Alternatives to Codestral Mamba

Google AI Studio is an all-in-one environment designed for building AI-first applications with Google’s latest models. It supports Gemini, Imagen, Veo, and Gemma, allowing developers to experiment across multiple modalities in one place. The platform emphasizes vibe coding, enabling users to describe what they want and let AI handle the technical heavy lifting. Developers can generate complete, production-ready apps using natural language instructions. One-click deployment makes it easy to move from prototype to live application. Google AI Studio includes a centralized dashboard for API keys, billing, and usage tracking. Detailed logs and rate-limit insights help teams operate efficiently. SDK support for Python, Node.js, and REST APIs ensures flexibility. Quickstart guides reduce onboarding time to minutes. Overall, Google AI Studio blends experimentation, vibe coding, and scalable production into a single workflow.

Falcon Mamba 7B marks a significant milestone as the inaugural open-source State Space Language Model (SSLM), presenting a revolutionary architecture within the Falcon model family. Celebrated as the premier open-source SSLM globally by Hugging Face, it establishes a new standard for efficiency in artificial intelligence. In contrast to conventional transformers, SSLMs require significantly less memory and can produce lengthy text sequences seamlessly without extra resource demands. Falcon Mamba 7B outperforms top transformer models, such as Meta’s Llama 3.1 8B and Mistral’s 7B, demonstrating enhanced capabilities. This breakthrough not only highlights Abu Dhabi’s dedication to pushing the boundaries of AI research but also positions the region as a pivotal player in the global AI landscape. Such advancements are vital for fostering innovation and collaboration in technology.

Mistral Code is a cutting-edge AI coding assistant tailored for enterprise software engineering teams that need frontier-grade AI capabilities combined with security, compliance, and full IT control. Building on the proven open-source Continue project, Mistral Code delivers a vertically integrated solution that includes state-of-the-art models like Codestral, Codestral Embed, Devstral, and Mistral Medium for comprehensive coding assistance—from autocomplete to agentic coding and chat support. It supports local, cloud, and serverless deployments, allowing enterprises to choose how and where to run AI-powered coding workflows while ensuring all code and data remain within corporate boundaries. Addressing key enterprise pain points, Mistral Code offers deep customization, broad task automation beyond simple suggestions, and unified SLAs across models, plugins, and infrastructure. The platform is capable of reasoning over code files, Git diffs, terminal output, and issues, enabling engineers to complete fully scoped development tasks with configurable approval workflows to keep senior engineers in control. Enterprises such as Spain’s Abanca, France’s SNCF, and global integrator Capgemini rely on Mistral Code to boost developer productivity while maintaining compliance in regulated industries. The system includes a rich admin console with granular platform controls, seat management, and detailed usage analytics for IT managers. Mistral Code is currently in private beta for JetBrains IDEs and VSCode, with general availability expected soon.

Phi-4-mini-flash-reasoning is a 3.8 billion-parameter model that is part of Microsoft's Phi series, specifically designed for edge, mobile, and other environments with constrained resources where processing power, memory, and speed are limited. This innovative model features the SambaY hybrid decoder architecture, integrating Gated Memory Units (GMUs) with Mamba state-space and sliding-window attention layers, achieving up to ten times the throughput and a latency reduction of 2 to 3 times compared to its earlier versions without compromising on its ability to perform complex mathematical and logical reasoning. With a support for a context length of 64K tokens and being fine-tuned on high-quality synthetic datasets, it is particularly adept at handling long-context retrieval, reasoning tasks, and real-time inference, all manageable on a single GPU. Available through platforms such as Azure AI Foundry, NVIDIA API Catalog, and Hugging Face, Phi-4-mini-flash-reasoning empowers developers to create applications that are not only fast but also scalable and capable of intensive logical processing. This accessibility allows a broader range of developers to leverage its capabilities for innovative solutions.

