Top 5 LLMs with Reasoning Capabilities in 2025

(This reply was generated by Gemini Advanced Pro 1.5 from a prompt by Andreas Ramos. A reasoning AI is not like chat AI. The prompts have to be structured to work for the reasoning tool. It also helps to use XML. -- andreas)

Large Language Models (LLMs) are transforming the way we interact with technology. While early LLMs excelled at tasks like generating text and translating languages, the latest models are pushing the boundaries of Artificial Intelligence (AI) with advanced reasoning capabilities. These models can tackle complex problems, understand nuanced instructions, and even exhibit human-level performance in specific domains.

In this article, we explore the top five LLMs known for their reasoning prowess, examining their unique features and potential applications. These LLMs were selected based on their performance on reasoning benchmarks, expert opinions, and overall capabilities in handling complex tasks and exhibiting advanced problem-solving skills.

1. GPT-4 by OpenAI

GPT-4, announced by OpenAI in March 2023 and released with wider access later that year, is a leading LLM renowned for its advanced reasoning and problem-solving abilities1. It has demonstrated proficiency in various fields, including:

• Complex reasoning and understanding: GPT-4 can handle intricate tasks that require multi-step reasoning and logical deduction. For example, it can solve complex word problems, understand and respond to intricate logical puzzles, and generate coherent and logical arguments in response to complex questions1.
• Advanced coding capability: It can generate code in multiple programming languages, understand code snippets, and even debug complex programs. This makes it a valuable tool for software developers and programmers, assisting them in tasks like code completion, bug identification, and code optimization1.
• Proficiency in academic exams: GPT-4 has shown impressive performance in various academic tests, including the Bar exam, where it scored in the 90th percentile, and the GRE, where it achieved scores in the top percentiles for both verbal and quantitative reasoning2. These results highlight its ability to comprehend complex information, analyze data, and generate human-quality responses in challenging academic settings.

Model Architecture and Variations

OpenAI offers a suite of GPT-4 models with different capabilities and context window sizes:

• GPT-4o: The flagship model with a 128,000-token context window, capable of handling both text and audio inputs. This model is designed for a wide range of applications, including chatbots, content creation, and complex reasoning tasks3.
• GPT-4o mini: A smaller version of GPT-4o with a 128,000-token context window, optimized for faster and less expensive processing. This model is suitable for applications where speed and cost-efficiency are paramount3.
• o1: A reasoning model with a 200,000-token context window, designed for complex, multi-step tasks. This model excels at tasks that require in-depth analysis, logical deduction, and problem-solving3.
• o1-mini: A smaller version of o1 with a 128,000-token context window. This model offers a balance between performance and efficiency for reasoning tasks3.
• GPT-4 Turbo: A faster and more cost-effective version of GPT-4 with a 128,000-token context window. This model is optimized for speed and efficiency while maintaining high performance on a variety of tasks2.

Reasoning Capabilities and Benchmarks

GPT-4 models have been evaluated on various reasoning benchmarks, demonstrating their ability to handle complex tasks and solve problems effectively. Some of the key benchmarks include:

• HumanEval: This benchmark evaluates the model's ability to generate code that solves specific programming problems. GPT-4 has achieved high scores on HumanEval, demonstrating its proficiency in code generation and understanding4.
• ARC (AI2 Reasoning Challenge): This benchmark tests the model's ability to solve science questions and demonstrate common sense reasoning. GPT-4 has shown strong performance on ARC, highlighting its ability to understand and reason about scientific concepts5.
• HellaSwag: This benchmark evaluates the model's ability to understand and reason about everyday situations and events. GPT-4 has achieved impressive results on HellaSwag, showcasing its ability to comprehend and respond to real-world scenarios6.

