Alternatives to DeepSeekMath

DeepSeek-Coder-V2 is an open-source model tailored for excellence in programming and mathematical reasoning tasks. Utilizing a Mixture-of-Experts (MoE) architecture, it boasts a staggering 236 billion total parameters, with 21 billion of those being activated per token, which allows for efficient processing and outstanding performance. Trained on a massive dataset comprising 6 trillion tokens, this model enhances its prowess in generating code and tackling mathematical challenges. With the ability to support over 300 programming languages, DeepSeek-Coder-V2 has consistently outperformed its competitors on various benchmarks. It is offered in several variants, including DeepSeek-Coder-V2-Instruct, which is optimized for instruction-based tasks, and DeepSeek-Coder-V2-Base, which is effective for general text generation. Additionally, the lightweight options, such as DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct, cater to environments that require less computational power. These variations ensure that developers can select the most suitable model for their specific needs, making DeepSeek-Coder-V2 a versatile tool in the programming landscape.

Aristotle represents a groundbreaking advancement as the inaugural AI model constructed entirely as a Mathematical Superintelligence (MSI), aimed at providing solutions that are mathematically verified to intricate quantitative challenges without any instances of hallucination. When it receives inquiries in natural language related to mathematics, it translates these into Lean 4 formalism, solves them through rigorously verified proofs, and subsequently delivers both the proof alongside a natural language interpretation. In contrast to traditional language models that depend on probabilistic methods, the MSI framework of Aristotle eliminates uncertainty by employing demonstrable logic and openly identifying any errors or discrepancies. This innovative AI can be accessed via a web interface and developer API, allowing researchers to incorporate its precise reasoning capabilities into various domains, including theoretical physics, engineering, and computer science. Its design not only streamlines problem-solving but also enhances the reliability of results across multiple disciplines.

DeepSeek-R1 is a cutting-edge open-source reasoning model created by DeepSeek, aimed at competing with OpenAI's Model o1. It is readily available through web, app, and API interfaces, showcasing its proficiency in challenging tasks such as mathematics and coding, and achieving impressive results on assessments like the American Invitational Mathematics Examination (AIME) and MATH. Utilizing a mixture of experts (MoE) architecture, this model boasts a remarkable total of 671 billion parameters, with 37 billion parameters activated for each token, which allows for both efficient and precise reasoning abilities. As a part of DeepSeek's dedication to the progression of artificial general intelligence (AGI), the model underscores the importance of open-source innovation in this field. Furthermore, its advanced capabilities may significantly impact how we approach complex problem-solving in various domains.

MathGPT.ai is an innovative, AI-enhanced platform for math tutoring and course management that ensures a cheat-proof learning environment by skillfully merging an intelligent course creator with an AI tutor and assignment manager. Educators can easily incorporate existing open educational resources (OER), upload their own textbooks, lecture notes, or various materials, providing students with interactive learning experiences that occur directly within their chosen resources. The platform assists learners in navigating problems through detailed, step-by-step guidance, tailored coaching, and immediate feedback, all aimed at fostering a profound comprehension and boosting confidence. Its unique, deterministic AI model is finely tuned for exceptional accuracy in mathematical reasoning, steering clear of the usual inaccuracies associated with typical language models. Widely embraced in educational settings, the platform also includes live demonstrations that illustrate its problem-solving capabilities and adaptability to different learning preferences, while it actively supports STEM education through curriculum-compliant, endlessly patient instruction. With features designed to engage students and enhance their academic journey, MathGPT.ai stands as a vital tool for both educators and learners.

Obtain detailed, step-by-step guidance for solving mathematical problems, complete with worked-out examples and definitions of key concepts. You can visualize functions and understand variable relationships by instantly graphing any equation. Additionally, you can create graphs for your math challenges and search for supplementary educational resources, such as worksheets and instructional videos. Math assistance is available in your preferred language, and with Math Solver integrated into Microsoft Edge, you can receive help directly alongside your problems. By scanning photos of your math work, whether written by hand or calculated, Microsoft Math quickly identifies the issues and offers comprehensive solutions through detailed explanations, interactive graphs, and access to similar problems and online lectures. You can also efficiently research related mathematical ideas to enhance your understanding. As you work with Microsoft Mathematics, you will build confidence in your skills and techniques. You can write equations on the screen just like you would on paper, or use a sophisticated scientific math calculator to type and adjust your calculations easily. This seamless integration of tools ensures that you have the resources needed to succeed in your math studies.

This math homework application offers precise, immediate, and comprehensive solutions for math problems at any level. It excels at addressing difficult word problems and tackling the most intricate mathematical inquiries. If you continue to face challenges with your math assignments, you can consult with CameraMath's carefully selected, top-tier math tutors who are available online around the clock. As a leading platform for math problem-solving and learning, CameraMath serves millions of students seeking answers, preparing for exams, and exploring mathematical concepts. The app delivers not only a thorough solution but also a clear explanation for every math query, guiding you through each step in a way that enhances your understanding of how to reach the answer. CameraMath exposes learners to a variety of problem-solving techniques, fostering a deep and adaptable knowledge of mathematics. You can discover multiple methods for resolving even the most daunting math challenges. Word problems often pose a significant obstacle in math homework, and while many math solvers may struggle with them, CameraMath consistently rises to the occasion, ensuring you can tackle every problem with confidence. With its user-friendly interface and expert assistance, CameraMath is designed to empower students in their pursuit of math mastery.