Jamba stands out as the most potent and effective long context model, specifically designed for builders while catering to enterprise needs. With superior latency compared to other leading models of similar sizes, Jamba boasts a remarkable 256k context window, the longest that is openly accessible. Its innovative Mamba-Transformer MoE architecture focuses on maximizing cost-effectiveness and efficiency. Key features available out of the box include function calls, JSON mode output, document objects, and citation mode, all designed to enhance user experience. Jamba 1.5 models deliver exceptional performance throughout their extensive context window and consistently achieve high scores on various quality benchmarks. Enterprises can benefit from secure deployment options tailored to their unique requirements, allowing for seamless integration into existing systems. Jamba can be easily accessed on our robust SaaS platform, while deployment options extend to strategic partners, ensuring flexibility for users. For organizations with specialized needs, we provide dedicated management and continuous pre-training, ensuring that every client can leverage Jamba’s capabilities to the fullest. This adaptability makes Jamba a prime choice for enterprises looking for cutting-edge solutions.

Tencent Hunyuan represents a comprehensive family of multimodal AI models crafted by Tencent, encompassing a range of modalities including text, images, video, and 3D data, all aimed at facilitating general-purpose AI applications such as content creation, visual reasoning, and automating business processes. This model family features various iterations tailored for tasks like natural language interpretation, multimodal comprehension that combines vision and language (such as understanding images and videos), generating images from text, creating videos, and producing 3D content. The Hunyuan models utilize a mixture-of-experts framework alongside innovative strategies, including hybrid "mamba-transformer" architectures, to excel in tasks requiring reasoning, long-context comprehension, cross-modal interactions, and efficient inference capabilities. A notable example is the Hunyuan-Vision-1.5 vision-language model, which facilitates "thinking-on-image," allowing for intricate multimodal understanding and reasoning across images, video segments, diagrams, or spatial information. This robust architecture positions Hunyuan as a versatile tool in the rapidly evolving field of AI, capable of addressing a diverse array of challenges.

HunyuanVision, an innovative vision-language model created by Tencent's Hunyuan team, employs a mamba-transformer hybrid architecture that excels in performance and offers efficient inference for multimodal reasoning challenges. The latest iteration, Hunyuan-Vision-1.5, focuses on the concept of “thinking on images,” enabling it to not only comprehend the interplay of visual and linguistic content but also engage in advanced reasoning that includes tasks like cropping, zooming, pointing, box drawing, or annotating images for enhanced understanding. This model is versatile, supporting various vision tasks such as image and video recognition, OCR, and diagram interpretation, in addition to facilitating visual reasoning and 3D spatial awareness, all within a cohesive multilingual framework. Designed for compatibility across different languages and tasks, HunyuanVision aims to be open-sourced, providing access to checkpoints, a technical report, and inference support to foster community engagement and experimentation. Ultimately, this initiative encourages researchers and developers to explore and leverage the model's capabilities in diverse applications.

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.

The Falcon LLM represents a cutting-edge open-source family of large language models created by the Technology Innovation Institute (TII), offering sophisticated generative AI functionalities for natural language comprehension, logical reasoning, and content creation across a wide array of applications. Constructed using hybrid Transformer and Mamba architectures, the Falcon models range from compact 7B versions that excel in reasoning tasks to the formidable Falcon 180B, which competes with top-tier AI systems, ensuring both efficiency and scalability for various real-world applications like text generation, translation, summarization, sentiment analysis, and interactive chat agents. Additionally, it accommodates multilingual and multimodal functionality, effectively managing diverse languages and data formats while incorporating features for long context processing, coding tasks, logic, and mathematical reasoning, all while maintaining performance in environments with limited resources. This versatility makes the Falcon LLM a valuable tool for developers and researchers exploring innovative AI solutions.

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.

Introducing Mistral NeMo, our latest and most advanced small model yet, featuring a cutting-edge 12 billion parameters and an expansive context length of 128,000 tokens, all released under the Apache 2.0 license. Developed in partnership with NVIDIA, Mistral NeMo excels in reasoning, world knowledge, and coding proficiency within its category. Its architecture adheres to industry standards, making it user-friendly and a seamless alternative for systems currently utilizing Mistral 7B. To facilitate widespread adoption among researchers and businesses, we have made available both pre-trained base and instruction-tuned checkpoints under the same Apache license. Notably, Mistral NeMo incorporates quantization awareness, allowing for FP8 inference without compromising performance. The model is also tailored for diverse global applications, adept in function calling and boasting a substantial context window. When compared to Mistral 7B, Mistral NeMo significantly outperforms in understanding and executing detailed instructions, showcasing enhanced reasoning skills and the ability to manage complex multi-turn conversations. Moreover, its design positions it as a strong contender for multi-lingual tasks, ensuring versatility across various use cases.

