Top OpenEuroLLM Alternatives

OLMo 2 is a suite of fully open language models developed by the Allen Institute for AI (AI2), designed to provide researchers and developers with straightforward access to training datasets, open-source code, reproducible training methods, and extensive evaluations. These models are trained on a remarkable dataset consisting of up to 5 trillion tokens and are competitive with leading open-weight models such as Llama 3.1, especially in English academic assessments. A significant emphasis of OLMo 2 lies in maintaining training stability, utilizing techniques to reduce loss spikes during prolonged training sessions, and implementing staged training interventions to address capability weaknesses in the later phases of pretraining. Furthermore, the models incorporate advanced post-training methodologies inspired by AI2's Tülu 3, resulting in the creation of OLMo 2-Instruct models. To support continuous enhancements during the development lifecycle, an actionable evaluation framework called the Open Language Modeling Evaluation System (OLMES) has been established, featuring 20 benchmarks that assess vital capabilities. This thorough methodology not only promotes transparency but also actively encourages improvements in the performance of language models, ensuring they remain at the forefront of AI advancements. Ultimately, OLMo 2 aims to empower the research community by providing resources that foster innovation and collaboration in language modeling.

Teuken-7B is a cutting-edge multilingual language model designed to address the diverse linguistic landscape of Europe, emerging from the OpenGPT-X initiative. This model has been trained on a dataset where more than half comprises non-English content, effectively encompassing all 24 official languages of the European Union to ensure robust performance across these tongues. One of the standout features of Teuken-7B is its specially crafted multilingual tokenizer, which has been optimized for European languages, resulting in improved training efficiency and reduced inference costs compared to standard monolingual tokenizers. Users can choose between two distinct versions of the model: Teuken-7B-Base, which offers a foundational pre-trained experience, and Teuken-7B-Instruct, fine-tuned to enhance its responsiveness to user inquiries. Both variations are easily accessible on Hugging Face, promoting transparency and collaboration in the artificial intelligence sector while stimulating further advancements. The development of Teuken-7B not only showcases a commitment to fostering AI solutions but also underlines the importance of inclusivity and representation of Europe's rich cultural tapestry in technology. This initiative ultimately aims to bridge communication gaps and facilitate understanding among diverse populations across the continent.

Open R1 is a community-driven, open-source project aimed at replicating the advanced AI capabilities of DeepSeek-R1 through transparent and accessible methodologies. Participants can delve into the Open R1 AI model or engage in a complimentary online conversation with DeepSeek R1 through the Open R1 platform. This project provides a meticulous implementation of DeepSeek-R1's reasoning-optimized training framework, including tools for GRPO training, SFT fine-tuning, and synthetic data generation, all released under the MIT license. While the foundational training dataset remains proprietary, Open R1 empowers users with an extensive array of resources to build and refine their own AI models, fostering increased customization and exploration within the realm of artificial intelligence. Furthermore, this collaborative environment encourages innovation and shared knowledge, paving the way for advancements in AI technology.

OpenGPT-X is a German initiative focused on the development of large AI language models tailored to European needs, emphasizing qualities like adaptability, reliability, multilingual capabilities, and open-source accessibility. This collaborative effort brings together a range of partners to address the complete generative AI value chain, which involves scalable GPU infrastructure and the necessary data for training extensive language models, as well as model design and practical applications through prototypes and proofs of concept. The main objective of OpenGPT-X is to foster groundbreaking research with a strong focus on business applications, thereby enabling the rapid adoption of generative AI within Germany's economic framework. Moreover, the initiative prioritizes ethical AI development, ensuring that the resulting models align with European values and legal standards. In addition, OpenGPT-X provides essential resources like the LLM Workbook and a detailed three-part reference guide, replete with examples and tools to help users understand the critical features of large AI language models, ultimately promoting a deeper comprehension of this transformative technology. By offering such resources, OpenGPT-X not only advances the technical evolution of AI but also champions responsible use and implementation across diverse industries, thereby paving the way for a more informed approach to AI integration. This holistic approach aims to create a sustainable ecosystem where innovation and ethical considerations go hand in hand.

