Alternatives to Qwen2.5-Max

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.

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.

ERNIE X1 represents a sophisticated conversational AI model created by Baidu within their ERNIE (Enhanced Representation through Knowledge Integration) lineup. This iteration surpasses earlier versions by enhancing its efficiency in comprehending and producing responses that closely resemble human interaction. Utilizing state-of-the-art machine learning methodologies, ERNIE X1 adeptly manages intricate inquiries and expands its capabilities to include not only text processing but also image generation and multimodal communication. Its applications are widespread in the realm of natural language processing, including chatbots, virtual assistants, and automation in enterprises, leading to notable advancements in precision, contextual awareness, and overall response excellence. The versatility of ERNIE X1 makes it an invaluable tool in various industries, reflecting the continuous evolution of AI technology.

ERNIE 4.5 represents a state-of-the-art conversational AI platform crafted by Baidu, utilizing cutting-edge natural language processing (NLP) models to facilitate highly advanced, human-like communication. This platform is an integral component of Baidu's ERNIE (Enhanced Representation through Knowledge Integration) lineup, which incorporates multimodal features that encompass text, imagery, and voice interactions. With ERNIE 4.5, the AI models' capacity to comprehend intricate contexts is significantly improved, enabling them to provide more precise and nuanced answers. This makes the platform ideal for a wide range of applications, including but not limited to customer support, virtual assistant services, content generation, and automation in corporate environments. Furthermore, the integration of various modes of communication ensures that users can engage with the AI in the manner most convenient for them, enhancing the overall user experience.

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.

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.

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.

GPT-4.5 represents a significant advancement in AI technology, building on previous models by expanding its unsupervised learning techniques, refining its reasoning skills, and enhancing its collaborative features. This model is crafted to better comprehend human intentions and engage in more natural and intuitive interactions, resulting in greater accuracy and reduced hallucination occurrences across various subjects. Its sophisticated functions allow for the creation of imaginative and thought-provoking content, facilitate the resolution of intricate challenges, and provide support in various fields such as writing, design, and even space exploration. Furthermore, the model's enhanced ability to interact with humans paves the way for practical uses, ensuring that it is both more accessible and dependable for businesses and developers alike. By continually evolving, GPT-4.5 sets a new standard for how AI can assist in diverse applications and industries.

OpenAI's advanced research tool utilizes artificial intelligence to independently carry out intricate, multi-step research tasks across a range of fields, including science, programming, and mathematics. By processing user inputs—such as questions, textual documents, images, PDFs, or spreadsheets—the tool creates a detailed research strategy, collects pertinent information, and provides thorough answers in just a few minutes. Additionally, it offers summaries of the research process with citations, enabling users to verify the sources of the information. Although this tool greatly enhances research efficiency, it can sometimes yield errors or have difficulty distinguishing between credible sources and false information. Currently, it is accessible to ChatGPT Pro users, marking a significant advancement in AI-assisted knowledge exploration, and further enhancements for accuracy and response speed are in the pipeline. This ongoing development reflects a commitment to refining the tool's capabilities and ensuring users receive the most reliable information.

Tencent's Hunyuan-TurboS represents a cutting-edge AI model crafted to deliver swift answers and exceptional capabilities across multiple fields, including knowledge acquisition, mathematical reasoning, and creative endeavors. Departing from earlier models that relied on "slow thinking," this innovative system significantly boosts response rates, achieving a twofold increase in word output speed and cutting down first-word latency by 44%. With its state-of-the-art architecture, Hunyuan-TurboS not only enhances performance but also reduces deployment expenses. The model skillfully integrates fast thinking—prompt, intuition-driven responses—with slow thinking—methodical logical analysis—ensuring timely and precise solutions in a wide array of situations. Its remarkable abilities are showcased in various benchmarks, positioning it competitively alongside other top AI models such as GPT-4 and DeepSeek V3, thus marking a significant advancement in AI performance. As a result, Hunyuan-TurboS is poised to redefine expectations in the realm of artificial intelligence applications.