AI Math Solver is an advanced tool leveraging artificial intelligence to assist users in efficiently tackling and comprehending a variety of mathematical challenges. Its key features include: ● Extensive Math Coverage: The tool encompasses numerous branches of mathematics, such as arithmetic, algebra, geometry, trigonometry, calculus, and statistics, adeptly managing issues ranging from simple to highly complex. ● Image Recognition Capability: Users can capture images of math problems with their mobile devices, and the AI will swiftly identify the problems and deliver comprehensive, step-by-step solutions. ● Diverse Language Options: With support for over 30 languages, including Simplified Chinese, it caters to a global audience with varying language preferences. ● Quick Response Time: Users can obtain precise answers in as little as 10 seconds, accompanied by thorough solution steps to enhance their understanding of the material. ● Educational Materials: The platform provides interactive graphs, engaging video tutorials, and a variety of practice problems to further aid users in their learning journey. ● User-Friendly Interface: Designed with ease of use in mind, the tool ensures that users can navigate seamlessly and access its features without any hassle.

Interactive Mathematics is dedicated to sparking interest and educating individuals about the wonders of mathematics. It achieves this by offering straightforward examples, connecting concepts to real-life situations, and featuring interactive applets that enable users to delve into mathematical ideas. Annually, it assists over 5 million students who access our free lessons to excel in math. Building on that foundation, we have integrated our expertise with AI to launch a complimentary AI math problem solver and tutoring chat platform. This innovation merges a robust mathematical computational engine with advanced artificial intelligence to deliver a cutting-edge math problem solver and calculator. It surpasses ChatGPT in accuracy, outperforms traditional calculators in capability, and provides quicker responses than a human tutor! From complex word problems to algebraic equations and intricate calculus, our AI math problem solver and calculator can tackle it all. Additionally, our solver is adept at interpreting mathematical word problems and discerning the necessary mathematical operations to apply. This multifaceted approach not only enhances learning but also empowers students to develop a deeper understanding of math.

Phi-4-reasoning is an advanced transformer model featuring 14 billion parameters, specifically tailored for tackling intricate reasoning challenges, including mathematics, programming, algorithm development, and strategic planning. Through a meticulous process of supervised fine-tuning on select "teachable" prompts and reasoning examples created using o3-mini, it excels at generating thorough reasoning sequences that optimize computational resources during inference. By integrating outcome-driven reinforcement learning, Phi-4-reasoning is capable of producing extended reasoning paths. Its performance notably surpasses that of significantly larger open-weight models like DeepSeek-R1-Distill-Llama-70B and nears the capabilities of the comprehensive DeepSeek-R1 model across various reasoning applications. Designed for use in settings with limited computing power or high latency, Phi-4-reasoning is fine-tuned with synthetic data provided by DeepSeek-R1, ensuring it delivers precise and methodical problem-solving. This model's ability to handle complex tasks with efficiency makes it a valuable tool in numerous computational contexts.

Phi-4-mini-reasoning is a transformer-based language model with 3.8 billion parameters, specifically designed to excel in mathematical reasoning and methodical problem-solving within environments that have limited computational capacity or latency constraints. Its optimization stems from fine-tuning with synthetic data produced by the DeepSeek-R1 model, striking a balance between efficiency and sophisticated reasoning capabilities. With training that encompasses over one million varied math problems, ranging in complexity from middle school to Ph.D. level, Phi-4-mini-reasoning demonstrates superior performance to its base model in generating lengthy sentences across multiple assessments and outshines larger counterparts such as OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. Equipped with a 128K-token context window, it also facilitates function calling, which allows for seamless integration with various external tools and APIs. Moreover, Phi-4-mini-reasoning can be quantized through the Microsoft Olive or Apple MLX Framework, enabling its deployment on a variety of edge devices, including IoT gadgets, laptops, and smartphones. Its design not only enhances user accessibility but also expands the potential for innovative applications in mathematical fields.

The QwQ-32B model, created by Alibaba Cloud's Qwen team, represents a significant advancement in AI reasoning, aimed at improving problem-solving skills. Boasting 32 billion parameters, it rivals leading models such as DeepSeek's R1, which contains 671 billion parameters. This remarkable efficiency stems from its optimized use of parameters, enabling QwQ-32B to tackle complex tasks like mathematical reasoning, programming, and other problem-solving scenarios while consuming fewer resources. It can handle a context length of up to 32,000 tokens, making it adept at managing large volumes of input data. Notably, QwQ-32B is available through Alibaba's Qwen Chat service and is released under the Apache 2.0 license, which fosters collaboration and innovation among AI developers. With its cutting-edge features, QwQ-32B is poised to make a substantial impact in the field of artificial intelligence.