In honor of Archimedes, whose 2311th anniversary we celebrate this year, we are excited to introduce our inaugural Mathstral model, a specialized 7B architecture tailored for mathematical reasoning and scientific exploration. This model features a 32k context window and is released under the Apache 2.0 license. Our intention behind contributing Mathstral to the scientific community is to enhance the pursuit of solving advanced mathematical challenges that necessitate intricate, multi-step logical reasoning. The launch of Mathstral is part of our wider initiative to support academic endeavors, developed in conjunction with Project Numina. Much like Isaac Newton during his era, Mathstral builds upon the foundation laid by Mistral 7B, focusing on STEM disciplines. It demonstrates top-tier reasoning capabilities within its category, achieving remarkable results on various industry-standard benchmarks. Notably, it scores 56.6% on the MATH benchmark and 63.47% on the MMLU benchmark, showcasing the performance differences by subject between Mathstral 7B and its predecessor, Mistral 7B, further emphasizing the advancements made in mathematical modeling. This initiative aims to foster innovation and collaboration within the mathematical community.

Mistral AI has introduced the Mistral Large 2, a sophisticated AI model crafted to excel in various domains such as code generation, multilingual understanding, and intricate reasoning tasks. With an impressive 128k context window, this model accommodates a wide array of languages, including English, French, Spanish, and Arabic, while also supporting an extensive list of over 80 programming languages. Designed for high-throughput single-node inference, Mistral Large 2 is perfectly suited for applications requiring large context handling. Its superior performance on benchmarks like MMLU, coupled with improved capabilities in code generation and reasoning, guarantees both accuracy and efficiency in results. Additionally, the model features enhanced function calling and retrieval mechanisms, which are particularly beneficial for complex business applications. This makes Mistral Large 2 not only versatile but also a powerful tool for developers and businesses looking to leverage advanced AI capabilities.

Code Llama is an advanced language model designed to generate code through text prompts, distinguishing itself as a leading tool among publicly accessible models for coding tasks. This innovative model not only streamlines workflows for existing developers but also aids beginners in overcoming challenges associated with learning to code. Its versatility positions Code Llama as both a valuable productivity enhancer and an educational resource, assisting programmers in creating more robust and well-documented software solutions. Additionally, users can generate both code and natural language explanations by providing either type of prompt, making it an adaptable tool for various programming needs. Available for free for both research and commercial applications, Code Llama is built upon Llama 2 architecture and comes in three distinct versions: the foundational Code Llama model, Code Llama - Python which is tailored specifically for Python programming, and Code Llama - Instruct, optimized for comprehending and executing natural language directives effectively.

We present the Granite series of decoder-only code models specifically designed for tasks involving code generation, such as debugging, code explanation, and documentation, utilizing programming languages across a spectrum of 116 different types. An extensive assessment of the Granite Code model family across various tasks reveals that these models consistently achieve leading performance compared to other open-source code language models available today. Among the notable strengths of Granite Code models are: Versatile Code LLM: The Granite Code models deliver competitive or top-tier results across a wide array of code-related tasks, which include code generation, explanation, debugging, editing, translation, and beyond, showcasing their capacity to handle various coding challenges effectively. Additionally, their adaptability makes them suitable for both simple and complex coding scenarios. Reliable Enterprise-Grade LLM: All models in this series are developed using data that complies with licensing requirements and is gathered in alignment with IBM's AI Ethics guidelines, ensuring trustworthy usage for enterprise 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.

Mercury, the groundbreaking creation from Inception Labs, represents the first large language model at a commercial scale that utilizes diffusion technology, achieving a remarkable tenfold increase in processing speed while also lowering costs in comparison to standard autoregressive models. Designed for exceptional performance in reasoning, coding, and the generation of structured text, Mercury can handle over 1000 tokens per second when operating on NVIDIA H100 GPUs, positioning it as one of the most rapid LLMs on the market. In contrast to traditional models that produce text sequentially, Mercury enhances its responses through a coarse-to-fine diffusion strategy, which boosts precision and minimizes instances of hallucination. Additionally, with the inclusion of Mercury Coder, a tailored coding module, developers are empowered to take advantage of advanced AI-assisted code generation that boasts remarkable speed and effectiveness. This innovative approach not only transforms coding practices but also sets a new benchmark for the capabilities of AI in various applications.