Qwen-7B is the 7-billion parameter iteration of Alibaba Cloud's Qwen language model series, also known as Tongyi Qianwen. This large language model utilizes a Transformer architecture and has been pretrained on an extensive dataset comprising web texts, books, code, and more. Furthermore, we introduced Qwen-7B-Chat, an AI assistant that builds upon the pretrained Qwen-7B model and incorporates advanced alignment techniques. The Qwen-7B series boasts several notable features: It has been trained on a premium dataset, with over 2.2 trillion tokens sourced from a self-assembled collection of high-quality texts and codes across various domains, encompassing both general and specialized knowledge. Additionally, our model demonstrates exceptional performance, surpassing competitors of similar size on numerous benchmark datasets that assess capabilities in natural language understanding, mathematics, and coding tasks. This positions Qwen-7B as a leading choice in the realm of AI language models. Overall, its sophisticated training and robust design contribute to its impressive versatility and effectiveness.

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.

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.

Qwen2 represents a collection of extensive language models crafted by the Qwen team at Alibaba Cloud. This series encompasses a variety of models, including base and instruction-tuned versions, with parameters varying from 0.5 billion to an impressive 72 billion, showcasing both dense configurations and a Mixture-of-Experts approach. The Qwen2 series aims to outperform many earlier open-weight models, including its predecessor Qwen1.5, while also striving to hold its own against proprietary models across numerous benchmarks in areas such as language comprehension, generation, multilingual functionality, programming, mathematics, and logical reasoning. Furthermore, this innovative series is poised to make a significant impact in the field of artificial intelligence, offering enhanced capabilities for a diverse range of applications.

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.

CodeQwen serves as the coding counterpart to Qwen, which is a series of large language models created by the Qwen team at Alibaba Cloud. Built on a transformer architecture that functions solely as a decoder, this model has undergone extensive pre-training using a vast dataset of code. It showcases robust code generation abilities and demonstrates impressive results across various benchmarking tests. With the capacity to comprehend and generate long contexts of up to 64,000 tokens, CodeQwen accommodates 92 programming languages and excels in tasks such as text-to-SQL queries and debugging. Engaging with CodeQwen is straightforward—you can initiate a conversation with just a few lines of code utilizing transformers. The foundation of this interaction relies on constructing the tokenizer and model using pre-existing methods, employing the generate function to facilitate dialogue guided by the chat template provided by the tokenizer. In alignment with our established practices, we implement the ChatML template tailored for chat models. This model adeptly completes code snippets based on the prompts it receives, delivering responses without the need for any further formatting adjustments, thereby enhancing the user experience. The seamless integration of these elements underscores the efficiency and versatility of CodeQwen in handling diverse coding tasks.

Llama 4 Behemoth, with 288 billion active parameters, is Meta's flagship AI model, setting new standards for multimodal performance. Outpacing its predecessors like GPT-4.5 and Claude Sonnet 3.7, it leads the field in STEM benchmarks, offering cutting-edge results in tasks such as problem-solving and reasoning. Designed as the teacher model for the Llama 4 series, Behemoth drives significant improvements in model quality and efficiency through distillation. Although still in development, Llama 4 Behemoth is shaping the future of AI with its unparalleled intelligence, particularly in math, image, and multilingual tasks.

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.

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.

The MAI-1 Preview marks the debut of Microsoft AI's fully in-house developed foundation model, utilizing a mixture-of-experts architecture for streamlined performance. This model has undergone extensive training on around 15,000 NVIDIA H100 GPUs, equipping it to adeptly follow user instructions and produce relevant text responses for common inquiries, thus illustrating a prototype for future Copilot functionalities. Currently accessible for public testing on LMArena, MAI-1 Preview provides an initial look at the platform's direction, with plans to introduce select text-driven applications in Copilot over the next few weeks aimed at collecting user insights and enhancing its capabilities. Microsoft emphasizes its commitment to integrating its proprietary models, collaborations with partners, and advancements from the open-source sector to dynamically enhance user experiences through millions of distinct interactions every day. This innovative approach illustrates Microsoft's dedication to continuously evolving its AI offerings.

