Best AI Models in the USA - Page 16

The Hy3 preview represents Tencent Hy's most advanced model in the Hy series to date, featuring a substantial 295 billion parameters in a Mixture-of-Experts structure, with 21 billion parameters activated and an impressive 3.8 billion parameters dedicated to the MTP layer, all while accommodating a context window of up to 256,000 tokens. This groundbreaking model is the first to harness Tencent Hy's newly revamped infrastructure, aimed at enhancing practical applications in areas such as complex reasoning, following instructions, learning from context, coding tasks, and overall inference capabilities. By seamlessly integrating both rapid and thorough cognitive processing, it provides straightforward answers for simpler inquiries while facilitating in-depth analysis for intricate math, programming, and reasoning challenges. The model is crafted to exhibit comprehensive skills in understanding long contexts, adhering to instructions, employing tools, and executing agent workflows, with assessments conducted not only against conventional benchmarks but also within real-world business and development contexts. Furthermore, its design ensures adaptability to a wide range of scenarios, thereby broadening its usability in diverse applications.

Ornith-1.0 represents an innovative family of models tailored specifically for coding tasks that require agentic capabilities. This family encompasses a wide range of models, from the compact 9B Dense versions ideal for deployment on edge devices to the expansive 397B MoE frontier-scale models designed for peak performance, including variants such as 9B Dense, 31B Dense, 35B MoE, and 397B MoE. Built upon the foundational strengths of pretrained models like Gemma 4 and Qwen 3.5, Ornith-1.0 excels in achieving top-tier performance among open-source models that are similar in size when evaluated against coding benchmarks. A significant breakthrough of this model is its self-improving training framework, which effectively learns to produce both solution rollouts and the tailored scaffolds that direct those rollouts. Rather than depending on static, human-crafted harnesses, Ornith-1.0 perceives the scaffold as a dynamic entity that evolves alongside the policy, enabling the model to optimize both the orchestration of tasks and the resulting solutions in tandem. This dual optimization approach enhances the model's adaptability and effectiveness in real-world coding scenarios.

TabFM is an innovative zero-shot foundation model specifically created for handling tabular data, aimed at streamlining classification and regression processes that usually necessitate extensive manual model training, hyperparameter optimization, and tailored feature engineering. By transforming the challenge of tabular prediction into an in-context learning task, TabFM avoids the need to train a new supervised model for every dataset; instead, it consolidates historical training examples and target testing rows into a single cohesive prompt, allowing it to discern the intricate relationships between various columns and rows during inference. Given that tables are inherently two-dimensional and do not rely on a specific order, TabFM employs a hybrid architecture that integrates alternating attention mechanisms for both rows and columns, row compression techniques, and a specialized Transformer designed for in-context learning based on these compressed row embeddings. This sophisticated framework enables the model to effectively capture complex interactions and dependencies among features while maintaining computational efficiency, particularly advantageous for processing larger datasets. Furthermore, this approach not only enhances performance but also significantly reduces the time and resources typically required for model development in tabular data tasks.

Leanstral 1.5 is a model licensed under Apache-2.0, designed for effective proof engineering in Lean 4, aimed at enhancing the capabilities and accessibility of formal verification. It boasts a total of 119 billion parameters, with 6 billion of them being active, marking a significant improvement in performance for tasks such as theorem proving, agent-based proof engineering, and the verification of practical code. The development of Leanstral 1.5 involved a comprehensive three-stage training process, which included mid-training, supervised fine-tuning, and reinforcement learning utilizing CISPO. In a multiturn environment, the model is tasked with receiving a theorem statement, submitting a proof, and refining its approach based on feedback from the Lean compiler until the proof is either successfully compiled or the available resources are depleted. In the code agent setting, Leanstral functions similarly to a developer navigating a raw filesystem, allowing it to edit files, execute bash commands, and interact with the Lean language server to monitor goals, errors, and type information in real time. This innovative approach not only streamlines the proof engineering process but also significantly enhances the user experience in formal verification tasks.