Applications and Use Cases

GPT-4 models are being used in a wide range of applications, including:

• General-purpose chatbots: GPT-4o is used in ChatGPT and excels at engaging in natural conversations, providing informative and coherent responses, and adapting to different conversational styles7.
• Summarizing information: GPT-4 can summarize search results, articles, and other text from the web, extracting key information and presenting it in a concise and understandable format7.
• Customer service chatbots: GPT-4 can be trained on specific business documents and data to provide automated customer support, answering customer queries, resolving issues, and providing personalized assistance7.
• Translation: GPT-4 can translate text between multiple languages, facilitating cross-cultural communication and understanding7.
• Code generation: GPT-4 and GPT-4 Turbo are proficient in generating code in various programming languages, assisting developers in tasks like code completion, bug fixing, and code optimization7.
• Content creation: GPT-4 can generate marketing copy, social media posts, and other written content, assisting marketers and content creators in generating engaging and creative content7.
• Data analysis: GPT-4 can analyze data and extract insights from text, helping researchers and analysts understand complex data and draw meaningful conclusions7.

Limitations and Ethical Considerations

While GPT-4 demonstrates impressive reasoning capabilities, it's important to acknowledge its limitations and potential biases:

• Hallucination: Like other LLMs, GPT-4 can sometimes generate incorrect or nonsensical information, referred to as "hallucination." This can be a concern in applications where accuracy and reliability are critical.
• Bias in training data: GPT-4 is trained on a massive dataset of text and code, which may contain biases and reflect societal prejudices. This can lead to biased or unfair outputs, especially in sensitive domains.
• Potential misuse: GPT-4's advanced capabilities can be misused for malicious purposes, such as generating fake news, impersonating individuals, or creating harmful content. It's crucial to use GPT-4 responsibly and ethically, with safeguards in place to prevent misuse.

2. Eurus by OpenBMB

Eurus is a suite of LLMs specifically optimized for reasoning tasks1. Developed by OpenBMB, Eurus models are fine-tuned from Mistral-7B and CodeLlama-70B, achieving state-of-the-art results among open-source models1.

Model Architecture and Variations

Eurus offers several models with different training approaches and sizes:

• Eurus-7b-sft: Fine-tuned from Mistral-7B using supervised fine-tuning (SFT) on the UltraInteract dataset8.
• Eurus-7b-kto: Fine-tuned from Mistral-7B using the "knowledge to opinion" (KTO) preference learning algorithm8.
• Eurus-70b-sft: Fine-tuned from CodeLlama-70B using SFT8.
• Eurus-70b-nca: Fine-tuned from CodeLlama-70B using the "noisy channel augmentation" (NCA) preference learning algorithm8.
• Eurus-RM-7b: A reward model fine-tuned from Mistral-7B8.

Reasoning Capabilities and Benchmarks

Eurus models excel in various reasoning tasks, as demonstrated by their performance on several benchmarks:

• GSMPLUS: This benchmark evaluates the model's ability to solve grade-school math word problems. Eurus models have achieved high accuracy on GSMPLUS, showcasing their proficiency in mathematical reasoning9.
• MATH: This benchmark tests the model's ability to solve more complex mathematical problems, including those requiring logical deduction and problem-solving skills. Eurus models have demonstrated state-of-the-art results on MATH, highlighting their advanced mathematical reasoning capabilities9.
• BBH (Big-Bench Hard): This benchmark comprises a diverse set of challenging reasoning tasks, including logical puzzles, common sense reasoning, and problem-solving. Eurus models have shown strong performance on BBH, demonstrating their ability to handle a wide range of reasoning challenges9.

Training Datasets

The strong performance of Eurus models can be attributed to the high-quality datasets used for their training:

• UltraInteract: This dataset is specifically designed for complex reasoning tasks and includes a diverse set of instructions spanning math, coding, and logical reasoning problems. It pairs each instruction with a preference tree consisting of reasoning chains, multi-turn interaction trajectories, and pairwise positive and negative responses10.
• UltraFeedback: This dataset provides feedback on the model's responses, helping it learn and improve its reasoning abilities. It includes pairwise comparisons of responses, allowing the model to learn from its mistakes and refine its reasoning strategies9.

Applications and Use Cases

Eurus models are well-suited for applications that require advanced reasoning capabilities, such as:

• Education: Eurus can be used to create AI tutors that provide personalized instruction and feedback to students, helping them learn and improve their reasoning skills.
• Research: Eurus can assist researchers in tasks like data analysis, hypothesis generation, and problem-solving, accelerating scientific discovery and innovation.
• Automated decision-making: Eurus can be used in applications that require automated reasoning and decision-making, such as financial forecasting, risk assessment, and resource allocation.