GigaChat 3 Ultra is a breakthrough open-source LLM, offering 702 billion parameters built on an advanced MoE architecture that keeps computation efficient while delivering frontier-level performance. Its design activates only 36 billion parameters per step, combining high intelligence with practical deployment speeds, even for research and enterprise workloads. The model is trained entirely from scratch on a 14-trillion-token dataset spanning ten+ languages, expansive natural corpora, technical literature, competitive programming problems, academic datasets, and more than 5.5 trillion synthetic tokens engineered to enhance reasoning depth. This approach enables the model to achieve exceptional Russian-language capabilities, strong multilingual performance, and competitive global benchmark scores across math (GSM8K, MATH-500), programming (HumanEval+), and domain-specific evaluations. GigaChat 3 Ultra is optimized for compatibility with modern open-source tooling, enabling fine-tuning, inference, and integration using standard frameworks without complex custom builds. Advanced engineering techniques—including MTP, MLA, expert balancing, and large-scale distributed training—ensure stable learning at enormous scale while preserving fast inference. Beyond raw intelligence, the model includes upgraded alignment, improved conversational behavior, and a refined chat template using TypeScript-based function definitions for cleaner, more efficient interactions. It also features a built-in code interpreter, enhanced search subsystem with query reformulation, long-term user memory capabilities, and improved Russian-language stylistic accuracy down to punctuation and orthography. With leading performance on Russian benchmarks and strong showings across international tests, GigaChat 3 Ultra stands among the top five largest and most advanced open-source LLMs in the world. It represents a major engineering milestone for the open community.

Tülu 3 represents a state-of-the-art language model designed by the Allen Institute for AI (Ai2) with the objective of enhancing expertise in various domains such as knowledge, reasoning, mathematics, coding, and safety. Built on the foundation of the Llama 3 Base, it undergoes an intricate four-phase post-training process: meticulous prompt curation and synthesis, supervised fine-tuning across a diverse range of prompts and outputs, preference tuning with both off-policy and on-policy data, and a distinctive reinforcement learning approach that bolsters specific skills through quantifiable rewards. This open-source model is distinguished by its commitment to transparency, providing comprehensive access to its training data, coding resources, and evaluation metrics, thus helping to reduce the performance gap typically seen between open-source and proprietary fine-tuning methodologies. Performance evaluations indicate that Tülu 3 excels beyond similarly sized models, such as Llama 3.1-Instruct and Qwen2.5-Instruct, across multiple benchmarks, emphasizing its superior effectiveness. The ongoing evolution of Tülu 3 not only underscores a dedication to enhancing AI capabilities but also fosters an inclusive and transparent technological landscape. As such, it paves the way for future advancements in artificial intelligence that prioritize collaboration and accessibility for all users.

This repository presents the research preview for LongLLaMA, an innovative large language model capable of handling extensive contexts, reaching up to 256,000 tokens or potentially even more. Built on the OpenLLaMA framework, LongLLaMA has been fine-tuned using the Focused Transformer (FoT) methodology. The foundational code for this model comes from Code Llama. We are excited to introduce a smaller 3B base version of the LongLLaMA model, which is not instruction-tuned, and it will be released under an open license (Apache 2.0). Accompanying this release is inference code that supports longer contexts, available on Hugging Face. The model's weights are designed to effortlessly integrate with existing systems tailored for shorter contexts, particularly those that accommodate up to 2048 tokens. In addition to these features, we provide evaluation results and comparisons to the original OpenLLaMA models, thus offering a thorough insight into LongLLaMA's effectiveness in managing long-context tasks. This advancement marks a significant step forward in the field of language models, enabling more sophisticated applications and research opportunities.

Perplexity AI has unveiled R1 1776 as an open-source large language model (LLM) constructed on the DeepSeek R1 framework, aimed at promoting transparency and facilitating collaborative endeavors in AI development. This release allows researchers and developers to delve into the model's architecture and source code, enabling them to refine and adapt it for various applications. Through the public availability of R1 1776, Perplexity AI aspires to stimulate innovation while maintaining ethical principles within the AI industry. This initiative not only empowers the community but also cultivates a culture of shared knowledge and accountability among those working in AI. Furthermore, it represents a significant step towards democratizing access to advanced AI technologies.