Opus, recognized as our most advanced model, surpasses its competitors in numerous widely-used evaluation benchmarks for artificial intelligence, including assessments of undergraduate expert knowledge (MMLU), graduate-level reasoning (GPQA), fundamental mathematics (GSM8K), and others. Its performance approaches human-like comprehension and fluency in handling intricate tasks, positioning it at the forefront of general intelligence advancements. Furthermore, all Claude 3 models demonstrate enhanced abilities in analysis and prediction, sophisticated content creation, programming code generation, and engaging in conversations in various non-English languages such as Spanish, Japanese, and French, showcasing their versatility in communication.

Introducing the next iteration of our open-source large language model, this version features model weights along with initial code for the pretrained and fine-tuned Llama language models, which span from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been developed using an impressive 2 trillion tokens and offer double the context length compared to their predecessor, Llama 1. Furthermore, the fine-tuned models have been enhanced through the analysis of over 1 million human annotations. Llama 2 demonstrates superior performance against various other open-source language models across multiple external benchmarks, excelling in areas such as reasoning, coding capabilities, proficiency, and knowledge assessments. For its training, Llama 2 utilized publicly accessible online data sources, while the fine-tuned variant, Llama-2-chat, incorporates publicly available instruction datasets along with the aforementioned extensive human annotations. Our initiative enjoys strong support from a diverse array of global stakeholders who are enthusiastic about our open approach to AI, including companies that have provided valuable early feedback and are eager to collaborate using Llama 2. The excitement surrounding Llama 2 signifies a pivotal shift in how AI can be developed and utilized collectively.

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.

Qwen3-Max represents Alibaba's cutting-edge large language model, featuring a staggering trillion parameters aimed at enhancing capabilities in tasks that require agency, coding, reasoning, and managing lengthy contexts. This model is an evolution of the Qwen3 series, leveraging advancements in architecture, training methods, and inference techniques; it integrates both thinker and non-thinker modes, incorporates a unique “thinking budget” system, and allows for dynamic mode adjustments based on task complexity. Capable of handling exceptionally lengthy inputs, processing hundreds of thousands of tokens, it also supports tool invocation and demonstrates impressive results across various benchmarks, including coding, multi-step reasoning, and agent evaluations like Tau2-Bench. While the initial version prioritizes instruction adherence in a non-thinking mode, Alibaba is set to introduce reasoning functionalities that will facilitate autonomous agent operations in the future. In addition to its existing multilingual capabilities and extensive training on trillions of tokens, Qwen3-Max is accessible through API interfaces that align seamlessly with OpenAI-style functionalities, ensuring broad usability across applications. This comprehensive framework positions Qwen3-Max as a formidable player in the realm of advanced artificial intelligence language models.

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.

Hippocratic AI represents a cutting-edge advancement in artificial intelligence, surpassing GPT-4 on 105 out of 114 healthcare-related exams and certifications. Notably, it exceeded GPT-4's performance by at least five percent on 74 of these certifications, and on 43 of them, the margin was ten percent or greater. Unlike most language models that rely on a broad range of internet sources—which can sometimes include inaccurate information—Hippocratic AI is committed to sourcing evidence-based healthcare content through legal means. To ensure the model's effectiveness and safety, we are implementing a specialized Reinforcement Learning with Human Feedback process, involving healthcare professionals in training and validating the model before its release. This meticulous approach, dubbed RLHF-HP, guarantees that Hippocratic AI will only be launched after it receives the approval of a significant number of licensed healthcare experts, prioritizing patient safety and accuracy in its applications. The dedication to rigorous validation sets Hippocratic AI apart in the landscape of AI healthcare solutions.