DeepSeek R2 is the highly awaited successor to DeepSeek R1, an innovative AI reasoning model that made waves when it was introduced in January 2025 by the Chinese startup DeepSeek. This new version builds on the remarkable achievements of R1, which significantly altered the AI landscape by providing cost-effective performance comparable to leading models like OpenAI’s o1. R2 is set to offer a substantial upgrade in capabilities, promising impressive speed and reasoning abilities akin to that of a human, particularly in challenging areas such as complex coding and advanced mathematics. By utilizing DeepSeek’s cutting-edge Mixture-of-Experts architecture along with optimized training techniques, R2 is designed to surpass the performance of its predecessor while keeping computational demands low. Additionally, there are expectations that this model may broaden its reasoning skills to accommodate languages beyond just English, potentially increasing its global usability. The anticipation surrounding R2 highlights the ongoing evolution of AI technology and its implications for various industries.

Discover the power of Math AI, an innovative Chrome extension tailored to streamline your homework experience by unlocking the picture math solver for all your mathematical inquiries. Whether you are tackling fundamental arithmetic, algebra, linear algebra, or physics, this extension is designed to simplify your understanding of equations and concepts efficiently. Key Features and Benefits: ● Picture Math Solver: Capture a photo of your math problem with Math AI, allowing you to solve it quickly and effortlessly. ● Education Modes: Select from various modes to receive customized help—detailed explanations for intricate problems, instant solutions for straightforward ones, or an engaging AI tutor for a more personalized learning experience. ● Broader Subject Support: Beyond mathematics, this tool extends its utility to subjects like physics, chemistry, biology, and many other academic tasks, making it a versatile study companion. ● 21 Language Options: With support for 21 different languages, Math AI is accessible to users around the globe, ensuring that everyone can benefit from its features. ● Convenience at Your Fingertips: This extension not only enhances your learning but also saves you valuable time, allowing you to focus on mastering the material.

Phi-4-reasoning-plus is an advanced reasoning model with 14 billion parameters, enhancing the capabilities of the original Phi-4-reasoning. It employs reinforcement learning for better inference efficiency, processing 1.5 times the number of tokens compared to its predecessor, which results in improved accuracy. Remarkably, this model performs better than both OpenAI's o1-mini and DeepSeek-R1 across various benchmarks, including challenging tasks in mathematical reasoning and advanced scientific inquiries. Notably, it even outperforms the larger DeepSeek-R1, which boasts 671 billion parameters, on the prestigious AIME 2025 assessment, a qualifier for the USA Math Olympiad. Furthermore, Phi-4-reasoning-plus is accessible on platforms like Azure AI Foundry and HuggingFace, making it easier for developers and researchers to leverage its capabilities. Its innovative design positions it as a top contender in the realm of reasoning models.

DeepSeek-V2 is a cutting-edge Mixture-of-Experts (MoE) language model developed by DeepSeek-AI, noted for its cost-effective training and high-efficiency inference features. It boasts an impressive total of 236 billion parameters, with only 21 billion active for each token, and is capable of handling a context length of up to 128K tokens. The model utilizes advanced architectures such as Multi-head Latent Attention (MLA) to optimize inference by minimizing the Key-Value (KV) cache and DeepSeekMoE to enable economical training through sparse computations. Compared to its predecessor, DeepSeek 67B, this model shows remarkable improvements, achieving a 42.5% reduction in training expenses, a 93.3% decrease in KV cache size, and a 5.76-fold increase in generation throughput. Trained on an extensive corpus of 8.1 trillion tokens, DeepSeek-V2 demonstrates exceptional capabilities in language comprehension, programming, and reasoning tasks, positioning it as one of the leading open-source models available today. Its innovative approach not only elevates its performance but also sets new benchmarks within the field of artificial intelligence.

Gemini 2.5 Pro represents a cutting-edge AI model tailored for tackling intricate tasks, showcasing superior reasoning and coding skills. It stands out in various benchmarks, particularly in mathematics, science, and programming, where it demonstrates remarkable efficacy in activities such as web application development and code conversion. Building on the Gemini 2.5 framework, this model boasts a context window of 1 million tokens, allowing it to efficiently manage extensive datasets from diverse origins, including text, images, and code libraries. Now accessible through Google AI Studio, Gemini 2.5 Pro is fine-tuned for more advanced applications, catering to expert users with enhanced capabilities for solving complex challenges. Furthermore, its design reflects a commitment to pushing the boundaries of AI's potential in real-world scenarios.

The o3-mini-high model developed by OpenAI enhances artificial intelligence reasoning capabilities by improving deep problem-solving skills in areas such as programming, mathematics, and intricate tasks. This model incorporates adaptive thinking time and allows users to select from various reasoning modes—low, medium, and high—to tailor performance to the difficulty of the task at hand. Impressively, it surpasses the o1 series by an impressive 200 Elo points on Codeforces, providing exceptional efficiency at a reduced cost while ensuring both speed and precision in its operations. As a notable member of the o3 family, this model not only expands the frontiers of AI problem-solving but also remains user-friendly, offering a complimentary tier alongside increased limits for Plus subscribers, thereby making advanced AI more widely accessible. Its innovative design positions it as a significant tool for users looking to tackle challenging problems with enhanced support and adaptability.