Limitations and Ethical Considerations

While Eurus models demonstrate impressive reasoning capabilities, it's important to consider their limitations and potential biases:

• Dependence on training data: Eurus models are heavily reliant on the quality and diversity of their training data. Biases in the training data can lead to biased or unfair outputs.
• Limited explainability: Eurus models, like other deep learning models, can be difficult to interpret and understand. This can be a concern in applications where transparency and explainability are crucial.
• Potential for misuse: Eurus models can be misused for malicious purposes, such as generating misleading information or manipulating data. It's important to use Eurus responsibly and ethically, with safeguards in place to prevent misuse.

3. Dolphin Llama 13B

Dolphin Llama 13B is an open-source, uncensored LLM known for its strong reasoning capabilities in math and logic1. It's based on the Llama architecture and prioritizes non-commercial usage1.

Model Architecture and Variations

While specific variations of Dolphin Llama 13B are not explicitly mentioned in the provided research material, it's likely that different versions with varying levels of fine-tuning and parameter sizes exist.

One notable iteration is Dolphin 2.1, which introduces a novel decoder-decoder architecture for efficient long context processing5. This architecture utilizes memory tokens to compress extensive contextual information, significantly reducing the input length for the primary decoder model. This results in a 10-fold improvement in energy efficiency and a 5-fold improvement in latency compared to conventional full-length context processing methods5.

Reasoning Capabilities and Benchmarks

Dolphin Llama 13B specializes in:

• Mathematics: It demonstrates strong reasoning abilities within mathematical problems, achieving high accuracy on benchmarks like GSM8K, which evaluates the model's ability to solve grade-school math word problems11.
• Logic: It excels at logical reasoning tasks and problem-solving, demonstrating proficiency in understanding and responding to logical puzzles and reasoning challenges1.

Applications and Use Cases

Dolphin Llama 13B's reasoning capabilities make it suitable for applications such as:

• Educational tools: It can be used to develop AI-powered learning platforms that provide personalized instruction and feedback to students in math and logic.
• Research assistants: It can assist researchers in tasks that require logical reasoning and problem-solving, such as data analysis, hypothesis testing, and scientific inquiry.
• Game development: It can be used to create AI agents in games that exhibit intelligent behavior and logical decision-making.

Limitations and Ethical Considerations

While Dolphin Llama 13B offers strong reasoning capabilities, it's important to consider its limitations and potential biases:

• Uncensored nature: As an uncensored model, Dolphin Llama 13B may generate responses that are considered inappropriate or harmful. Users need to be aware of this and implement appropriate safeguards11.
• Limited commercial use: Its prioritization of non-commercial usage may restrict its applicability in certain business settings1.
• Potential for bias: Like other LLMs, Dolphin Llama 13B can be susceptible to biases present in its training data, which may lead to biased or unfair outputs.

4. Gemini by Google

Gemini is a family of multimodal AI models developed by Google7. It's used in various Google products, including the Gemini chatbot, Google Docs, and Gmail1.

Model Architecture and Variations

Gemini offers a range of models optimized for different devices and use cases:

• Gemini 2.0 Flash: The newest multimodal model with improved capabilities and next-generation features12.
• Gemini 2.0 Flash-Lite: A cost-efficient and low-latency version of Gemini 2.0 Flash12.
• Gemini 1.5 Flash: A fast and versatile model for various tasks12.
• Gemini 1.5 Flash-8B: A smaller model designed for high-volume and lower-intelligence tasks12.
• Gemini 1.5 Pro: A mid-size model optimized for a wide range of reasoning tasks12.
• Gemini 1.0 Ultra: A large and capable model for highly complex tasks13.
• Gemini Nano: An efficient model for on-device tasks13.