Qwen LLM represents a collection of advanced large language models created by Alibaba Cloud's Damo Academy. These models leverage an extensive dataset comprising text and code, enabling them to produce human-like text, facilitate language translation, craft various forms of creative content, and provide informative answers to queries. Key attributes of Qwen LLMs include: A range of sizes: The Qwen series features models with parameters varying from 1.8 billion to 72 billion, catering to diverse performance requirements and applications. Open source availability: Certain versions of Qwen are open-source, allowing users to access and modify the underlying code as needed. Multilingual capabilities: Qwen is equipped to comprehend and translate several languages, including English, Chinese, and French. Versatile functionalities: In addition to language generation and translation, Qwen models excel in tasks such as answering questions, summarizing texts, and generating code, making them highly adaptable tools for various applications. Overall, the Qwen LLM family stands out for its extensive capabilities and flexibility in meeting user needs.

Chinchilla is an advanced language model that operates with a compute budget comparable to Gopher while having 70 billion parameters and utilizing four times the amount of data. This model consistently and significantly surpasses Gopher (280 billion parameters), as well as GPT-3 (175 billion), Jurassic-1 (178 billion), and Megatron-Turing NLG (530 billion), across a wide variety of evaluation tasks. Additionally, Chinchilla's design allows it to use significantly less computational power during the fine-tuning and inference processes, which greatly enhances its applicability in real-world scenarios. Notably, Chinchilla achieves a remarkable average accuracy of 67.5% on the MMLU benchmark, marking over a 7% enhancement compared to Gopher, showcasing its superior performance in the field. This impressive capability positions Chinchilla as a leading contender in the realm of language models.

Qwen2-VL represents the most advanced iteration of vision-language models within the Qwen family, building upon the foundation established by Qwen-VL. This enhanced model showcases remarkable capabilities, including: Achieving cutting-edge performance in interpreting images of diverse resolutions and aspect ratios, with Qwen2-VL excelling in visual comprehension tasks such as MathVista, DocVQA, RealWorldQA, and MTVQA, among others. Processing videos exceeding 20 minutes in length, enabling high-quality video question answering, engaging dialogues, and content creation. Functioning as an intelligent agent capable of managing devices like smartphones and robots, Qwen2-VL utilizes its sophisticated reasoning and decision-making skills to perform automated tasks based on visual cues and textual commands. Providing multilingual support to accommodate a global audience, Qwen2-VL can now interpret text in multiple languages found within images, extending its usability and accessibility to users from various linguistic backgrounds. This wide-ranging capability positions Qwen2-VL as a versatile tool for numerous applications across different fields.

Pre-trained language models have made significant strides, achieving top-tier performance across multiple Natural Language Processing (NLP) applications. The impressive capabilities of GPT-3 highlight how increasing the scale of these models can unlock their vast potential. Recently, a comprehensive framework known as ERNIE 3.0 was introduced to pre-train large-scale models enriched with knowledge, culminating in a model boasting 10 billion parameters. This iteration of ERNIE 3.0 has surpassed the performance of existing leading models in a variety of NLP tasks. To further assess the effects of scaling, we have developed an even larger model called ERNIE 3.0 Titan, which consists of up to 260 billion parameters and is built on the PaddlePaddle platform. Additionally, we have implemented a self-supervised adversarial loss alongside a controllable language modeling loss, enabling ERNIE 3.0 Titan to produce texts that are both reliable and modifiable, thus pushing the boundaries of what these models can achieve. This approach not only enhances the model's capabilities but also opens new avenues for research in text generation and control.

Utilize sophisticated coding and language models across a diverse range of applications. Harness the power of expansive generative AI models that possess an intricate grasp of both language and code, paving the way for enhanced reasoning and comprehension skills essential for developing innovative applications. These advanced models can be applied to multiple scenarios, including writing support, automatic code creation, and data reasoning. Moreover, ensure responsible AI practices by implementing measures to detect and mitigate potential misuse, all while benefiting from enterprise-level security features offered by Azure. With access to generative models pretrained on vast datasets comprising trillions of words, you can explore new possibilities in language processing, code analysis, reasoning, inferencing, and comprehension. Further personalize these generative models by using labeled datasets tailored to your unique needs through an easy-to-use REST API. Additionally, you can optimize your model's performance by fine-tuning hyperparameters for improved output accuracy. The few-shot learning functionality allows you to provide sample inputs to the API, resulting in more pertinent and context-aware outcomes. This flexibility enhances your ability to meet specific application demands effectively.