Mistral Large 3 is a frontier-scale open AI model built on a sophisticated Mixture-of-Experts framework that unlocks 41B active parameters per step while maintaining a massive 675B total parameter capacity. This architecture lets the model deliver exceptional reasoning, multilingual mastery, and multimodal understanding at a fraction of the compute cost typically associated with models of this scale. Trained entirely from scratch on 3,000 NVIDIA H200 GPUs, it reaches competitive alignment performance with leading closed models, while achieving best-in-class results among permissively licensed alternatives. Mistral Large 3 includes base and instruction editions, supports images natively, and will soon introduce a reasoning-optimized version capable of even deeper thought chains. Its inference stack has been carefully co-designed with NVIDIA, enabling efficient low-precision execution, optimized MoE kernels, speculative decoding, and smooth long-context handling on Blackwell NVL72 systems and enterprise-grade clusters. Through collaborations with vLLM and Red Hat, developers gain an easy path to run Large 3 on single-node 8×A100 or 8×H100 environments with strong throughput and stability. The model is available across Mistral AI Studio, Amazon Bedrock, Azure Foundry, Hugging Face, Fireworks, OpenRouter, Modal, and more, ensuring turnkey access for development teams. Enterprises can go further with Mistral’s custom-training program, tailoring the model to proprietary data, regulatory workflows, or industry-specific tasks. From agentic applications to multilingual customer automation, creative workflows, edge deployment, and advanced tool-use systems, Mistral Large 3 adapts to a wide range of production scenarios. With this release, Mistral positions the 3-series as a complete family—spanning lightweight edge models to frontier-scale MoE intelligence—while remaining fully open, customizable, and performance-optimized across the stack.

Llama, a leading-edge foundational large language model developed by Meta AI, is designed to assist researchers in expanding the frontiers of artificial intelligence research. By offering streamlined yet powerful models like Llama, even those with limited resources can access advanced tools, thereby enhancing inclusivity in this fast-paced and ever-evolving field. The development of more compact foundational models, such as Llama, proves beneficial in the realm of large language models since they require considerably less computational power and resources, which allows for the exploration of novel approaches, validation of existing studies, and examination of potential new applications. These models harness vast amounts of unlabeled data, rendering them particularly effective for fine-tuning across diverse tasks. We are introducing Llama in various sizes, including 7B, 13B, 33B, and 65B parameters, each supported by a comprehensive model card that details our development methodology while maintaining our dedication to Responsible AI practices. By providing these resources, we seek to empower a wider array of researchers to actively participate in and drive forward the developments in the field of AI. Ultimately, our goal is to foster an environment where innovation thrives and collaboration flourishes.

We are thrilled to introduce MPT-7B, the latest model in the MosaicML Foundation Series. This transformer model has been carefully developed from scratch, utilizing 1 trillion tokens of varied text and code during its training. It is accessible as open-source software, making it suitable for commercial use and achieving performance levels comparable to LLaMA-7B. The entire training process was completed in just 9.5 days on the MosaicML platform, with no human intervention, and incurred an estimated cost of $200,000. With MPT-7B, users can train, customize, and deploy their own versions of MPT models, whether they opt to start from one of our existing checkpoints or initiate a new project. Additionally, we are excited to unveil three specialized variants alongside the core MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, with the latter featuring an exceptional context length of 65,000 tokens for generating extensive content. These new offerings greatly expand the horizons for developers and researchers eager to harness the capabilities of transformer models in their innovative initiatives. Furthermore, the flexibility and scalability of MPT-7B are designed to cater to a wide range of application needs, fostering creativity and efficiency in developing advanced AI solutions.

StarCoder and StarCoderBase are sophisticated Large Language Models crafted for coding tasks, built from freely available data sourced from GitHub, which includes an extensive array of over 80 programming languages, along with Git commits, GitHub issues, and Jupyter notebooks. Similarly to LLaMA, these models were developed with around 15 billion parameters trained on an astonishing 1 trillion tokens. Additionally, StarCoderBase was specifically optimized with 35 billion Python tokens, culminating in the evolution of what we now recognize as StarCoder. Our assessments revealed that StarCoderBase outperforms other open-source Code LLMs when evaluated against well-known programming benchmarks, matching or even exceeding the performance of proprietary models like OpenAI's code-cushman-001 and the original Codex, which was instrumental in the early development of GitHub Copilot. With a remarkable context length surpassing 8,000 tokens, the StarCoder models can manage more data than any other open LLM available, thus unlocking a plethora of possibilities for innovative applications. This adaptability is further showcased by our ability to engage with the StarCoder models through a series of interactive dialogues, effectively transforming them into versatile technical aides capable of assisting with a wide range of programming challenges. Furthermore, this interactive capability enhances user experience, making it easier for developers to obtain immediate support and insights on complex coding issues.

