Best AI tools for< Reasoning Answers >

OpenAI01.net is an AI tool that offers free usage with some limitations. It provides a new series of AI models designed to spend more time thinking before responding, capable of reasoning through complex tasks and solving harder problems in science, coding, and math. Users can ask questions and get answers for free, with the option to select different models based on credits. The tool excels in complex reasoning tasks and has shown impressive performance in various benchmarks.

echowin is an AI Voice Agent Builder Platform that enables businesses to create AI agents for calls, chat, and Discord. It offers a comprehensive solution for automating customer support with features like Agentic AI logic and reasoning, support for over 30 languages, parallel call answering, and 24/7 availability. The platform allows users to build, train, test, and deploy AI agents quickly and efficiently, without compromising on capabilities or scalability. With a focus on simplicity and effectiveness, echowin empowers businesses to enhance customer interactions and streamline operations through cutting-edge AI technology.

Ask a Philosopher is a website where users can submit questions to be answered by a philosopher. The platform allows individuals to seek philosophical insights and perspectives on various topics. It serves as a space for intellectual discourse and exploration of ideas, offering a unique opportunity to engage with philosophical thinking in a practical and accessible manner.

The website features Grok-1.5, an AI application that bridges the gap between the digital and physical worlds through its multimodal model. Grok-1.5 boasts enhanced reasoning capabilities and a context length of 128,000 tokens. Additionally, the platform offers PromptIDE, an IDE for prompt engineering and interpretability research, allowing users to create and share complex prompts in Python. Grok, an AI modeled after the Hitchhiker’s Guide to the Galaxy, is also available on the site, providing answers to a wide range of questions and even suggesting relevant queries. The platform aims to facilitate knowledge sharing and exploration through advanced AI technologies.

Tutorly is an AI-powered tutoring platform that offers personalized quiz questions, interactive verbal reasoning exercises, and tailored feedback to enhance the learning experience. Users can choose from a selection of premade tutors or provide custom instructions, upload notes, chat with the tutor, and ask unlimited questions to get instant, accurate answers. With flexible pricing plans and access to beta gamemodes, Tutorly aims to revolutionize the way students learn beyond limits.

Mendel AI is an advanced clinical AI tool that deciphers clinical data with clinician-like logic. It offers a fully integrated suite of clinical-specific data processing products, combining OCR, de-identification, and clinical reasoning to interpret medical records. Users can ask questions in plain English and receive accurate answers from health records in seconds. Mendel's technology goes beyond traditional AI by understanding patient-level data and ensuring consistency and explainability of results in healthcare.

Hey Mind is a new kind of design lab that aims to unlock hidden ideas through AI-powered tools. The platform offers a unique journaling experience with LB, an inner guide, to help users understand their thoughts better. Hey Mind promotes a new renaissance by transforming work into play, enhancing productivity through creative workflows and innovative thinking. Users can revolutionize the way they work and think, with a focus on boosting efficiency and creativity.

Choosy Chat is an AI-powered chat application that utilizes advanced AI models such as OpenAI GPT-4o and Google Gemini Pro 1.5 to provide intelligent responses and engage in meaningful conversations with users. The application is designed to assist users in various tasks, including answering questions, providing information on recent knowledge, coding assistance, and reasoning puzzles. Choosy Chat aims to enhance user experience through its cutting-edge AI technology and user-friendly interface.

Gemini is a large and powerful AI model developed by Google. It is designed to handle a wide variety of text and image reasoning tasks, and it can be used to build a variety of AI-powered applications. Gemini is available in three sizes: Ultra, Pro, and Nano. Ultra is the most capable model, but it is also the most expensive. Pro is the best performing model for a wide variety of tasks, and it is a good value for the price. Nano is the most efficient model, and it is designed for on-device use cases.

Alan AI is an advanced conversational AI platform that offers a wide range of AI solutions for various industries. It simplifies tasks, enhances business operations, and empowers sales strategies through AI technology. The platform provides features like question answering, semantic search, reporting, private data sources, and context awareness. With a focus on actionable AI, Alan AI aims to redefine learning and streamline decision-making processes. It offers a comprehensive suite of tools for developers, including technology architecture overview, integration, deployment, and analytics. Alan AI stands out for its innovative approach to AI reasoning, transparency, and control, making it a valuable asset for organizations seeking to leverage AI capabilities.

Kie.ai is an AI platform that offers access to DeepSeek R1 & V3 APIs for secure and scalable AI solutions. It provides advanced reasoning models for tasks in math, coding, and language, along with versatile natural language processing capabilities. With no local deployment required, developers can easily integrate the APIs into their projects for fast and efficient AI solutions. Kie.ai ensures data security by hosting the APIs on U.S.-based servers, offering affordable pricing plans and comprehensive documentation for seamless integration.