GLM-4.6 builds upon the foundations laid by its predecessor, showcasing enhanced reasoning, coding, and agent capabilities, resulting in notable advancements in inferential accuracy, improved tool usage during reasoning tasks, and a more seamless integration within agent frameworks. In comprehensive benchmark evaluations that assess reasoning, coding, and agent performance, GLM-4.6 surpasses GLM-4.5 and competes robustly against other models like DeepSeek-V3.2-Exp and Claude Sonnet 4, although it still lags behind Claude Sonnet 4.5 in terms of coding capabilities. Furthermore, when subjected to practical tests utilizing an extensive “CC-Bench” suite that includes tasks in front-end development, tool creation, data analysis, and algorithmic challenges, GLM-4.6 outperforms GLM-4.5 while nearing parity with Claude Sonnet 4, achieving victory in approximately 48.6% of direct comparisons and demonstrating around 15% improved token efficiency. This latest model is accessible through the Z.ai API, providing developers the flexibility to implement it as either an LLM backend or as the core of an agent within the platform's API ecosystem. In addition, its advancements could significantly enhance productivity in various application domains, making it an attractive option for developers looking to leverage cutting-edge AI technology.

Baichuan-13B is an advanced large-scale language model developed by Baichuan Intelligent, featuring 13 billion parameters and available for open-source and commercial use, building upon its predecessor Baichuan-7B. This model has set new records for performance among similarly sized models on esteemed Chinese and English evaluation metrics. The release includes two distinct pre-training variations: Baichuan-13B-Base and Baichuan-13B-Chat. By significantly increasing the parameter count to 13 billion, Baichuan-13B enhances its capabilities, training on 1.4 trillion tokens from a high-quality dataset, which surpasses LLaMA-13B's training data by 40%. It currently holds the distinction of being the model with the most extensive training data in the 13B category, providing robust support for both Chinese and English languages, utilizing ALiBi positional encoding, and accommodating a context window of 4096 tokens for improved comprehension and generation. This makes it a powerful tool for a variety of applications in natural language processing.

Stability AI, along with its CarperAI lab, is excited to unveil Stable Beluga 1 and its advanced successor, Stable Beluga 2, previously known as FreeWilly, both of which are robust new Large Language Models (LLMs) available for public use. These models exhibit remarkable reasoning capabilities across a wide range of benchmarks, showcasing their versatility and strength. Stable Beluga 1 is built on the original LLaMA 65B foundation model and has undergone meticulous fine-tuning with a novel synthetically-generated dataset utilizing Supervised Fine-Tune (SFT) in the conventional Alpaca format. In a similar vein, Stable Beluga 2 utilizes the LLaMA 2 70B foundation model, pushing the boundaries of performance in the industry. Their development marks a significant step forward in the evolution of open access AI technologies.

Falcon-40B is a causal decoder-only model consisting of 40 billion parameters, developed by TII and trained on 1 trillion tokens from RefinedWeb, supplemented with carefully selected datasets. It is distributed under the Apache 2.0 license. Why should you consider using Falcon-40B? This model stands out as the leading open-source option available, surpassing competitors like LLaMA, StableLM, RedPajama, and MPT, as evidenced by its ranking on the OpenLLM Leaderboard. Its design is specifically tailored for efficient inference, incorporating features such as FlashAttention and multiquery capabilities. Moreover, it is offered under a flexible Apache 2.0 license, permitting commercial applications without incurring royalties or facing restrictions. It's important to note that this is a raw, pretrained model and is generally recommended to be fine-tuned for optimal performance in most applications. If you need a version that is more adept at handling general instructions in a conversational format, you might want to explore Falcon-40B-Instruct as a potential alternative.