We are thrilled to unveil our new X1 large series of models, marking a significant advancement in our offerings. The most powerful model from Giga ML is now available for both pre-training and fine-tuning in an on-premises setup. Our integration with Open AI ensures seamless compatibility with existing tools such as long chain, llama-index, and more, enhancing usability. Additionally, users have the option to pre-train LLMs using tailored data sources, including industry-specific documents or proprietary company files. As the realm of large language models (LLMs) continues to rapidly advance, it presents remarkable opportunities for breakthroughs in natural language processing across diverse sectors. However, the industry still faces several substantial challenges that need addressing. At Giga ML, we are proud to present the X1 Large 32k model, an innovative on-premise LLM solution crafted to confront these key challenges head-on, empowering organizations to fully leverage the capabilities of LLMs. This launch is not just a step forward for our technology, but a major stride towards enhancing the language processing capabilities of businesses everywhere. We believe that by providing these advanced tools, we can drive meaningful improvements in how organizations communicate and operate.

Olmo 3 constitutes an extensive series of open models that include versions with 7 billion and 32 billion parameters, delivering outstanding performance in areas such as base functionality, reasoning, instruction, and reinforcement learning, all while ensuring transparency throughout the development process, including access to raw training datasets, intermediate checkpoints, training scripts, extended context support (with a remarkable window of 65,536 tokens), and provenance tools. The backbone of these models is derived from the Dolma 3 dataset, which encompasses about 9 trillion tokens and employs a thoughtful mixture of web content, scientific research, programming code, and comprehensive documents; this meticulous strategy of pre-training, mid-training, and long-context usage results in base models that receive further refinement through supervised fine-tuning, preference optimization, and reinforcement learning with accountable rewards, leading to the emergence of the Think and Instruct versions. Importantly, the 32 billion Think model has earned recognition as the most formidable fully open reasoning model available thus far, showcasing a performance level that closely competes with that of proprietary models in disciplines such as mathematics, programming, and complex reasoning tasks, highlighting a considerable leap forward in the realm of open model innovation. This breakthrough not only emphasizes the capabilities of open-source models but also suggests a promising future where they can effectively rival conventional closed systems across a range of sophisticated applications, potentially reshaping the landscape of artificial intelligence.

GPT-J is an advanced language model created by EleutherAI, recognized for its remarkable abilities. In terms of performance, GPT-J demonstrates a level of proficiency that competes with OpenAI's renowned GPT-3 across a range of zero-shot tasks. Impressively, it has surpassed GPT-3 in certain aspects, particularly in code generation. The latest iteration, named GPT-J-6B, is built on an extensive linguistic dataset known as The Pile, which is publicly available and comprises a massive 825 gibibytes of language data organized into 22 distinct subsets. While GPT-J shares some characteristics with ChatGPT, it is essential to note that its primary focus is on text prediction rather than serving as a chatbot. Additionally, a significant development occurred in March 2023 when Databricks introduced Dolly, a model designed to follow instructions and operating under an Apache license, which further enhances the array of available language models. This ongoing progression in AI technology is instrumental in expanding the possibilities within the realm of natural language processing. As these models evolve, they continue to reshape how we interact with and utilize language in various applications.

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.

Stability AI, in collaboration with its CarperAI lab, proudly introduces Stable Beluga 1 and its enhanced version, Stable Beluga 2, formerly called FreeWilly, both of which are powerful new Large Language Models (LLMs) now accessible to the public. These innovations demonstrate exceptional reasoning abilities across a diverse array of benchmarks, highlighting their adaptability and robustness. Stable Beluga 1 is constructed upon the foundational LLaMA 65B model and has been carefully fine-tuned using a cutting-edge synthetically-generated dataset through Supervised Fine-Tune (SFT) in the traditional Alpaca format. Similarly, Stable Beluga 2 is based on the LLaMA 2 70B model, further advancing performance standards in the field. The introduction of these models signifies a major advancement in the progression of open access AI technology, paving the way for future developments in the sector. With their release, users can expect enhanced capabilities that could revolutionize various applications.