Reve 2.1 represents a significant advancement in visual intelligence and global knowledge, emerging just a month after its predecessor, Reve 2.0. This updated model builds upon the same foundation of controllability but enhances it at every level through improved intuitive prompt comprehension, better rendering of foreign text, and more accurate native 4K outputs. It offers a more detailed approach to planning, demonstrates heightened reasoning capabilities regarding the relationships between elements, and achieves superior precision with full 16-megapixel resolution outputs. The model is designed under the premise that images should resemble code, featuring hierarchical layouts and controllable regions, thus integrating layout planning directly into visual intelligence. By considering structure, hierarchy, and spatial relationships prior to rendering, Reve 2.1 excels in handling complex scenes, intricate compositions, and detailed visual instructions. Additionally, it provides precision editing capabilities, allowing users to address and modify every element individually, which enhances creative control and flexibility. Overall, Reve 2.1 redefines the possibilities of image generation and manipulation, pushing the boundaries of what is achievable in visual technology.

Gemini 3.5 Flash-Lite stands out as the quickest model within Google's Gemini 3.5 lineup, specifically engineered for tasks requiring low latency and for enhancing developer workflows that demand high throughput, including agentic search, document processing, coding, and extensive data analysis. It boasts an impressive output capacity of 350 tokens per second and marks a significant enhancement over earlier Flash-Lite iterations in terms of both quality and agentic capabilities. Developers have the flexibility to adjust the model's thinking level to suit the demands of the task at hand: minimal or low thinking allows for rapid processing of large volumes, while elevated thinking levels accommodate more intricate, multi-step workflows involving subagents. Furthermore, the model is equipped with built-in computational skills, enabling it to interact effectively with various digital environments across compatible platforms. Additionally, Gemini 3.5 Flash-Lite excels in coding, comprehending long contexts, and executing real-world tasks, consistently outperforming its predecessor, Gemini 3.1 Flash-Lite, in critical assessments and even exceeding the performance of Gemini 3 Flash on multiple benchmarks related to agentic functions and software engineering. This impressive performance highlights its potential to transform how developers approach complex workflows and data-intensive tasks.

GPT-Realtime-2.1 is an OpenAI realtime model designed for advanced voice-agent and speech-to-speech AI applications. It improves on GPT-Realtime-2 with stronger alphanumeric recognition, better silence and noise handling, and more natural interruption behavior. The model supports text, audio, and image inputs, while producing text and audio outputs for interactive realtime experiences. Developers can use GPT-Realtime-2.1 across endpoints such as Chat Completions, Responses, Realtime, realtime translation, realtime transcription sessions, and related OpenAI API workflows. The model supports function calling, configurable reasoning effort, instruction following, and reasoning token support for complex voice-agent tasks. Its 128,000-token context window and 32,000-token maximum output make it suitable for longer conversations and more detailed realtime workflows. GPT-Realtime-2.1 does not support video, structured outputs, fine-tuning, or predicted outputs according to OpenAI’s current documentation. Pricing starts at $4 per 1 million text input tokens and $24 per 1 million text output tokens, with separate pricing for audio and image tokens. By combining realtime audio interaction, reasoning, tool use, and multimodal input, GPT-Realtime-2.1 helps developers build responsive AI agents for support, sales, operations, translation, transcription, and interactive voice applications.

WorldClaw represents a comprehensive framework that facilitates the generation of expansive, freely navigable, and modifiable 3D open worlds, all derived from open-ended text prompts. Instead of constructing an entire world in one go, it employs a strategy that transitions from a broad overview to detailed regional features, ensuring spatial coherence while enriching local attributes. Initially, planning agents convert the text prompt into a structured scene specification that includes regions, terrain, assets, materials, visual aesthetics, and spatial arrangements. It establishes a globally consistent terrain base by utilizing semantic layouts, reusable assets, generative or procedural materials, and height fields that are sensitive to regional context. For areas that demand more intricate detail, WorldClaw generates terrain-conditioned compositions, reconstructs editable textured meshes, and appropriately places them within the scene. Subsequently, render-based agents enhance the terrain geometry, fine-tune object appearances, optimize arrangements, and ensure proper interactions with the surrounding environment. This multi-layered approach allows for both the creation of vast landscapes and the intricate detailing needed for immersive exploration.