Reka is a cutting-edge AI application offering next-generation multimodal AI models that empower agents to see, hear, and speak. Their flagship model, Reka Core, competes with industry leaders like OpenAI and Google, showcasing top performance across various evaluation metrics. Reka's models are natively multimodal, capable of tasks such as generating textual descriptions from videos, translating speech, answering complex questions, writing code, and more. With advanced reasoning capabilities, Reka enables users to solve a wide range of complex problems. The application provides end-to-end support for 32 languages, image and video comprehension, multilingual understanding, tool use, function calling, and coding, as well as speech input and output.

Prompt Engineering is a discipline focused on developing and optimizing prompts to efficiently utilize language models (LMs) for various applications and research topics. It involves skills to understand the capabilities and limitations of large language models, improving their performance on tasks like question answering and arithmetic reasoning. Prompt engineering is essential for designing robust prompting techniques that interact with LLMs and other tools, enhancing safety and building new capabilities by augmenting LLMs with domain knowledge and external tools.

Imandra is a company that provides automated logical reasoning for Large Language Models (LLMs). Imandra's technology allows LLMs to build mental models and reason about them, unlocking the potential of generative AI for industries where correctness and compliance matter. Imandra's platform is used by leading financial firms, the US Air Force, and DARPA.

HelloScribe is an autonomous reasoning engine that provides high-level creativity, strategy, and planning. It offers over 150 precision-made AI tools and templates, the ability to create in over 50 languages, speech-to-text functionality, and access to over 200 million research papers, live news, and web search. HelloScribe is designed to help professionals in various fields, including sales, marketing, consulting, and research, by automating tasks, providing real-time insights, and facilitating collaboration.

LangChain is an AI tool that offers a suite of products supporting developers in the LLM application lifecycle. It provides a framework to construct LLM-powered apps easily, visibility into app performance, and a turnkey solution for serving APIs. LangChain enables developers to build context-aware, reasoning applications and future-proof their applications by incorporating vendor optionality. LangSmith, a part of LangChain, helps teams improve accuracy and performance, iterate faster, and ship new AI features efficiently. The tool is designed to drive operational efficiency, increase discovery & personalization, and deliver premium products that generate revenue.

DeepSeek R1 is a revolutionary open-source AI model for advanced reasoning that outperforms leading AI models in mathematics, coding, and general reasoning tasks. It utilizes a sophisticated MoE architecture with 37B active/671B total parameters and 128K context length, incorporating advanced reinforcement learning techniques. DeepSeek R1 offers multiple variants and distilled models optimized for complex problem-solving, multilingual understanding, and production-grade code generation. It provides cost-effective pricing compared to competitors like OpenAI o1, making it an attractive choice for developers and enterprises.

AI Builders Summit is a 4-week virtual training event designed to equip data scientists, ML and AI engineers, and innovators with the latest advancements in large language models (LLMs), AI agents, and Retrieval-Augmented Generation (RAG). The summit emphasizes hands-on learning and real-world applications, with interactive workshops, platform credits, and direct exposure to industry-leading tools. Attendees can learn progressively over four weeks, building practical skills through expert-led sessions, cutting-edge tools, and industry insights.

Socratify is an AI tool designed for professionals, students, and curious minds to sharpen their debating skills. It offers curated business stories, challenges users with AI, provides personalized feedback, and encourages daily practice in just 5 minutes. Users can enhance decision-making, explore real business situations, and improve critical thinking through active learning. Socratify aims to upgrade how humans think and learn by leveraging AI technology.

Cognition is an applied AI lab that has developed Devin, a collaborative AI teammate designed to assist engineering teams in achieving more. Devin is the world's first AI software engineer, available for all engineering teams. The team behind Cognition comprises individuals with extensive experience in applied AI at top companies, and they are focused on building AI that can reason and solve complex problems.

ST-LLM is a temporal-sensitive video large language model that incorporates joint spatial-temporal modeling, dynamic masking strategy, and global-local input module for effective video understanding. It has achieved state-of-the-art results on various video benchmarks. The repository provides code and weights for the model, along with demo scripts for easy usage. Users can train, validate, and use the model for tasks like video description, action identification, and reasoning.

This repository is dedicated to summarizing papers related to large language models with the field of law. It includes applications of large language models in legal tasks, legal agents, legal problems of large language models, data resources for large language models in law, law LLMs, and evaluation of large language models in the legal domain.