We are developing sophisticated large language models specifically designed to embrace India's diverse linguistic landscape, while also promoting groundbreaking GenAI applications with tailored enterprise solutions. Our primary goal is to establish a comprehensive platform that enables businesses to easily develop and evaluate their own GenAI applications. With a strong belief in the power of open-source technology, we are committed to supporting community-oriented models and datasets, and we will lead efforts to assemble extensive data resources that benefit the public. Our team is made up of passionate AI innovators who integrate their skills in research, engineering, product design, and business strategy to propel advancements in the field. Driven by a shared commitment to scientific rigor and a desire to create a positive impact on society, we nurture a work culture where tackling complex technological challenges is viewed as a genuine passion. In this collaborative setting, we aim to expand the horizons of AI and its applications for the betterment of communities both locally and globally. By fostering innovation and inclusivity, we believe we can unlock new possibilities and drive meaningful change across various sectors.

LFM2 is a cutting-edge series of on-device foundation models specifically engineered to deliver an exceptionally fast generative-AI experience across a wide range of devices. It employs an innovative hybrid architecture that enables decoding and pre-filling speeds up to twice as fast as competing models, while also improving training efficiency by as much as threefold compared to earlier versions. Striking a perfect balance between quality, latency, and memory use, these models are ideally suited for embedded system applications, allowing for real-time, on-device AI capabilities in smartphones, laptops, vehicles, wearables, and many other platforms. This results in millisecond-level inference, enhanced device longevity, and complete data sovereignty for users. Available in three configurations with 0.35 billion, 0.7 billion, and 1.2 billion parameters, LFM2 demonstrates superior benchmark results compared to similarly sized models, excelling in knowledge recall, mathematical problem-solving, adherence to multilingual instructions, and conversational dialogue evaluations. With such impressive capabilities, LFM2 not only elevates the user experience but also establishes a new benchmark for on-device AI performance, paving the way for future advancements in the field.

IBM® Granite™ offers a collection of AI models tailored for business use, developed with a strong emphasis on trustworthiness and scalability in AI solutions. At present, the open-source Granite models are readily available for use. Our mission is to democratize AI access for developers, which is why we have made the core Granite Code, along with Time Series, Language, and GeoSpatial models, available as open-source on Hugging Face. These resources are shared under the permissive Apache 2.0 license, enabling broad commercial usage without significant limitations. Each Granite model is crafted using carefully curated data, providing outstanding transparency about the origins of the training material. Furthermore, we have released tools for validating and maintaining the quality of this data to the public, adhering to the high standards necessary for enterprise applications. This unwavering commitment to transparency and quality not only underlines our dedication to innovation but also encourages collaboration within the AI community, paving the way for future advancements.

The latest iteration in the Llama series, Llama 3.3, marks a notable leap forward in the realm of language models, designed to improve AI's abilities in both understanding and communication. It features enhanced contextual reasoning, more refined language generation, and state-of-the-art fine-tuning capabilities that yield remarkably accurate, human-like responses for a wide array of applications. This version benefits from a broader training dataset, advanced algorithms that allow for deeper comprehension, and reduced biases when compared to its predecessors. Llama 3.3 excels in various domains such as natural language understanding, creative writing, technical writing, and multilingual conversations, making it an invaluable tool for businesses, developers, and researchers. Furthermore, its modular design lends itself to adaptable deployment across specific sectors, ensuring consistent performance and flexibility even in expansive applications. With these significant improvements, Llama 3.3 is set to transform the benchmarks for AI language models and inspire further innovations in the field. It is an exciting time for AI development as this new version opens doors to novel possibilities in human-computer interaction.

