multi-step prompts.

Examples of Their Reasoning Capabilities

• Phi-2 (Microsoft): Trained on synthetic textbooks and logic puzzles, excelling at common-sense reasoning (e.g., “If Alice has 3 apples and gives 2 to Bob, how many does she have?”).

• Mistral 7B: Uses grouped-query attention for efficient long-context reasoning, outperforming larger models on logic puzzles and code generation.

• Llama 2 (Meta): Fine-tuned on coding and math datasets (e.g., CodeLlama variant), enabling multi-step problem-solving in domains like arithmetic or chemistry.

Was GPT-1 the “Reasoning Model”?Permalink

GPT-1 (2018) was groundbreaking as an early transformer-based language model, but it lacked explicit reasoning capabilities:

• Limitations: Struggled with multi-step logic (e.g., arithmetic beyond simple addition). They have limited common-sense understanding (e.g., “John put a glass on the edge of a table. It fell. Why?”).
• Role in Evolution: GPT-1’s transformer architecture became the foundation for later reasoning-focused models, but it wasn’t optimized for structured reasoning tasks.

The term “reasoning model” gained traction with models like GPT-3 (2020), which showed emergent reasoning abilities, and GPT-4/Claude 3, which integrated explicit reasoning frameworks (e.g., tree-of-thought prompting).

Why there is a Confusion in the market and people think GPT o1 is the first reasoning model?Permalink

• Ambiguity in Terminology: “Reasoning” is a spectrum. Even early models like GPT-1 could answer simple questions, but modern models handle structured, multi-step reasoning.
• Evolution of Capabilities: As models scaled, reasoning emerged as a byproduct of training on diverse data (e.g., GPT-3), but newer models are deliberately engineered for reasoning.
• Recent models are using Chain of Thought, Tree of Thoughts to plan and execute reasoning processes, they are taking time to think and do the work and showing the progress on the screen. This is giving impression that earlier models were not reasoning.

Language Models vs. Reasoning ModelsPermalink

Language models are AI systems trained to predict the next token (word, subword, or character) in a sequence. They learn statistical patterns from vast text corpora. Model like GPT-1, BERT, Llama 2, Falcon-180B can do several language task like translation, text generation, sentiment analysis, summarization, generation, NER and question-answering. But they lack the capability to solve complex, multi-step problems like arithmetic or logic.

Reasoning models are language models explicitly optimized to perform structured problem-solving, logical inference, or causal analysis. They bridge the gap between pattern recognition and human-like deduction. Techniques like Sparse attention (Mistral), chain-of-thought prompting (GPT-4) helps in reasoning.

The next step of these two development is Agentic systems. Agentic systems are AI frameworks that autonomously plan, act, and iteratively improve using language or reasoning models as a core component. They integrate tools (APIs, calculators, temperature, or thousands of other tools can be written and integrated by Agentic systems), memory (ability to remember previous conversations), and feedback loops (ability to improve from previous feedbacks).

Proprietary and Opensource Modern Reasoning ModelsPermalink

Modern reasoning models have significantly advanced AI’s ability to perform logical and structured problem-solving.

Proprietary Reasoning Models:Permalink

1. Claude 3.7 Sonnet by Anthropic: Introduced in February 2025, Claude 3.7 Sonnet is a hybrid reasoning model that combines instinctive outputs with in-depth reasoning. It features an “extended thinking mode” for complex problem-solving, enhancing performance in tasks like coding and legal queries. (Anthropic Launches the World’s First ‘Hybrid Reasoning’ AI Model | WIRED).

2. Grok-3 by xAI: Released in February 2025, Grok-3 is trained with significantly more computing power than its predecessor, Grok-2. It offers advanced reasoning capabilities, including a “Think” mode for structured problem-solving and a “Big Brain” mode for complex tasks.

3. DeepSeek-R1 by DeepSeek: Launched in January 2025, DeepSeek-R1 is an open-source reasoning model utilizing reinforcement learning. It competes with leading AI models, offering advanced reasoning capabilities at a lower operational cost.