HuatuoGPT-o1 is a medical language model designed for advanced medical reasoning. It can identify mistakes, explore alternative strategies, and refine answers. The model leverages verifiable medical problems and a specialized medical verifier to guide complex reasoning trajectories and enhance reasoning through reinforcement learning. The repository provides access to models, data, and code for HuatuoGPT-o1, allowing users to deploy the model for medical reasoning tasks.

ReST-MCTS is a reinforced self-training approach that integrates process reward guidance with tree search MCTS to collect higher-quality reasoning traces and per-step value for training policy and reward models. It eliminates the need for manual per-step annotation by estimating the probability of steps leading to correct answers. The inferred rewards refine the process reward model and aid in selecting high-quality traces for policy model self-training.

Foyle is a project focused on building agents to assist software developers in deploying and operating software. It aims to improve agent performance by collecting human feedback on agent suggestions and human examples of reasoning traces. Foyle utilizes a literate environment using vscode notebooks to interact with infrastructure, capturing prompts, AI-provided answers, and user corrections. The goal is to continuously retrain AI to enhance performance. Additionally, Foyle emphasizes the importance of reasoning traces for training agents to work with internal systems, providing a self-documenting process for operations and troubleshooting.

MathVerse is an all-around visual math benchmark designed to evaluate the capabilities of Multi-modal Large Language Models (MLLMs) in visual math problem-solving. It collects high-quality math problems with diagrams to assess how well MLLMs can understand visual diagrams for mathematical reasoning. The benchmark includes 2,612 problems transformed into six versions each, contributing to 15K test samples. It also introduces a Chain-of-Thought (CoT) Evaluation strategy for fine-grained assessment of output answers.

DeepAI is a proxy server that enhances the interaction experience of large language models (LLMs) by integrating the 'thinking chain' process. It acts as an intermediary layer, receiving standard OpenAI API compatible requests, using independent 'thinking services' to generate reasoning processes, and then forwarding the enhanced requests to the LLM backend of your choice. This ensures that responses are not only generated by the LLM but also based on pre-inference analysis, resulting in more insightful and coherent answers. DeepAI supports seamless integration with applications designed for the OpenAI API, providing endpoints for '/v1/chat/completions' and '/v1/models', making it easy to integrate into existing applications. It offers features such as reasoning chain enhancement, flexible backend support, API key routing, weighted random selection, proxy support, comprehensive logging, and graceful shutdown.

DeepSearcher is a tool that combines reasoning LLMs and Vector Databases to perform search, evaluation, and reasoning based on private data. It is suitable for enterprise knowledge management, intelligent Q&A systems, and information retrieval scenarios. The tool maximizes the utilization of enterprise internal data while ensuring data security, supports multiple embedding models, and provides support for multiple LLMs for intelligent Q&A and content generation. It also includes features like private data search, vector database management, and document loading with web crawling capabilities under development.

The 'awesome-deliberative-prompting' repository focuses on how to ask Large Language Models (LLMs) to produce reliable reasoning and make reason-responsive decisions through deliberative prompting. It includes success stories, prompting patterns and strategies, multi-agent deliberation, reflection and meta-cognition, text generation techniques, self-correction methods, reasoning analytics, limitations, failures, puzzles, datasets, tools, and other resources related to deliberative prompting. The repository provides a comprehensive overview of research, techniques, and tools for enhancing reasoning capabilities of LLMs.

Raspberry is an open source project aimed at creating a toy dataset for finetuning Large Language Models (LLMs) with reasoning abilities. The project involves synthesizing complex user queries across various domains, generating CoT and Self-Critique data, cleaning and rectifying samples, finetuning an LLM with the dataset, and seeking funding for scalability. The ultimate goal is to develop a dataset that challenges models with tasks requiring math, coding, logic, reasoning, and planning skills, spanning different sectors like medicine, science, and software development.

MisguidedAttention is a collection of prompts designed to challenge the reasoning abilities of large language models by presenting them with modified versions of well-known thought experiments, riddles, and paradoxes. The goal is to assess the logical deduction capabilities of these models and observe any shortcomings or fallacies in their responses. The repository includes a variety of prompts that test different aspects of reasoning, such as decision-making, probability assessment, and problem-solving. By analyzing how language models handle these challenges, researchers can gain insights into their reasoning processes and potential biases.

CLadder is a repository containing the CLadder dataset for evaluating causal reasoning in language models. The dataset consists of yes/no questions in natural language that require statistical and causal inference to answer. It includes fields such as question_id, given_info, question, answer, reasoning, and metadata like query_type and rung. The dataset also provides prompts for evaluating language models and example questions with associated reasoning steps. Additionally, it offers dataset statistics, data variants, and code setup instructions for using the repository.