Gemini 2.0 represents a cutting-edge AI model created by Google, aimed at delivering revolutionary advancements in natural language comprehension, reasoning abilities, and multimodal communication. This new version builds upon the achievements of its earlier model by combining extensive language processing with superior problem-solving and decision-making skills, allowing it to interpret and produce human-like responses with enhanced precision and subtlety. In contrast to conventional AI systems, Gemini 2.0 is designed to simultaneously manage diverse data formats, such as text, images, and code, rendering it an adaptable asset for sectors like research, business, education, and the arts. Key enhancements in this model include improved contextual awareness, minimized bias, and a streamlined architecture that guarantees quicker and more consistent results. As a significant leap forward in the AI landscape, Gemini 2.0 is set to redefine the nature of human-computer interactions, paving the way for even more sophisticated applications in the future. Its innovative features not only enhance user experience but also facilitate more complex and dynamic engagements across various fields.

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.

XLNet introduces an innovative approach to unsupervised language representation learning by utilizing a unique generalized permutation language modeling objective. Furthermore, it leverages the Transformer-XL architecture, which proves to be highly effective in handling language tasks that require processing of extended contexts. As a result, XLNet sets new benchmarks with its state-of-the-art (SOTA) performance across multiple downstream language applications, such as question answering, natural language inference, sentiment analysis, and document ranking. This makes XLNet a significant advancement in the field of natural language processing.

Qwen2.5-1M, an open-source language model from the Qwen team, has been meticulously crafted to manage context lengths reaching as high as one million tokens. This version introduces two distinct model variants, namely Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, representing a significant advancement as it is the first instance of Qwen models being enhanced to accommodate such large context lengths. In addition to this, the team has released an inference framework that is based on vLLM and incorporates sparse attention mechanisms, which greatly enhance the processing speed for 1M-token inputs, achieving improvements between three to seven times. A detailed technical report accompanies this release, providing in-depth insights into the design choices and the results from various ablation studies. This transparency allows users to fully understand the capabilities and underlying technology of the models.

Mistral Large stands as the premier language model from Mistral AI, engineered for sophisticated text generation and intricate multilingual reasoning tasks such as text comprehension, transformation, and programming code development. This model encompasses support for languages like English, French, Spanish, German, and Italian, which allows it to grasp grammar intricacies and cultural nuances effectively. With an impressive context window of 32,000 tokens, Mistral Large can retain and reference information from lengthy documents with accuracy. Its abilities in precise instruction adherence and native function-calling enhance the development of applications and the modernization of tech stacks. Available on Mistral's platform, Azure AI Studio, and Azure Machine Learning, it also offers the option for self-deployment, catering to sensitive use cases. Benchmarks reveal that Mistral Large performs exceptionally well, securing its position as the second-best model globally that is accessible via an API, just behind GPT-4, illustrating its competitive edge in the AI landscape. Such capabilities make it an invaluable tool for developers seeking to leverage advanced AI technology.

GPT-4.1 represents a significant upgrade in generative AI, with notable advancements in coding, instruction adherence, and handling long contexts. This model supports up to 1 million tokens of context, allowing it to tackle complex, multi-step tasks across various domains. GPT-4.1 outperforms earlier models in key benchmarks, particularly in coding accuracy, and is designed to streamline workflows for developers and businesses by improving task completion speed and reliability.

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.

We are excited to unveil Codestral, our inaugural code generation model. This open-weight generative AI system is specifically crafted for tasks related to code generation, enabling developers to seamlessly write and engage with code via a unified instruction and completion API endpoint. As it becomes proficient in both programming languages and English, Codestral is poised to facilitate the creation of sophisticated AI applications tailored for software developers. With a training foundation that encompasses a wide array of over 80 programming languages—ranging from widely-used options like Python, Java, C, C++, JavaScript, and Bash to more niche languages such as Swift and Fortran—Codestral ensures a versatile support system for developers tackling various coding challenges and projects. Its extensive language capabilities empower developers to confidently navigate different coding environments, making Codestral an invaluable asset in the programming landscape.