Grok 3 DeepSearch represents a sophisticated research agent and model aimed at enhancing the reasoning and problem-solving skills of artificial intelligence, emphasizing deep search methodologies and iterative reasoning processes. In contrast to conventional models that depend primarily on pre-existing knowledge, Grok 3 DeepSearch is equipped to navigate various pathways, evaluate hypotheses, and rectify inaccuracies in real-time, drawing from extensive datasets while engaging in logical, chain-of-thought reasoning. Its design is particularly suited for tasks necessitating critical analysis, including challenging mathematical equations, programming obstacles, and detailed academic explorations. As a state-of-the-art AI instrument, Grok 3 DeepSearch excels in delivering precise and comprehensive solutions through its distinctive deep search functionalities, rendering it valuable across both scientific and artistic disciplines. This innovative tool not only streamlines problem-solving but also fosters a deeper understanding of complex concepts.

QwQ-Max-Preview is a cutting-edge AI model based on the Qwen2.5-Max framework, specifically engineered to excel in areas such as complex reasoning, mathematical problem-solving, programming, and agent tasks. This preview showcases its enhanced capabilities across a variety of general-domain applications while demonstrating proficiency in managing intricate workflows. Anticipated to be officially released as open-source software under the Apache 2.0 license, QwQ-Max-Preview promises significant improvements and upgrades in its final iteration. Additionally, it contributes to the development of a more inclusive AI environment, as evidenced by the forthcoming introduction of the Qwen Chat application and streamlined model versions like QwQ-32B, which cater to developers interested in local deployment solutions. This initiative not only broadens accessibility but also encourages innovation within the AI community.

Upstage has unveiled Solar Pro 2, a cutting-edge large language model designed for frontier-scale applications, capable of managing intricate tasks and workflows in various sectors including finance, healthcare, and law. This model is built on a streamlined architecture with 31 billion parameters, ensuring exceptional multilingual capabilities, particularly in Korean, where it surpasses even larger models on key benchmarks such as Ko-MMLU, Hae-Rae, and Ko-IFEval, while maintaining strong performance in English and Japanese as well. In addition to its advanced language comprehension and generation abilities, Solar Pro 2 incorporates a sophisticated Reasoning Mode that significantly enhances the accuracy of multi-step tasks across a wide array of challenges, from general reasoning assessments (MMLU, MMLU-Pro, HumanEval) to intricate mathematics problems (Math500, AIME) and software engineering tasks (SWE-Bench Agentless), achieving problem-solving efficiency that rivals or even surpasses that of models with double the parameters. Furthermore, its enhanced tool-use capabilities allow the model to effectively engage with external APIs and data, broadening its applicability in real-world scenarios. This innovative design not only demonstrates exceptional versatility but also positions Solar Pro 2 as a formidable player in the evolving landscape of AI technologies.

Kimi K2 represents a cutting-edge series of open-source large language models utilizing a mixture-of-experts (MoE) architecture, with a staggering 1 trillion parameters in total and 32 billion activated parameters tailored for optimized task execution. Utilizing the Muon optimizer, it has been trained on a substantial dataset of over 15.5 trillion tokens, with its performance enhanced by MuonClip’s attention-logit clamping mechanism, resulting in remarkable capabilities in areas such as advanced knowledge comprehension, logical reasoning, mathematics, programming, and various agentic operations. Moonshot AI offers two distinct versions: Kimi-K2-Base, designed for research-level fine-tuning, and Kimi-K2-Instruct, which is pre-trained for immediate applications in chat and tool interactions, facilitating both customized development and seamless integration of agentic features. Comparative benchmarks indicate that Kimi K2 surpasses other leading open-source models and competes effectively with top proprietary systems, particularly excelling in coding and intricate task analysis. Furthermore, it boasts a generous context length of 128 K tokens, compatibility with tool-calling APIs, and support for industry-standard inference engines, making it a versatile option for various applications. The innovative design and features of Kimi K2 position it as a significant advancement in the field of artificial intelligence language processing.

EXAONE Deep represents a collection of advanced language models that are enhanced for reasoning, created by LG AI Research, and come in sizes of 2.4 billion, 7.8 billion, and 32 billion parameters. These models excel in a variety of reasoning challenges, particularly in areas such as mathematics and coding assessments. Significantly, the EXAONE Deep 2.4B model outshines other models of its size, while the 7.8B variant outperforms both open-weight models of similar dimensions and the proprietary reasoning model known as OpenAI o1-mini. Furthermore, the EXAONE Deep 32B model competes effectively with top-tier open-weight models in the field. The accompanying repository offers extensive documentation that includes performance assessments, quick-start guides for leveraging EXAONE Deep models with the Transformers library, detailed explanations of quantized EXAONE Deep weights formatted in AWQ and GGUF, as well as guidance on how to run these models locally through platforms like llama.cpp and Ollama. Additionally, this resource serves to enhance user understanding and accessibility to the capabilities of EXAONE Deep models.