The MiniMax‑M1 model, introduced by MiniMax AI and licensed under Apache 2.0, represents a significant advancement in hybrid-attention reasoning architecture. With an extraordinary capacity for handling a 1 million-token context window and generating outputs of up to 80,000 tokens, it facilitates in-depth analysis of lengthy texts. Utilizing a cutting-edge CISPO algorithm, MiniMax‑M1 was trained through extensive reinforcement learning, achieving completion on 512 H800 GPUs in approximately three weeks. This model sets a new benchmark in performance across various domains, including mathematics, programming, software development, tool utilization, and understanding of long contexts, either matching or surpassing the capabilities of leading models in the field. Additionally, users can choose between two distinct variants of the model, each with a thinking budget of either 40K or 80K, and access the model's weights and deployment instructions on platforms like GitHub and Hugging Face. Such features make MiniMax‑M1 a versatile tool for developers and researchers alike.

StarCoder and StarCoderBase represent advanced Large Language Models specifically designed for code, developed using openly licensed data from GitHub, which encompasses over 80 programming languages, Git commits, GitHub issues, and Jupyter notebooks. In a manner akin to LLaMA, we constructed a model with approximately 15 billion parameters trained on a staggering 1 trillion tokens. Furthermore, we tailored the StarCoderBase model with 35 billion Python tokens, leading to the creation of what we now refer to as StarCoder. Our evaluations indicated that StarCoderBase surpasses other existing open Code LLMs when tested against popular programming benchmarks and performs on par with or even exceeds proprietary models like code-cushman-001 from OpenAI, the original Codex model that fueled early iterations of GitHub Copilot. With an impressive context length exceeding 8,000 tokens, the StarCoder models possess the capability to handle more information than any other open LLM, thus paving the way for a variety of innovative applications. This versatility is highlighted by our ability to prompt the StarCoder models through a sequence of dialogues, effectively transforming them into dynamic technical assistants that can provide support in diverse programming tasks.

Mistral 7B is a language model with 7.3 billion parameters that demonstrates superior performance compared to larger models such as Llama 2 13B on a variety of benchmarks. It utilizes innovative techniques like Grouped-Query Attention (GQA) for improved inference speed and Sliding Window Attention (SWA) to manage lengthy sequences efficiently. Released under the Apache 2.0 license, Mistral 7B is readily available for deployment on different platforms, including both local setups and prominent cloud services. Furthermore, a specialized variant known as Mistral 7B Instruct has shown remarkable capabilities in following instructions, outperforming competitors like Llama 2 13B Chat in specific tasks. This versatility makes Mistral 7B an attractive option for developers and researchers alike.

We are excited to announce the launch of Phi-2, a language model featuring 2.7 billion parameters that excels in reasoning and language comprehension, achieving top-tier results compared to other base models with fewer than 13 billion parameters. In challenging benchmarks, Phi-2 competes with and often surpasses models that are up to 25 times its size, a feat made possible by advancements in model scaling and meticulous curation of training data. Due to its efficient design, Phi-2 serves as an excellent resource for researchers interested in areas such as mechanistic interpretability, enhancing safety measures, or conducting fine-tuning experiments across a broad spectrum of tasks. To promote further exploration and innovation in language modeling, Phi-2 has been integrated into the Azure AI Studio model catalog, encouraging collaboration and development within the research community. Researchers can leverage this model to unlock new insights and push the boundaries of language technology.

Sky-T1-32B-Preview is an innovative open-source reasoning model crafted by the NovaSky team at UC Berkeley's Sky Computing Lab. It delivers performance comparable to proprietary models such as o1-preview on various reasoning and coding assessments, while being developed at a cost of less than $450, highlighting the potential for budget-friendly, advanced reasoning abilities. Fine-tuned from Qwen2.5-32B-Instruct, the model utilized a meticulously curated dataset comprising 17,000 examples spanning multiple fields, such as mathematics and programming. The entire training process was completed in just 19 hours using eight H100 GPUs with DeepSpeed Zero-3 offloading technology. Every component of this initiative—including the data, code, and model weights—is entirely open-source, allowing both academic and open-source communities to not only replicate but also improve upon the model's capabilities. This accessibility fosters collaboration and innovation in the realm of artificial intelligence research and development.