CodeGen is an open-source framework designed for generating code through program synthesis, utilizing TPU-v4 for its training. It stands out as a strong contender against OpenAI Codex in the realm of code generation solutions.

Falcon-7B is a causal decoder-only model comprising 7 billion parameters, developed by TII and trained on an extensive dataset of 1,500 billion tokens from RefinedWeb, supplemented with specially selected corpora, and it is licensed under Apache 2.0. What are the advantages of utilizing Falcon-7B? This model surpasses similar open-source alternatives, such as MPT-7B, StableLM, and RedPajama, due to its training on a remarkably large dataset of 1,500 billion tokens from RefinedWeb, which is further enhanced with carefully curated content, as evidenced by its standing on the OpenLLM Leaderboard. Additionally, it boasts an architecture that is finely tuned for efficient inference, incorporating technologies like FlashAttention and multiquery mechanisms. Moreover, the permissive nature of the Apache 2.0 license means users can engage in commercial applications without incurring royalties or facing significant limitations. This combination of performance and flexibility makes Falcon-7B a strong choice for developers seeking advanced modeling capabilities.

Yi-Large is an innovative proprietary large language model created by 01.AI, featuring an impressive context length of 32k and a cost structure of $2 for each million tokens for both inputs and outputs. Renowned for its superior natural language processing abilities, common-sense reasoning, and support for multiple languages, it competes effectively with top models such as GPT-4 and Claude3 across various evaluations. This model is particularly adept at handling tasks that involve intricate inference, accurate prediction, and comprehensive language comprehension, making it ideal for applications such as knowledge retrieval, data categorization, and the development of conversational chatbots that mimic human interaction. Built on a decoder-only transformer architecture, Yi-Large incorporates advanced features like pre-normalization and Group Query Attention, and it has been trained on an extensive, high-quality multilingual dataset to enhance its performance. The model's flexibility and economical pricing position it as a formidable player in the artificial intelligence landscape, especially for businesses looking to implement AI technologies on a global scale. Additionally, its ability to adapt to a wide range of use cases underscores its potential to revolutionize how organizations leverage language models for various needs.

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.

Gemini Enterprise, an all-encompassing AI platform from Google Cloud, is designed to harness the full capabilities of Google’s sophisticated AI models, tools for creating agents, and enterprise-level access to data, seamlessly integrating these into daily workflows. This innovative solution features a cohesive chat interface that facilitates employee interaction with internal documents, applications, various data sources, and personalized AI agents. The foundation of Gemini Enterprise consists of six essential elements: the Gemini suite of large multimodal models, an agent orchestration workbench (previously known as Google Agentspace), ready-made starter agents, powerful data integration connectors for business systems, extensive security and governance frameworks, and a collaborative partner ecosystem for customized integrations. Built to scale across various departments and organizations, it empowers users to develop no-code or low-code agents capable of automating diverse tasks like research synthesis, customer service responses, code assistance, and contract analysis while adhering to corporate compliance regulations. Moreover, the platform is designed to enhance productivity and foster innovation within businesses, ensuring that users can leverage advanced AI technologies with ease.

GLM-4.7 Flash serves as a streamlined version of Z.ai's premier large language model, GLM-4.7, which excels in advanced coding, logical reasoning, and executing multi-step tasks with exceptional agentic capabilities and an extensive context window. This model, rooted in a mixture of experts (MoE) architecture, is fine-tuned for efficient inference, striking a balance between high performance and optimized resource utilization, thus making it suitable for deployment on local systems that require only moderate memory while still showcasing advanced reasoning, programming, and agent-like task handling. Building upon the advancements of its predecessor, GLM-4.7 brings forth enhanced capabilities in programming, reliable multi-step reasoning, context retention throughout interactions, and superior workflows for tool usage, while also accommodating lengthy context inputs, with support for up to approximately 200,000 tokens. The Flash variant successfully maintains many of these features within a more compact design, achieving competitive results on benchmarks for coding and reasoning tasks among similarly-sized models. Ultimately, this makes GLM-4.7 Flash an appealing choice for users seeking powerful language processing capabilities without the need for extensive computational resources.