K2 Think represents a groundbreaking open-source advanced reasoning model that has been developed in collaboration between the Institute of Foundation Models at MBZUAI and G42. Even with its relatively modest 32 billion parameters, K2 Think achieves performance that rivals that of leading models with significantly larger parameter counts. Its strength lies in mathematical reasoning, where it has secured top rankings on prestigious benchmarks such as AIME ’24/’25, HMMT ’25, and OMNI-Math-HARD. This model is part of a wider initiative of UAE-developed open models, which includes Jais (for Arabic), NANDA (for Hindi), and SHERKALA (for Kazakh), and it builds upon the groundwork established by the K2-65B, a fully reproducible open-source foundation model released in 2024. K2 Think is crafted to be open, efficient, and adaptable, featuring a web app interface that facilitates user exploration, and its innovative approach to parameter positioning marks a significant advancement in the realm of compact architectures for high-level AI reasoning. Additionally, its development highlights a commitment to enhancing access to state-of-the-art AI technologies in various languages and domains.

Qwen3-Coder is a versatile coding model that comes in various sizes, prominently featuring the 480B-parameter Mixture-of-Experts version with 35B active parameters, which naturally accommodates 256K-token contexts that can be extended to 1M tokens. This model achieves impressive performance that rivals Claude Sonnet 4, having undergone pre-training on 7.5 trillion tokens, with 70% of that being code, and utilizing synthetic data refined through Qwen2.5-Coder to enhance both coding skills and overall capabilities. Furthermore, the model benefits from post-training techniques that leverage extensive, execution-guided reinforcement learning, which facilitates the generation of diverse test cases across 20,000 parallel environments, thereby excelling in multi-turn software engineering tasks such as SWE-Bench Verified without needing test-time scaling. In addition to the model itself, the open-source Qwen Code CLI, derived from Gemini Code, empowers users to deploy Qwen3-Coder in dynamic workflows with tailored prompts and function calling protocols, while also offering smooth integration with Node.js, OpenAI SDKs, and environment variables. This comprehensive ecosystem supports developers in optimizing their coding projects effectively and efficiently.

Marco-o1 represents a state-of-the-art AI framework specifically designed for superior natural language understanding and immediate problem resolution. It is meticulously crafted to provide accurate and contextually appropriate replies, merging profound language insight with an optimized framework for enhanced speed and effectiveness. This model thrives in numerous settings, such as interactive dialogue systems, content generation, technical assistance, and complex decision-making processes, effortlessly adjusting to various user requirements. Prioritizing seamless, user-friendly experiences, dependability, and adherence to ethical AI standards, Marco-o1 emerges as a leading-edge resource for both individuals and enterprises in pursuit of intelligent, flexible, and scalable AI solutions. Additionally, the MCTS technique facilitates the investigation of numerous reasoning pathways by utilizing confidence scores based on the softmax-adjusted log probabilities of the top-k alternative tokens, steering the model towards the most effective resolutions while maintaining a high level of precision. Such capabilities not only enhance the overall performance of the model but also significantly improve user satisfaction and engagement.

Gemini Advanced represents a state-of-the-art AI model that excels in natural language comprehension, generation, and problem-solving across a variety of fields. With its innovative neural architecture, it provides remarkable accuracy, sophisticated contextual understanding, and profound reasoning abilities. This advanced system is purpose-built to tackle intricate and layered tasks, which include generating comprehensive technical documentation, coding, performing exhaustive data analysis, and delivering strategic perspectives. Its flexibility and ability to scale make it an invaluable resource for both individual practitioners and large organizations. By establishing a new benchmark for intelligence, creativity, and dependability in AI-driven solutions, Gemini Advanced is set to transform various industries. Additionally, users will gain access to Gemini in platforms like Gmail and Docs, along with 2 TB of storage and other perks from Google One, enhancing overall productivity. Furthermore, Gemini Advanced facilitates access to Gemini with Deep Research, enabling users to engage in thorough and instantaneous research on virtually any topic.

The overwhelming amount of information available poses a significant challenge to advancements in science. With the rapid expansion of scientific literature and data, pinpointing valuable insights within this vast sea of information has become increasingly difficult. Nowadays, people rely on search engines to access scientific knowledge, yet these tools alone cannot effectively categorize and organize this complex information. Galactica is an advanced language model designed to capture, synthesize, and analyze scientific knowledge. It is trained on a diverse array of scientific materials, including research papers, reference texts, knowledge databases, and other relevant resources. In various scientific tasks, Galactica demonstrates superior performance compared to existing models. For instance, on technical knowledge assessments involving LaTeX equations, Galactica achieves a score of 68.2%, significantly higher than the 49.0% of the latest GPT-3 model. Furthermore, Galactica excels in reasoning tasks, outperforming Chinchilla in mathematical MMLU with scores of 41.3% to 35.7%, and surpassing PaLM 540B in MATH with a notable 20.4% compared to 8.8%. This indicates that Galactica not only enhances accessibility to scientific information but also improves our ability to reason through complex scientific queries.