Qwen-7B represents the seventh iteration in Alibaba Cloud's Qwen language model lineup, also referred to as Tongyi Qianwen, featuring 7 billion parameters. This advanced language model employs a Transformer architecture and has undergone pretraining on a vast array of data, including web content, literature, programming code, and more. In addition, we have launched Qwen-7B-Chat, an AI assistant that enhances the pretrained Qwen-7B model by integrating sophisticated alignment techniques. The Qwen-7B series includes several remarkable attributes: Its training was conducted on a premium dataset encompassing over 2.2 trillion tokens collected from a custom assembly of high-quality texts and codes across diverse fields, covering both general and specialized areas of knowledge. Moreover, the model excels in performance, outshining similarly-sized competitors on various benchmark datasets that evaluate skills in natural language comprehension, mathematical reasoning, and programming challenges. This establishes Qwen-7B as a prominent contender in the AI language model landscape. In summary, its intricate training regimen and solid architecture contribute significantly to its outstanding adaptability and efficiency in a wide range of applications.

Qwen2 is a comprehensive array of advanced language models developed by the Qwen team at Alibaba Cloud. This collection includes various models that range from base to instruction-tuned versions, with parameters from 0.5 billion up to an impressive 72 billion, demonstrating both dense configurations and a Mixture-of-Experts architecture. The Qwen2 lineup is designed to surpass many earlier open-weight models, including its predecessor Qwen1.5, while also competing effectively against proprietary models across several benchmarks in domains such as language understanding, text generation, multilingual capabilities, programming, mathematics, and logical reasoning. Additionally, this cutting-edge series is set to significantly influence the artificial intelligence landscape, providing enhanced functionalities that cater to a wide array of applications. As such, the Qwen2 models not only represent a leap in technological advancement but also pave the way for future innovations in the field.

Aya stands as a pioneering open-source generative large language model that supports a remarkable 101 languages, far exceeding the offerings of other open-source alternatives. This expansive language support allows researchers to harness the powerful capabilities of LLMs for numerous languages and cultures that have frequently been neglected by dominant models in the industry. Alongside the launch of the Aya model, we are also unveiling the largest multilingual instruction fine-tuning dataset, which contains 513 million entries spanning 114 languages. This extensive dataset is enriched with distinctive annotations from native and fluent speakers around the globe, ensuring that AI technology can address the needs of a diverse international community that has often encountered obstacles to access. Therefore, Aya not only broadens the horizons of multilingual AI but also fosters inclusivity among various linguistic groups, paving the way for future advancements in the field. By creating an environment where linguistic diversity is celebrated, Aya stands to inspire further innovations that can bridge gaps in communication and understanding.

Vicuna-13B is a conversational AI created by fine-tuning LLaMA on a collection of user dialogues sourced from ShareGPT. Early evaluations, using GPT-4 as a benchmark, suggest that Vicuna-13B reaches over 90% of the performance level found in OpenAI's ChatGPT and Google Bard, while outperforming other models like LLaMA and Stanford Alpaca in more than 90% of tested cases. The estimated cost to train Vicuna-13B is around $300, which is quite economical for a model of its caliber. Furthermore, the model's source code and weights are publicly accessible under non-commercial licenses, promoting a spirit of collaboration and further development. This level of transparency not only fosters innovation but also allows users to delve into the model's functionalities across various applications, paving the way for new ideas and enhancements. Ultimately, such initiatives can significantly contribute to the advancement of conversational AI technologies.

Qwen3-Max is Alibaba's state-of-the-art large language model, boasting an impressive trillion parameters designed to enhance performance in tasks that demand agency, coding, reasoning, and the management of long contexts. As a progression of the Qwen3 series, this model utilizes improved architecture, training techniques, and inference methods; it features both thinker and non-thinker modes, introduces a distinctive “thinking budget” approach, and offers the flexibility to switch modes according to the complexity of the tasks. With its capability to process extremely long inputs and manage hundreds of thousands of tokens, it also enables the invocation of tools and showcases remarkable outcomes across various benchmarks, including evaluations related to coding, multi-step reasoning, and agent assessments like Tau2-Bench. Although the initial iteration primarily focuses on following instructions within a non-thinking framework, Alibaba plans to roll out reasoning features that will empower autonomous agent functionalities in the near future. Furthermore, with its robust multilingual support and comprehensive training on trillions of tokens, Qwen3-Max is available through API interfaces that integrate well with OpenAI-style functionalities, guaranteeing extensive applicability across a range of applications. This extensive and innovative framework positions Qwen3-Max as a significant competitor in the field of advanced artificial intelligence language models, making it a pivotal tool for developers and researchers alike.