4. Microsoft: Microsoft’s Phi-4, with 14 billion parameters, is designed for complex reasoning tasks, particularly in STEM and mathematics, outperforming larger models on certain benchmarks. It’s available on Azure AI Foundry under the Microsoft Research License Agreement and will launch on Hugging Face, focusing on accuracy through enhanced training (Microsoft introduces Phi-4, an AI model for advanced reasoning tasks – Computerworld). Available on Azure AI Foundry and Hugging Face (Microsoft’s Phi-4 announcement).

5. Google: Google’s Gemini 2.0 Flash Thinking Experimental is an enhanced reasoning model capable of showing its thoughts, improving performance and explainability. It’s designed for multimodal tasks like programming, math, and physics, and is accessible through Google AI Studio and Vertex AI, with a one-million token context window for deeper analysis (Google’s Gemini 2.0 Flash Thinking Experimental). It’s experimental, with some inconsistencies noted in simple tasks, but shows promise in complex problem-solving.

6. OpenAI: OpenAI has introduced o1, o1-mini, and o3-mini, focusing on advanced reasoning for tasks like math, science, and coding. These models use reinforcement learning (RL) techniques and are available through the OpenAI API and ChatGPT, with o3-mini recently made free for basic users (OpenAI’s o1 announcement). They are noted for their high performance but come with higher costs, with o1-mini being 20x more expensive per token than GPT-4o mini, reflecting the computational intensity of reasoning.

Open-Source Reasoning Models:Permalink

1. DeepSeek-R1 by DeepSeek: Beyond its proprietary applications, DeepSeek-R1 is also available as an open-source model under the MIT license, promoting widespread use and adaptation. DeepSeek-R1 is open-source, available on Hugging Face, and has gained attention for its efficiency and performance, reshaping the AI landscape by challenging proprietary models with lower computational requirements (DeepSeek’s reasoning AI shows power of small models, efficiently trained | IBM). Available DeepSeek-R1 on Hugging Face

2. Llama-3.2V-11B-cot by Xkev: This open-source model is trained on reasoning stages and utilizes beam search during inference, enhancing its logical reasoning capabilities.

3. DeepThought-8B-Llama-v0.01-alpha by Ruliad: An open-source model based on Llama, it outputs its reasoning process in structured JSON, facilitating transparency and further research.

4. QwQ-32B-Preview by Qwen: A preview of Qwen’s reasoning model, released before R1, it’s utilized by researchers for experiments in logical reasoning.

5. Sky-T1-32B-Preview by NovaSky-AI: Fine-tuned from Qwen2.5 32B, this model is trained on outputs of QwQ-32B, enhancing its reasoning capabilities through structured training.

6. S1: Developed by researchers at Stanford and the University of Washington, S1 is a recent open-source reasoning model trained on less than $50 in compute credits, achieving performance comparable to OpenAI’s o1 on benchmarks like AIME2024 and LiveCodeBench-Medium. It’s trained on datasets distilled from Google’s Gemini Thinking Experimental, suggesting potential availability on platforms like Hugging Face, though specific access details may require further research (This Free ‘Reasoning’ AI Model Beats OpenAI’s o1—Without a $20 Monthly Fee - Decrypt).

Market Availability and TrendsPermalink

These models are accessible through various channels: proprietary models via APIs (e.g., OpenAI, Anthropic, Google Cloud), cloud platforms (e.g., Azure AI Foundry, Alibaba Cloud), and open-source repositories (e.g., Hugging Face). The market has seen a shift toward inference compute, with reasoning models requiring more processing time but offering enhanced reliability, as seen with DeepSeek-R1’s cost efficiency and S1’s low training cost. However, controversy exists around pricing, with OpenAI’s models being significantly more expensive, and performance debates, especially for open-source models competing with proprietary ones.

The AI marketplace, encompassing platforms like Hugging Face, Azure AI Foundry, Google Cloud, and company APIs, has seen a surge in these models, reflecting a trend toward inference compute, where additional processing time enhances reliability and sensibility of outputs. This trend coincides with the growing demand for AI in enterprise applications, where accuracy and explainability are critical.