Yi-Lightning, a product of 01.AI and spearheaded by Kai-Fu Lee, marks a significant leap forward in the realm of large language models, emphasizing both performance excellence and cost-effectiveness. With the ability to process a context length of up to 16K tokens, it offers an attractive pricing model of $0.14 per million tokens for both inputs and outputs, making it highly competitive in the market. The model employs an improved Mixture-of-Experts (MoE) framework, featuring detailed expert segmentation and sophisticated routing techniques that enhance its training and inference efficiency. Yi-Lightning has distinguished itself across multiple fields, achieving top distinctions in areas such as Chinese language processing, mathematics, coding tasks, and challenging prompts on chatbot platforms, where it ranked 6th overall and 9th in style control. Its creation involved an extensive combination of pre-training, targeted fine-tuning, and reinforcement learning derived from human feedback, which not only enhances its performance but also prioritizes user safety. Furthermore, the model's design includes significant advancements in optimizing both memory consumption and inference speed, positioning it as a formidable contender in its field.

Open R1 is a collaborative, open-source effort focused on mimicking the sophisticated AI functionalities of DeepSeek-R1 using clear and open methods. Users have the opportunity to explore the Open R1 AI model or engage in a free online chat with DeepSeek R1 via the Open R1 platform. This initiative presents a thorough execution of DeepSeek-R1's reasoning-optimized training framework, featuring resources for GRPO training, SFT fine-tuning, and the creation of synthetic data, all available under the MIT license. Although the original training dataset is still proprietary, Open R1 equips users with a complete suite of tools to create and enhance their own AI models, allowing for greater customization and experimentation in the field of artificial intelligence.

DeepSeek Coder is an innovative software solution poised to transform the realm of data analysis and programming. By harnessing state-of-the-art machine learning techniques and natural language processing, it allows users to effortlessly incorporate data querying, analysis, and visualization into their daily tasks. The user-friendly interface caters to both beginners and seasoned developers, making the writing, testing, and optimization of code a straightforward process. Among its impressive features are real-time syntax validation, smart code suggestions, and thorough debugging capabilities, all aimed at enhancing productivity in coding. Furthermore, DeepSeek Coder’s proficiency in deciphering intricate data sets enables users to extract valuable insights and develop advanced data-centric applications with confidence. Ultimately, its combination of powerful tools and ease of use positions DeepSeek Coder as an essential asset for anyone engaged in data-driven projects.

DeepSeek V3.1 stands as a revolutionary open-weight large language model, boasting an impressive 685-billion parameters and an expansive 128,000-token context window, which allows it to analyze extensive documents akin to 400-page books in a single invocation. This model offers integrated functionalities for chatting, reasoning, and code creation, all within a cohesive hybrid architecture that harmonizes these diverse capabilities. Furthermore, V3.1 accommodates multiple tensor formats, granting developers the versatility to enhance performance across various hardware setups. Preliminary benchmark evaluations reveal strong results, including a remarkable 71.6% on the Aider coding benchmark, positioning it competitively with or even superior to systems such as Claude Opus 4, while achieving this at a significantly reduced cost. Released under an open-source license on Hugging Face with little publicity, DeepSeek V3.1 is set to revolutionize access to advanced AI technologies, potentially disrupting the landscape dominated by conventional proprietary models. Its innovative features and cost-effectiveness may attract a wide range of developers eager to leverage cutting-edge AI in their projects.

Tülu 3 is a cutting-edge language model created by the Allen Institute for AI (Ai2) that aims to improve proficiency in fields like knowledge, reasoning, mathematics, coding, and safety. It is based on the Llama 3 Base and undergoes a detailed four-stage post-training regimen: careful prompt curation and synthesis, supervised fine-tuning on a wide array of prompts and completions, preference tuning utilizing both off- and on-policy data, and a unique reinforcement learning strategy that enhances targeted skills through measurable rewards. Notably, this open-source model sets itself apart by ensuring complete transparency, offering access to its training data, code, and evaluation tools, thus bridging the performance divide between open and proprietary fine-tuning techniques. Performance assessments reveal that Tülu 3 surpasses other models with comparable sizes, like Llama 3.1-Instruct and Qwen2.5-Instruct, across an array of benchmarks, highlighting its effectiveness. The continuous development of Tülu 3 signifies the commitment to advancing AI capabilities while promoting an open and accessible approach to technology.

MathGPT is an advanced AI math assistant designed to help students solve problems in subjects like algebra, geometry, calculus, and statistics. Users can upload images or PDFs of math questions, and the platform provides detailed, step-by-step solutions to enhance understanding and learning. Acting as a virtual math tutor, MathGPT simplifies complex concepts and helps students build confidence in their abilities. With over 2 million users, it is a free, reliable, and user-friendly resource for improving math skills and tackling challenging problems.