Reasoning Capabilities and Benchmarks

Gemini models have demonstrated strong reasoning capabilities in various domains:

• MMLU (Massive Multitask Language Understanding): Gemini Ultra is the first model to outperform human experts on MMLU, a benchmark that uses a combination of 57 subjects, including math, physics, history, law, medicine, and ethics, to test both world knowledge and problem-solving abilities13.
• HumanEval: Gemini Ultra excels in several coding benchmarks, including HumanEval, demonstrating its proficiency in code generation and understanding13.
• Natural2Code: This internal benchmark evaluates the model's ability to generate code from natural language descriptions. Gemini Ultra has shown impressive performance on Natural2Code, highlighting its ability to understand and translate human instructions into code13.

Applications and Use Cases

Gemini models specialize in various fields, including:

• Text generation and content creation: Gemini excels at generating creative text formats, including blog posts, scripts, and musical pieces. It can assist writers, marketers, and content creators in generating engaging and original content14.
• Machine translation and language understanding: Gemini can translate languages with high accuracy and understand nuanced language, facilitating cross-cultural communication and understanding14.
• Question answering and information retrieval: Gemini can answer questions and retrieve information from various sources, providing accurate and relevant information to users14.
• Code generation and creative coding: Gemini can generate code in popular programming languages and assist with coding tasks, helping developers write code more efficiently and effectively14.
• Multimodal dialogue and natural conversations: Gemini can engage in natural conversations and understand different conversational styles, making it suitable for chatbot development and other conversational AI applications14.

Limitations and Ethical Considerations

While Gemini models exhibit strong reasoning capabilities, it's important to consider their limitations and potential biases:

• Dependence on data quality: Gemini models are trained on massive datasets, and their performance is heavily reliant on the quality and diversity of this data. Biases in the training data can lead to biased or unfair outputs.
• Limited explainability: Like other deep learning models, Gemini models can be difficult to interpret and understand, which can be a concern in applications where transparency and explainability are crucial.
• Potential for misuse: Gemini's advanced capabilities can be misused for malicious purposes, such as generating fake news or manipulating information. It's crucial to use Gemini responsibly and ethically, with safeguards in place to prevent misuse.

5. Claude 3 by Anthropic

Claude 3 is a family of LLMs developed by Anthropic, known for their helpfulness, honesty, and harmlessness7. They are used in various applications, including Slack, Notion, and Zoom1.

Model Architecture and Variations

Claude 3 offers three models with different capabilities and speeds:

• Claude 3.5 Haiku: The fastest and most compact model, designed for near-instant responsiveness15.
• Claude 3.5 Sonnet: A balanced model that offers a good combination of intelligence and speed15.
• Claude 3 Opus: The most intelligent model in the family, designed for complex reasoning tasks15.

Reasoning Capabilities and Benchmarks

Claude models are known for their nuanced reasoning and detailed analysis capabilities16. They excel in:

• GRE reading and writing exams: Claude 2.0 scored above the 90th percentile compared to college students applying to graduate school, demonstrating its ability to comprehend and analyze complex written material17.
• Multi-step reasoning: Claude 3 models have shown significant improvements in handling multi-step workflows and complex reasoning tasks, such as analyzing charts and graphs, extracting information from text, and generating detailed reports17.
• Long document handling: Claude models can handle long documents and perform tasks like summarization and question answering with extended context. This is achieved through their large context window and efficient processing capabilities18.

Constitutional AI

A key aspect of Claude's development is its "Constitutional AI" approach19. This approach involves training the model on a set of principles and guidelines, similar to a constitution, to ensure its safety and helpfulness. This helps Claude avoid generating harmful or inappropriate content and promotes ethical and responsible AI behavior.

Applications and Use Cases

Claude models are used in various applications, including:

• Advanced reasoning: Claude can perform complex cognitive tasks beyond simple pattern recognition, such as analyzing complex data, generating creative content, and solving intricate problems20.
• Vision analysis: Claude can transcribe and analyze images, including handwritten notes and graphs, extracting information and providing insights from visual data20.
• Code generation: Claude can generate code, turn images into structured data, and debug code, assisting developers in various programming tasks20.
• Multilingual processing: Claude can translate between languages, practice grammar, and create multilingual content, facilitating cross-cultural communication and understanding20.