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.

The Megatron-Turing Natural Language Generation model (MT-NLG) stands out as the largest and most advanced monolithic transformer model for the English language, boasting an impressive 530 billion parameters. This 105-layer transformer architecture significantly enhances the capabilities of previous leading models, particularly in zero-shot, one-shot, and few-shot scenarios. It exhibits exceptional precision across a wide range of natural language processing tasks, including completion prediction, reading comprehension, commonsense reasoning, natural language inference, and word sense disambiguation. To foster further research on this groundbreaking English language model and to allow users to explore and utilize its potential in various language applications, NVIDIA has introduced an Early Access program for its managed API service dedicated to the MT-NLG model. This initiative aims to facilitate experimentation and innovation in the field of natural language processing.

OpenAI’s GPT-5 represents the cutting edge in AI language models, designed to be smarter, faster, and more reliable across diverse applications such as legal analysis, scientific research, and financial modeling. This flagship model incorporates built-in “thinking” to deliver accurate, professional, and nuanced responses that help users solve complex problems. With a massive context window and high token output limits, GPT-5 supports extensive conversations and intricate coding tasks with minimal prompting. It introduces advanced features like the verbosity parameter, enabling users to control the detail and tone of generated content. GPT-5 also integrates seamlessly with enterprise data sources like Google Drive and SharePoint, enhancing response relevance with company-specific knowledge while ensuring data privacy. The model’s improved personality and steerability make it adaptable for a wide range of business needs. Available in ChatGPT and API platforms, GPT-5 brings expert intelligence to every user, from casual individuals to large organizations. Its release marks a major step forward in AI-assisted productivity and collaboration.

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.

Amazon Nova Premier is a cutting-edge model released as part of the Amazon Bedrock family, designed for tackling sophisticated tasks with unmatched efficiency. With the ability to process text, images, and video, it is ideal for complex workflows that require deep contextual understanding and multi-step execution. This model boasts a significant advantage with its one-million token context, making it suitable for analyzing massive documents or expansive code bases. Moreover, Nova Premier's distillation feature allows the creation of more efficient models, such as Nova Pro and Nova Micro, that deliver high accuracy with reduced latency and operational costs. Its advanced capabilities have already proven effective in various scenarios, such as investment research, where it can coordinate multiple agents to gather and synthesize relevant financial data. This process not only saves time but also enhances the overall efficiency of the AI models used.

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.

ChatGPT by OpenAI is a versatile AI conversational platform that provides assistance in writing, learning, brainstorming, code generation, and problem-solving across a wide range of topics. Available for free with optional Plus and Pro subscription plans, it supports real-time text and voice interactions on web browsers and mobile apps. Users can leverage ChatGPT to create content, summarize meetings, debug code, analyze data, and even generate images using integrated tools like DALL·E 3. The platform is accessible via desktop and mobile devices and offers personalized workflows through custom GPTs and projects. Advanced plans unlock deeper research capabilities, extended limits, and access to cutting-edge AI models like GPT-4o and OpenAI o1 pro mode. ChatGPT integrates search capabilities for real-time information and enables collaboration through features like Canvas for project editing. It caters to students, professionals, hobbyists, and developers seeking efficient, AI-driven support. OpenAI continually updates ChatGPT with new tools and enhanced usability.

Z.ai has unveiled its latest flagship model, GLM-4.5, which boasts an impressive 355 billion total parameters (with 32 billion active) and is complemented by the GLM-4.5-Air variant, featuring 106 billion total parameters (12 billion active), designed to integrate sophisticated reasoning, coding, and agent-like functions into a single framework. This model can switch between a "thinking" mode for intricate, multi-step reasoning and tool usage and a "non-thinking" mode that facilitates rapid responses, accommodating a context length of up to 128K tokens and enabling native function invocation. Accessible through the Z.ai chat platform and API, and with open weights available on platforms like HuggingFace and ModelScope, GLM-4.5 is adept at processing a wide range of inputs for tasks such as general problem solving, common-sense reasoning, coding from the ground up or within existing frameworks, as well as managing comprehensive workflows like web browsing and slide generation. The architecture is underpinned by a Mixture-of-Experts design, featuring loss-free balance routing, grouped-query attention mechanisms, and an MTP layer that facilitates speculative decoding, ensuring it meets enterprise-level performance standards while remaining adaptable to various applications. As a result, GLM-4.5 sets a new benchmark for AI capabilities across numerous domains.