Qwen3 is a state-of-the-art large language model designed to revolutionize the way we interact with AI. Featuring both thinking and non-thinking modes, Qwen3 allows users to customize its response style, ensuring optimal performance for both complex reasoning tasks and quick inquiries. With the ability to support 119 languages, the model is suitable for international projects. The model's hybrid training approach, which involves over 36 trillion tokens, ensures accuracy across a variety of disciplines, from coding to STEM problems. Its integration with platforms such as Hugging Face, ModelScope, and Kaggle allows for easy adoption in both research and production environments. By enhancing multilingual support and incorporating advanced AI techniques, Qwen3 is designed to push the boundaries of AI-driven applications.

DeepSeek has launched DeepSeek-V3.1-Terminus, an upgrade to the V3.1 architecture that integrates user suggestions to enhance output stability, consistency, and overall agent performance. This new version significantly decreases the occurrences of mixed Chinese and English characters as well as unintended distortions, leading to a cleaner and more uniform language generation experience. Additionally, the update revamps both the code agent and search agent subsystems to deliver improved and more dependable performance across various benchmarks. DeepSeek-V3.1-Terminus is available as an open-source model, with its weights accessible on Hugging Face, making it easier for the community to leverage its capabilities. The structure of the model remains consistent with DeepSeek-V3, ensuring it is compatible with existing deployment strategies, and updated inference demonstrations are provided for users to explore. Notably, the model operates at a substantial scale of 685B parameters and supports multiple tensor formats, including FP8, BF16, and F32, providing adaptability in different environments. This flexibility allows developers to choose the most suitable format based on their specific needs and resource constraints.

The days of puzzling over complex problems are over. With Sizzle, you can simplify any challenge into manageable steps that are easy to understand. You can ask for definitions and engage in a straightforward dialogue with Sizzle, making the learning process incredibly accessible. Whether you're tackling math questions or delving into chemistry, whether it's a multiple-choice quiz or a challenging word problem, Sizzle is ready to assist you. This tool serves as your reliable partner when faced with word problems, making the process smoother and more efficient. Just snap a photo of the issue at hand, and Sizzle will guide you through the solution step-by-step. What distinguishes Sizzle is its innovative educational method; instead of merely providing solutions, it nurtures your problem-solving skills and deepens your comprehension of fundamental principles. Simply take a picture of any question you are grappling with, and Sizzle will walk you through to the solution, ensuring you grasp each step along the way. Its strengths lie particularly in subjects like physics, chemistry, and biology, along with algebra and calculus, making it a versatile learning tool. With Sizzle by your side, you can transform your academic challenges into opportunities for growth and understanding.

Qwen2.5-VL-32B represents an advanced AI model specifically crafted for multimodal endeavors, showcasing exceptional skills in reasoning related to both text and images. This iteration enhances the previous Qwen2.5-VL series, resulting in responses that are not only of higher quality but also more aligned with human-like formatting. The model demonstrates remarkable proficiency in mathematical reasoning, nuanced image comprehension, and intricate multi-step reasoning challenges, such as those encountered in benchmarks like MathVista and MMMU. Its performance has been validated through comparisons with competing models, often surpassing even the larger Qwen2-VL-72B in specific tasks. Furthermore, with its refined capabilities in image analysis and visual logic deduction, Qwen2.5-VL-32B offers thorough and precise evaluations of visual content, enabling it to generate insightful responses from complex visual stimuli. This model has been meticulously optimized for both textual and visual tasks, making it exceptionally well-suited for scenarios that demand advanced reasoning and understanding across various forms of media, thus expanding its potential applications even further.

- Vision : Our vision is that interactive and personalized learning will be available to everyone at any time, and we will disseminate resources for education inequity. Our product has two modes: Learner Mode and Assistant Mode. Helper Mode offers step-by-step answers with an accuracy of 90%+ and allows users select steps for further explanations. It helps students double-check solutions and solve difficult math problems. We also offer Learner Mode which breaks down large problems into smaller problems and gives hints with knowledge points to guide users to solve problems on their own. It helps students catch up on hard knowledge points and correct concepts in a Socratic way. - Technology: We use a large language model, and knowledge graph. 90%+ accuracy - Price: 100% Free, no freemium features - Team: We have the support of top tech companies such as Google, Amazon, and top schools like Stanford and UW.

Smaug-72B is a formidable open-source large language model (LLM) distinguished by several prominent features: Exceptional Performance: It currently ranks first on the Hugging Face Open LLM leaderboard, outperforming models such as GPT-3.5 in multiple evaluations, demonstrating its ability to comprehend, react to, and generate text that closely resembles human writing. Open Source Availability: In contrast to many high-end LLMs, Smaug-72B is accessible to everyone for use and modification, which encourages cooperation and innovation within the AI ecosystem. Emphasis on Reasoning and Mathematics: This model excels particularly in reasoning and mathematical challenges, a capability attributed to specialized fine-tuning methods developed by its creators, Abacus AI. Derived from Qwen-72B: It is essentially a refined version of another robust LLM, Qwen-72B, which was launched by Alibaba, thereby enhancing its overall performance. In summary, Smaug-72B marks a notable advancement in the realm of open-source artificial intelligence, making it a valuable resource for developers and researchers alike. Its unique strengths not only elevate its status but also contribute to the ongoing evolution of AI technology.