Limitations and Ethical Considerations

While Claude models demonstrate strong reasoning capabilities, it's important to consider their limitations and potential biases:

• Dependence on constitutional AI: Claude's reliance on its "constitution" may limit its ability to handle certain types of requests or generate responses that deviate from its guidelines.
• Potential for bias: Despite its safety measures, Claude can still be susceptible to biases present in its training data, which may lead to biased or unfair outputs.
• Limited transparency: The specific details of Claude's "constitution" and its implementation may not be fully transparent, which can be a concern for users who require full explainability.

Comparative Analysis of Reasoning Capabilities

The five LLMs discussed in this article exhibit diverse reasoning capabilities and strengths:

• GPT-4: Excels in complex reasoning, code generation, and academic proficiency, with a wide range of models and applications.
• Eurus: Specifically optimized for reasoning, achieving state-of-the-art results on math, code generation, and logical reasoning benchmarks.
• Dolphin Llama 13B: Strong in math and logic, with an uncensored nature and a focus on non-commercial usage.
• Gemini: A multimodal model with strong performance in various domains, including text generation, translation, and code generation.
• Claude: Known for nuanced reasoning, detailed analysis, and a "Constitutional AI" approach for safety and helpfulness.

The choice of the most suitable LLM for a specific task depends on the specific requirements and priorities. For example, if advanced coding capabilities are crucial, GPT-4 or Gemini might be preferred. If mathematical reasoning is paramount, Eurus or Dolphin Llama 13B could be more suitable. And if safety and ethical considerations are paramount, Claude might be the best choice.

Summary Table

LLM

Models

Context Window

Specializations

Notable Features

GPT-4

GPT-4o, GPT-4o mini, o1, o1-mini, GPT-4 Turbo

8,192 - 200,000 tokens

Complex reasoning, code generation, academic proficiency

Multimodal capabilities (GPT-4o), high performance on reasoning benchmarks

Eurus

Eurus-7b-sft, Eurus-7b-kto, Eurus-70b-sft, Eurus-70b-nca, Eurus-RM-7b

Up to 32,768 tokens (estimated)

Mathematics, code generation, logical reasoning

State-of-the-art results on reasoning benchmarks, trained on specialized datasets (UltraInteract, UltraFeedback)

Dolphin Llama 13B

Dolphin 2.1, other variations

Not specified

Mathematics, logic

Open-source, uncensored, efficient long context processing (Dolphin 2.1)

Gemini

Gemini 2.0 Flash, Gemini 2.0 Flash-Lite, Gemini 1.5 Flash, Gemini 1.5 Flash-8B, Gemini 1.5 Pro, Gemini 1.0 Ultra, Gemini Nano

32,768 - 2,000,000 tokens

Text generation, translation, question answering, code generation, multimodal dialogue

Multimodal capabilities, long context window, high performance on various benchmarks

Claude 3

Claude 3.5 Haiku, Claude 3.5 Sonnet, Claude 3 Opus

200,000 tokens

Advanced reasoning, vision analysis, code generation, multilingual processing

Constitutional AI approach for safety and helpfulness, strong performance on reasoning and language tasks

Future Trends in LLM Reasoning

The field of LLM reasoning is rapidly evolving, with ongoing research and development pushing the boundaries of AI capabilities. Some of the key future trends include:

• Advancements in multimodal reasoning: LLMs are becoming increasingly capable of handling multiple modalities, such as text, images, and audio, simultaneously. This will enable them to understand and reason about the world in a more comprehensive and human-like manner.
• Improved explainability: Researchers are working on making LLM reasoning more transparent and explainable. This will help users understand how LLMs arrive at their conclusions and build trust in their reasoning abilities.
• Development of more robust and reliable reasoning models: Ongoing efforts are focused on developing LLMs that are more robust to noise, bias, and adversarial attacks, ensuring that their reasoning remains reliable and trustworthy in various situations.

The advancements in LLM reasoning have the potential to revolutionize various fields, from education and research to healthcare and customer service. As these models continue to evolve, we can expect even more impressive reasoning capabilities and applications in the years to come.

Works cited

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