Introducing the Granite series of decoder-only code models, purpose-built for various code generation tasks such as debugging, explaining code, and creating documentation, while supporting an impressive range of 116 programming languages. A comprehensive evaluation of the Granite Code model family across multiple tasks demonstrates that these models consistently outperform other open-source code language models currently available, establishing their superiority in the field. One of the key advantages of the Granite Code models is their versatility: they achieve competitive or leading results in numerous code-related activities, including code generation, explanation, debugging, editing, and translation, thereby highlighting their ability to effectively tackle a diverse set of coding challenges. Furthermore, their adaptability equips them to excel in both straightforward and intricate coding situations, making them a valuable asset for developers. In addition, all models within the Granite series are created using data that adheres to licensing standards and follows IBM's AI Ethics guidelines, ensuring their reliability and integrity for enterprise-level applications. This commitment to ethical practices reinforces the models' position as trustworthy tools for professionals in the coding landscape.

Developing advanced language models poses considerable hurdles, requiring immense computational power, sophisticated distributed computing methods, and a deep understanding of machine learning. As a result, only a select few organizations undertake the complex endeavor of creating large language models (LLMs) independently. Additionally, many entities equipped with the requisite expertise and resources have started to limit the accessibility of their discoveries, reflecting a significant change from the more open practices observed in recent months. At Cerebras, we prioritize the importance of open access to leading-edge models, which is why we proudly introduce Cerebras-GPT to the open-source community. This initiative features a lineup of seven GPT models, with parameter sizes varying from 111 million to 13 billion. By employing the Chinchilla training formula, these models achieve remarkable accuracy while maintaining computational efficiency. Importantly, Cerebras-GPT is designed to offer faster training times, lower costs, and reduced energy use compared to any other model currently available to the public. Through the release of these models, we aspire to encourage further innovation and foster collaborative efforts within the machine learning community, ultimately pushing the boundaries of what is possible in this rapidly evolving field.

GLM-5 is Z.ai’s most advanced open-source model to date, purpose-built for complex systems engineering, long-horizon planning, and autonomous agent workflows. Building on the foundation of GLM-4.5, it dramatically scales both total parameters and pre-training data while increasing active parameter efficiency. The integration of DeepSeek Sparse Attention allows GLM-5 to maintain strong long-context reasoning capabilities while reducing deployment costs. To improve post-training performance, Z.ai developed slime, an asynchronous reinforcement learning infrastructure that significantly boosts training throughput and iteration speed. As a result, GLM-5 achieves top-tier performance among open-source models across reasoning, coding, and general agent benchmarks. It demonstrates exceptional strength in long-term operational simulations, including leading results on Vending Bench 2, where it manages a year-long simulated business with strong financial outcomes. In coding evaluations such as SWE-bench and Terminal-Bench 2.0, GLM-5 delivers competitive results that narrow the gap with proprietary frontier systems. The model is fully open-sourced under the MIT License and available through Hugging Face, ModelScope, and Z.ai’s developer platforms. Developers can deploy GLM-5 locally using inference frameworks like vLLM and SGLang, including support for non-NVIDIA hardware through optimization and quantization techniques. Through Z.ai, users can access both Chat Mode for fast interactions and Agent Mode for tool-augmented, multi-step task execution. GLM-5 also enables structured document generation, producing ready-to-use .docx, .pdf, and .xlsx files for business and academic workflows. With compatibility across coding agents and cross-application automation frameworks, GLM-5 moves foundation models from conversational assistants toward full-scale work engines.

Meta’s Llama 4 Behemoth is an advanced multimodal AI model that boasts 288 billion active parameters, making it one of the most powerful models in the world. It outperforms other leading models like GPT-4.5 and Gemini 2.0 Pro on numerous STEM-focused benchmarks, showcasing exceptional skills in math, reasoning, and image understanding. As the teacher model behind Llama 4 Scout and Llama 4 Maverick, Llama 4 Behemoth drives major advancements in model distillation, improving both efficiency and performance. Currently still in training, Behemoth is expected to redefine AI intelligence and multimodal processing once fully deployed.