LiveBench is a benchmark tool designed for Language Model Models (LLMs) with a focus on limiting contamination through monthly new questions based on recent datasets, arXiv papers, news articles, and IMDb movie synopses. It provides verifiable, objective ground-truth answers for accurate scoring without an LLM judge. The tool offers 18 diverse tasks across 6 categories and promises to release more challenging tasks over time. LiveBench is built on FastChat's llm_judge module and incorporates code from LiveCodeBench and IFEval.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

The 'farel-bench' project is a benchmark tool for testing LLM reasoning abilities with family relationship quizzes. It generates quizzes based on family relationships of varying degrees and measures the accuracy of large language models in solving these quizzes. The project provides scripts for generating quizzes, running models locally or via APIs, and calculating benchmark metrics. The quizzes are designed to test logical reasoning skills using family relationship concepts, with the goal of evaluating the performance of language models in this specific domain.

This repository focuses on test-time computing, exploring various strategies such as test-time adaptation, modifying the input, editing the representation, calibrating the output, test-time reasoning, and search strategies. It covers topics like self-supervised test-time training, in-context learning, activation steering, nearest neighbor models, reward modeling, and multimodal reasoning. The repository provides resources including papers and code for researchers and practitioners interested in enhancing the reasoning capabilities of large language models.

AReaL (Ant Reasoning RL) is an open-source reinforcement learning system developed at the RL Lab, Ant Research. It is designed for training Large Reasoning Models (LRMs) in a fully open and inclusive manner. AReaL provides reproducible experiments for 1.5B and 7B LRMs, showcasing its scalability and performance across diverse computational budgets. The system follows an iterative training process to enhance model performance, with a focus on mathematical reasoning tasks. AReaL is equipped to adapt to different computational resource settings, enabling users to easily configure and launch training trials. Future plans include support for advanced models, optimizations for distributed training, and exploring research topics to enhance LRMs' reasoning capabilities.

MMMU is a benchmark designed to evaluate multimodal models on college-level subject knowledge tasks, covering 30 subjects and 183 subfields with 11.5K questions. It focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of various models highlights substantial challenges, with room for improvement to stimulate the community towards expert artificial general intelligence (AGI).

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

The Self-Iterative Agent System for Complex Problem Solving is a solution developed for the Alibaba Mathematical Competition (AI Challenge). It involves multiple LLMs engaging in multi-round 'self-questioning' to iteratively refine the problem-solving process and select optimal solutions. The system consists of main and evaluation models, with a process that includes detailed problem-solving steps, feedback loops, and iterative improvements. The approach emphasizes communication and reasoning between sub-agents, knowledge extraction, and the importance of Agent-like architectures in complex tasks. While effective, there is room for improvement in model capabilities and error prevention mechanisms.

Guides you through detailed reasoning to find well-supported answers.

Ubbe generates answers, not just advice. Designed for action, Ubbe adjusts its reasoning framework and tool use as your objective evolves, allowing you to solve even the most complex tasks.

An astute critical-thinking partner with the deductive reasoning skills of Sherlock Holmes

I imitate GPT-5 LLM, with advanced reasoning, personalization, and higher emotional intelligence

Solves problems using the simplest solutions, explains reasoning concisely.

Conversate numerically to your prompts

Formal academic logic analysis.

My strong counterarguments refine reasoning, fostering intellectual growth.

Grades op-eds on reasoning, fair engagement, and open-mindedness.

Self Discover: Self-Composing Reasoning Structures. A self-reflecting reasoning agent.

I identify flaws in debates and outline reasoning errors.

Expert at logical puzzles, reasoning, and thoughtful analysis. Uses CoT and ToT.

Constructive advisor in educational strategies, focusing on inverse reasoning and DDD.

Articulate, precise mentor employing the Socratic method & Batesonian reasoning to find solution to issues (updated on 10 Jan 24)

A decisive entity that makes choices and explains reasoning.

As LegalGPT, I'm an AI legal assistant with expertise in law, adaptable for nationwide legal queries. I provide precise, context-sensitive advice based on a rich knowledge source, aiding in legal reasoning and drafting. Note: I'm not a substitute for a lawyer.

海龜湯是訓練邏輯推理與MECE的好遊戲，透過「是」、「不是」或「無關」得到線索，拼湊出完整的劇情。如果太過困難，可以說「你贏了」

A logician with a witty and gentlemanly demeanor.

Stephen Toulmin’s Theory of Argumentation. FIRST TIME? Start with "Good morning!" PRIMEIRA VEZ? Comece com um "Bom dia!"

解答数理逻辑问题的专家