Happy Shrimp 1.0 is an innovative AI music creation tool that transforms abstract concepts into fully realized songs based on a single user input. This model allows users to initiate the musical process with an emotion, narrative theme, preferred genre, or artistic vision, without requiring any specialized knowledge in music theory like BPM, key, or instrumentation. It excels in generating both vocal tracks and instrumental pieces, crafting melodies, arrangements, lyrics, and vocals either completely from scratch or by incorporating user-provided lyrics. With a vast understanding of musical styles, it adeptly navigates creative aesthetics across a wide array of genres, cultures, and historical periods, effectively converting descriptive ideas into well-structured musical pieces. The model can produce music in various styles, including but not limited to Chinese music, pop, R&B, soul, hip hop, rock, funk, electronic, classical, and jazz. By leveraging its extensive knowledge of the world and music theory, the model elegantly represents the underlying structure and "grammar" of music, enabling it to create compositions that resonate with listeners on multiple levels. In essence, Happy Shrimp 1.0 is a bridge between imagination and sound, opening doors for anyone to bring their musical visions to life.

Apodex 1.1 serves as an online platform designed for tackling intricate research and professional projects, shifting the focus of reasoning from reports to the actual execution of tasks. This tool is engineered to guide users through a comprehensive workflow, incorporating file management, search capabilities, code execution, tool interactions, and coordination among Agent Teams from inception to completion. Users can upload various resources such as research papers, datasets, spreadsheets, images, and code snippets, allowing the system to read and manipulate these files effectively. It autonomously writes and executes analysis scripts, evaluates interim results, adjusts its strategy as needed, and connects conclusions back to the original data and materials. Apodex 1.1 is adept at maintaining the progress of tasks throughout extensive workflows, accommodating new feedback during the execution phase, recovering from any setbacks, and ensuring that the plan, steps, artifacts, dependencies, exceptions, and subsequent actions are all clearly visible. Additionally, in Deep Discover mode, an asynchronous Agent Team divides work into parallel subtasks, consistently channeling valuable insights back into the primary task, ultimately enhancing the efficiency and effectiveness of the research process. This innovative approach not only streamlines workflows but also fosters a deeper understanding of the data being utilized.

Jev is TypeSafe AI’s first public System One Model, a class of AI designed to make fast, structured decisions that software can consume directly. Instead of generating arbitrary strings like a traditional large language model, Jev produces predefined type-safe values accompanied by calibrated probabilities and confidence estimates. Its architecture generates outputs in parallel rather than autoregressively producing one token at a time, allowing the model to prioritize speed and computational efficiency. TypeSafe trains Jev using Reinforcement Learning for Calibrated Decisions, an approach intended to optimize for accurate uncertainty estimates and consistent structured outputs. The model can be embedded into conventional software as an intelligent decision layer for classification, scoring, routing, extraction, branching, and other tasks where hand-written rules would be too rigid. Jev can also be used to judge, verify, guardrail, or detect problematic behavior in outputs from other AI systems. TypeSafe reports typical end-to-end response times between 70 and 500 milliseconds and positions the model for applications where low latency is important. The company also emphasizes schema guarantees, meaning Jev’s outputs are constrained to the structures defined by the application rather than requiring developers to parse and validate unrestricted generated text. Jev is aimed at developers and organizations building automation, real-time software, large-scale data workflows, and production systems that require dependable structured AI decisions.