QVQ-Max is an advanced visual reasoning platform that enables AI to process images and videos for solving diverse problems, from academic tasks to creative projects. With its ability to perform detailed observation, such as identifying objects and reading charts, along with deep reasoning to analyze content, QVQ-Max can assist in solving complex mathematical equations or predicting actions in video clips. The model's flexibility extends to creative endeavors, helping users refine sketches or develop scripts for videos. Although still in early development, QVQ-Max has already showcased its potential in a wide range of applications, including data analysis, education, and lifestyle assistance.

The NVIDIA Llama Nemotron family comprises a series of sophisticated language models that are fine-tuned for complex reasoning and a wide array of agentic AI applications. These models shine in areas such as advanced scientific reasoning, complex mathematics, coding, following instructions, and executing tool calls. They are designed for versatility, making them suitable for deployment on various platforms, including data centers and personal computers, and feature the ability to switch reasoning capabilities on or off, which helps to lower inference costs during less demanding tasks. The Llama Nemotron series consists of models specifically designed to meet different deployment requirements. Leveraging the foundation of Llama models and enhanced through NVIDIA's post-training techniques, these models boast a notable accuracy improvement of up to 20% compared to their base counterparts while also achieving inference speeds that can be up to five times faster than other leading open reasoning models. This remarkable efficiency allows for the management of more intricate reasoning challenges, boosts decision-making processes, and significantly lowers operational expenses for businesses. Consequently, the Llama Nemotron models represent a significant advancement in the field of AI, particularly for organizations seeking to integrate cutting-edge reasoning capabilities into their systems.

Claude Sonnet 3.7, a state-of-the-art AI model by Anthropic, is designed for versatility, offering users the option to switch between quick, efficient responses and deeper, more reflective answers. This dynamic model shines in complex problem-solving scenarios, where high-level reasoning and nuanced understanding are crucial. By allowing Claude to pause for self-reflection before answering, Sonnet 3.7 excels in tasks that demand deep analysis, such as coding, natural language processing, and critical thinking applications. Its flexibility makes it an invaluable tool for professionals and organizations looking for an adaptable AI that delivers both speed and thoughtful insights.

By leveraging our advanced algorithms, you can enhance your model's complexity to more accurately reflect real-world scenarios while ensuring you can solve it within the given timeframe. Seamlessly incorporate Gurobi into your existing applications using familiar programming languages. Our user-friendly programming interfaces are crafted to be efficient and contemporary, helping you reduce the learning curve while boosting your overall productivity. The Python API we offer includes advanced modeling features that simplify the creation of optimization models, making the process more straightforward. You have the option to utilize Anaconda Python distributions, which come with pre-compiled libraries to facilitate application development, or choose Spyder for visual development and Jupyter for a notebook-style development experience, ensuring you have versatile tools at your disposal. Additionally, Gurobi's robust capabilities empower users to tackle increasingly complex optimization challenges with confidence.

The Gemini 1.5 Pro AI model represents a pinnacle in language modeling, engineered to produce remarkably precise, context-sensitive, and human-like replies suitable for a wide range of uses. Its innovative neural framework allows it to excel in tasks involving natural language comprehension, generation, and reasoning. This model has been meticulously fine-tuned for adaptability, making it capable of handling diverse activities such as content creation, coding, data analysis, and intricate problem-solving. Its sophisticated algorithms provide a deep understanding of language, allowing for smooth adjustments to various domains and conversational tones. Prioritizing both scalability and efficiency, the Gemini 1.5 Pro is designed to cater to both small applications and large-scale enterprise deployments, establishing itself as an invaluable asset for driving productivity and fostering innovation. Moreover, its ability to learn from user interactions enhances its performance, making it even more effective in real-world scenarios.

NerdPal is an innovative math solver that enables users to either snap a photo or manually input a math problem to receive immediate answers accompanied by detailed, step-by-step guidance on the solution process. This AI-driven math tutor is designed to assist students in comprehending various mathematical concepts, offering explanations that clarify how to tackle problems ranging from basic arithmetic to advanced calculus. Users can choose their preferred method for solving problems, whether through image capture or a specialized math keyboard, ensuring a flexible experience tailored to individual needs. Additionally, the app allows for the tracking of previously solved problems, the creation of personal user accounts, and the submission of feedback directly within the platform. Recognizing the challenges many face with mathematics, NerdPal aims to provide essential support and tools to make learning more accessible and efficient. By utilizing NerdPal, students can significantly reduce the time spent grappling with complex concepts, as it also offers access to instructional videos that enhance understanding. With its user-friendly features and comprehensive assistance, NerdPal transforms the often tedious process of studying math into a more engaging and effective experience.