Claude Sonnet 3.5 sets a new standard for AI performance with outstanding benchmarks in graduate-level reasoning (GPQA), undergraduate-level knowledge (MMLU), and coding proficiency (HumanEval). This model shows significant improvements in understanding nuance, humor, and complex instructions, while consistently producing high-quality content that resonates naturally with users. Operating at twice the speed of Claude Opus 3, it delivers faster and more efficient results, making it perfect for use cases such as context-sensitive customer support and multi-step workflow automation. Claude Sonnet 3.5 is available for free on Claude.ai and the Claude iOS app, with higher rate limits for Claude Pro and Team plan subscribers. It’s also accessible through the Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI, making it an accessible and cost-effective choice for businesses and developers.

An advanced End-to-End MLLM is designed to accept various forms of references and effectively ground responses. The Ferret Model utilizes a combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which allows for detailed and flexible referring and grounding capabilities within the MLLM framework. The GRIT Dataset, comprising approximately 1.1 million entries, serves as a large-scale and hierarchical dataset specifically crafted for robust instruction tuning in the ground-and-refer category. Additionally, the Ferret-Bench is a comprehensive multimodal evaluation benchmark that simultaneously assesses referring, grounding, semantics, knowledge, and reasoning, ensuring a well-rounded evaluation of the model's capabilities. This intricate setup aims to enhance the interaction between language and visual data, paving the way for more intuitive AI systems.

We are thrilled to present DBRX, a versatile open LLM developed by Databricks. This innovative model achieves unprecedented performance on a variety of standard benchmarks, setting a new benchmark for existing open LLMs. Additionally, it equips both the open-source community and enterprises crafting their own LLMs with features that were once exclusive to proprietary model APIs; our evaluations indicate that it outperforms GPT-3.5 and competes effectively with Gemini 1.0 Pro. Notably, it excels as a code model, outperforming specialized counterparts like CodeLLaMA-70B in programming tasks, while also demonstrating its prowess as a general-purpose LLM. The remarkable quality of DBRX is complemented by significant enhancements in both training and inference efficiency. Thanks to its advanced fine-grained mixture-of-experts (MoE) architecture, DBRX elevates the efficiency of open models to new heights. In terms of inference speed, it can be twice as fast as LLaMA2-70B, and its total and active parameter counts are approximately 40% of those in Grok-1, showcasing its compact design without compromising capability. This combination of speed and size makes DBRX a game-changer in the landscape of open AI models.

The Mixtral 8x7B model is an advanced sparse mixture of experts (SMoE) system that boasts open weights and is released under the Apache 2.0 license. This model demonstrates superior performance compared to Llama 2 70B across various benchmarks while achieving inference speeds that are six times faster. Recognized as the leading open-weight model with a flexible licensing framework, Mixtral also excels in terms of cost-efficiency and performance. Notably, it competes with and often surpasses GPT-3.5 in numerous established benchmarks, highlighting its significance in the field. Its combination of accessibility, speed, and effectiveness makes it a compelling choice for developers seeking high-performing AI solutions.

Introducing DeepSeek-V3.2-Exp, our newest experimental model derived from V3.1-Terminus, featuring the innovative DeepSeek Sparse Attention (DSA) that enhances both training and inference speed for lengthy contexts. This DSA mechanism allows for precise sparse attention while maintaining output quality, leading to improved performance for tasks involving long contexts and a decrease in computational expenses. Benchmark tests reveal that V3.2-Exp matches the performance of V3.1-Terminus while achieving these efficiency improvements. The model is now fully operational across app, web, and API platforms. Additionally, to enhance accessibility, we have slashed DeepSeek API prices by over 50% effective immediately. During a transition period, users can still utilize V3.1-Terminus via a temporary API endpoint until October 15, 2025. DeepSeek encourages users to share their insights regarding DSA through our feedback portal. Complementing the launch, DeepSeek-V3.2-Exp has been made open-source, with model weights and essential technology—including crucial GPU kernels in TileLang and CUDA—accessible on Hugging Face. We look forward to seeing how the community engages with this advancement.