Mistral AI has unveiled two cutting-edge models specifically designed for on-device computing and edge use cases, collectively referred to as "les Ministraux": Ministral 3B and Ministral 8B. These innovative models stand out due to their capabilities in knowledge retention, commonsense reasoning, function-calling, and overall efficiency, all while remaining within the sub-10B parameter range. They boast support for a context length of up to 128k, making them suitable for a diverse range of applications such as on-device translation, offline smart assistants, local analytics, and autonomous robotics. Notably, Ministral 8B incorporates an interleaved sliding-window attention mechanism, which enhances both the speed and memory efficiency of inference processes. Both models are adept at serving as intermediaries in complex multi-step workflows, skillfully managing functions like input parsing, task routing, and API interactions based on user intent, all while minimizing latency and operational costs. Benchmark results reveal that les Ministraux consistently exceed the performance of similar models across a variety of tasks, solidifying their position in the market. As of October 16, 2024, these models are now available for developers and businesses, with Ministral 8B being offered at a competitive rate of $0.1 for every million tokens utilized. This pricing structure enhances accessibility for users looking to integrate advanced AI capabilities into their solutions.

This repository showcases the research preview of LongLLaMA, an advanced large language model that can manage extensive contexts of up to 256,000 tokens or potentially more. LongLLaMA is developed on the OpenLLaMA framework and has been fine-tuned utilizing the Focused Transformer (FoT) technique. The underlying code for LongLLaMA is derived from Code Llama. We are releasing a smaller 3B base variant of the LongLLaMA model, which is not instruction-tuned, under an open license (Apache 2.0), along with inference code that accommodates longer contexts available on Hugging Face. This model's weights can seamlessly replace LLaMA in existing systems designed for shorter contexts, specifically those handling up to 2048 tokens. Furthermore, we include evaluation results along with comparisons to the original OpenLLaMA models, thereby providing a comprehensive overview of LongLLaMA's capabilities in the realm of long-context processing.

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.

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.

We have developed a model known as ChatGPT that engages users in dialogue. This conversational structure allows ChatGPT to effectively respond to follow-up inquiries, acknowledge errors, question faulty assumptions, and decline unsuitable requests. InstructGPT, a related model, focuses on adhering to specific instructions given in prompts and delivering comprehensive answers. ChatGPT Plus is a premium subscription service designed for ChatGPT, the conversational AI. The subscription costs $20 per month, offering subscribers several advantages: - Uninterrupted access to ChatGPT, even during high-demand periods - Accelerated response times - Access to GPT-4 - Integration of ChatGPT plugins - Capability for web-browsing with ChatGPT - Priority for new features and enhancements Currently, ChatGPT Plus is accessible to users in the United States, with plans to gradually invite individuals from our waitlist in the upcoming weeks. We also aim to broaden access and support to more countries and regions in the near future, ensuring that a wider audience can experience its benefits.

Mistral AI has launched two cutting-edge models designed for on-device computing and edge applications, referred to as "les Ministraux": Ministral 3B and Ministral 8B. These innovative models redefine the standards of knowledge, commonsense reasoning, function-calling, and efficiency within the sub-10B category. They are versatile enough to be utilized or customized for a wide range of applications, including managing complex workflows and developing specialized task-focused workers. Capable of handling up to 128k context length (with the current version supporting 32k on vLLM), Ministral 8B also incorporates a unique interleaved sliding-window attention mechanism to enhance both speed and memory efficiency during inference. Designed for low-latency and compute-efficient solutions, these models excel in scenarios such as offline translation, smart assistants that don't rely on internet connectivity, local data analysis, and autonomous robotics. Moreover, when paired with larger language models like Mistral Large, les Ministraux can effectively function as streamlined intermediaries, facilitating function-calling within intricate multi-step workflows, thereby expanding their applicability across various domains. This combination not only enhances performance but also broadens the scope of what can be achieved with AI in edge computing.