MiMo-V2.6-Pro-UltraSpeed is Xiaomi MiMo’s accelerated serving option for MiMo-V2.6-Pro, built for applications that require very high output speed without changing the underlying model quality. Xiaomi states that UltraSpeed can generate output at up to 20 times the speed of the standard MiMo-V2.6-Pro configuration. The model retains MiMo-V2.6-Pro’s natively omnimodal capabilities across coding, agentic workflows, visual reasoning, computer use, and research. Developers can use it for long-horizon software engineering, automation, debugging, tool-driven tasks, and other workloads that benefit from rapid model responses. Its multimodal abilities also support frontend generation, presentation creation, 3D modeling, interactive environments, and visual feedback loops. The broader MiMo-V2.6 architecture combines coding capabilities with 3D spatial reasoning, multimodal perception, and computer-use agent functionality. Xiaomi positions UltraSpeed for real-time interaction and other workflows where response latency is especially important. The accelerated model is offered through MiMo Desktop and can also be called through the Xiaomi MiMo API Platform. MiMo-V2.6-Pro-UltraSpeed is intended for developers and organizations that prioritize maximum generation speed while retaining the capabilities of Xiaomi’s higher-end MiMo-V2.6-Pro model.

Aikido Altar represents a cutting-edge open-weight security framework designed to empower organizations with advanced defensive security intelligence tailored for their infrastructures. This model is particularly suited for environments requiring sovereign security where sensitive assets like source code, architectural documents, vulnerability assessments, and other confidential information must remain within the organization's confines and not be shared with external inference services. Built on the GLM-5.3 architecture, Altar employs techniques such as quantization and expert pruning to compress the model size from an extensive 1.51 TB in full precision down to a more manageable 328 GB, all while maintaining the majority of the original model's reasoning and security features. The model retains 168 out of the original 256 routed experts in each backbone expert layer and adopts a W4A16 representation, enhancing its practicality for security tasks that require handling extensive and evolving context windows. The calibration of expert selection was conducted using internal pentesting data and multilingual sources, ensuring that no client information was utilized, which upholds the integrity of cybersecurity, programming, and linguistic capabilities. This innovative approach not only streamlines deployment but also fortifies the organization's security posture against emerging threats.

Holo4 is H Company's series of generalist computer-use and agentic AI models built to perform multi-step work across software interfaces. It is available as Holo4 27B, a dense 27-billion-parameter model, and Holo4 35B-A3B, a Mixture-of-Experts model containing 35 billion total parameters with 3 billion active. Holo4 can interact with applications by clicking and typing through graphical interfaces, writing and executing code, or calling MCP and API tools. The same model can operate across desktops, websites, Android devices, code sandboxes, and business APIs without requiring developers to select a separate specialized model for each environment. H Company trained Holo4 using 127 billion supervised fine-tuning tokens, with approximately three-quarters consisting of successful agentic trajectories spanning desktop, web, MCP/API, and mobile tasks. Reinforcement learning then trained separate experts for desktop and web interaction and for terminal, MCP, and API work before merging them into a single model. Holo4 27B scored 85.2% on OSWorld, 61.7% on OSWorld 2.0, 45.4% on AutomationBench, and 85.1% on AndroidWorld in the evaluations reported by H Company. The models support a 256K context window, with the 27B model positioned for greater accuracy on long multi-step tasks and the 35B-A3B version positioned as a faster and less expensive alternative. Holo4 is available through a hosted API and downloadable model weights, enabling developers and enterprises to build agents that perform workflows spanning multiple applications and interaction methods.

Mercury represents an advanced family of diffusion large language models engineered to achieve top-tier LLM performance at remarkably fast speeds, processing over 1,000 tokens per second on commercial NVIDIA GPUs for immediate AI applications. These models are compatible with OpenAI and are designed to seamlessly replace traditional LLMs, facilitating easier integration into current AI frameworks. Among them, Mercury 2.5 stands out as the most sophisticated reasoning diffusion LLM, tailored for intricate applications where both performance and quality are priorities. It boasts a substantial 260K context window, enabling advanced reasoning, tool utilization, and structured output, with practical applications ranging from swift coding cycles to the development of agents, customer support solutions, and enterprise-level search functionalities. Additionally, Mercury Voice is specifically fine-tuned for voice agents, achieving a remarkable time-to-first-token of under 170 ms and supporting reasoning, tool use, structured output, and a 128K context window. This makes it highly suitable for various applications, including customer support, patient care, educational tools, and gaming experiences. Overall, the Mercury family is focused on pushing the boundaries of what AI can accomplish in real-time environments.

RoBERTa enhances the language masking approach established by BERT, where the model is designed to predict segments of text that have been deliberately concealed within unannotated language samples. Developed using PyTorch, RoBERTa makes significant adjustments to BERT's key hyperparameters, such as eliminating the next-sentence prediction task and utilizing larger mini-batches along with elevated learning rates. These modifications enable RoBERTa to excel in the masked language modeling task more effectively than BERT, resulting in superior performance in various downstream applications. Furthermore, we examine the benefits of training RoBERTa on a substantially larger dataset over an extended duration compared to BERT, incorporating both existing unannotated NLP datasets and CC-News, a new collection sourced from publicly available news articles. This comprehensive approach allows for a more robust and nuanced understanding of language.

ESMFold demonstrates how artificial intelligence can equip us with innovative instruments to explore the natural world, akin to the way the microscope revolutionized our perception by allowing us to observe the minute details of life. Through AI, we can gain a fresh perspective on the vast array of biological diversity, enhancing our comprehension of life sciences. A significant portion of AI research has been dedicated to enabling machines to interpret the world in a manner reminiscent of human understanding. However, the complex language of proteins remains largely inaccessible to humans and has proven challenging for even the most advanced computational systems. Nevertheless, AI holds the promise of unlocking this intricate language, facilitating our grasp of biological processes. Exploring AI within the realm of biology not only enriches our understanding of life sciences but also sheds light on the broader implications of artificial intelligence itself. Our research highlights the interconnectedness of various fields: the large language models powering advancements in machine translation, natural language processing, speech recognition, and image synthesis also possess the capability to assimilate profound insights about biological systems. This cross-disciplinary approach could pave the way for unprecedented discoveries in both AI and biology.

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.

Our platform is designed alongside groundbreaking scientific advancements that uncover how individuals perceive and articulate over 30 unique emotions. The ability to comprehend and convey emotions effectively is essential for the advancement of voice assistants, health technologies, social media platforms, and numerous other fields. It is vital that AI applications are rooted in collaborative, thorough, and inclusive scientific practices. Treating human emotions as mere tools for AI's objectives must be avoided, ensuring that the advantages of AI are accessible to individuals from a variety of backgrounds. Those impacted by AI should possess sufficient information to make informed choices regarding its implementation. Furthermore, the deployment of AI must occur only with the explicit and informed consent of those it influences, fostering a greater sense of trust and ethical responsibility in its use. Ultimately, prioritizing emotional intelligence in AI development will enrich user experiences and enhance interpersonal connections.

FreedomGPT represents an entirely uncensored and private AI chatbot developed by Age of AI, LLC. Our venture capital firm is dedicated to investing in emerging companies that will shape the future of Artificial Intelligence, while prioritizing transparency as a fundamental principle. We are convinced that AI has the potential to significantly enhance the quality of life for people around the globe, provided it is utilized in a responsible manner that prioritizes individual liberties. This chatbot was designed to illustrate the essential need for AI that is free from bias and censorship, emphasizing the importance of complete privacy. As generative AI evolves to become an extension of human thought, it is crucial that it remains shielded from involuntary exposure to others. A key component of our investment strategy at Age of AI is the belief that individuals and organizations alike will require their own private large language models. By supporting companies that focus on this vision, we aim to transform various sectors and ensure that personalized AI becomes an integral part of everyday life.

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.

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.

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.

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.

Experience unparalleled security and privacy along with the most advanced iteration of ChatGPT to date. 1. Customer data and prompts are excluded from model training processes. 2. Data is securely encrypted both at rest using AES-256 and during transit with TLS 1.2 or higher. 3. Compliance with SOC 2 standards is ensured. 4. A dedicated admin console simplifies bulk management of members. 5. Features like SSO and Domain Verification enhance security. 6. An analytics dashboard provides insights into usage patterns. 7. Users enjoy unlimited, high-speed access to GPT-4 alongside Advanced Data Analysis capabilities*. 8. With 32k token context windows, you can input four times longer texts and retain memory. 9. Easily shareable chat templates facilitate collaboration within your organization. 10. This comprehensive suite of features ensures that your team operates seamlessly and securely.