Best AI Models - Page 26
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Hy4
Tencent
Hy4 preview is a new-generation open source Mixture-of-Experts flagship model built for real-world productivity tasks across software engineering, office work, game development, and scientific research. The model contains 770B total parameters with 49B activated per token and supports a 1M-token context window, giving it the capacity to work through large codebases, extensive document collections, and long multi-step tasks. Its 78-layer architecture combines Gated DeepSeek Sparse Attention with IndexCache for cross-layer sparse index reuse and identity Hyper-Connections to expand information flow between layers. A native Multi-Token Prediction layer is included for speculative decoding. Hy4 preview is designed to understand, plan, debug, and verify long-horizon engineering tasks, with additional gains in front-end visual quality and interaction design. -
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TimesFM-3
Google
TimesFM-3 is a state-of-the-art time series foundation model designed for highly accurate multivariate forecasting in a single forward pass. The 330 million parameter model is pre-trained on a real-world and synthetic time-series corpus comprising more than 1 trillion time points, building on the efficiency and zero-shot generalization of earlier TimesFM models. It can jointly predict multiple coevolving time series and capture dependencies that improve accuracy without task-specific fine-tuning. The model supports multiple targets with point and quantile forecasts, past covariates that are known only historically, and past-future dynamic covariates such as planned promotions, holidays, or weather forecasts. TimesFM-3 uses a decoder-only transformer architecture, processes contiguous data in patches of 32 time steps, and applies alternating causal temporal attention and full variate attention to combine patterns across time and related series. -
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Gemini 3.8 Flash Cyber
Google
Gemini 3.8 Flash Cyber is Google’s most capable cybersecurity model, providing frontier-level performance in vulnerability detection and automated patching with the speed needed for quick iteration. It is designed specifically for trusted defenders and is available through the Fairwind Program. On CyberGym, a standard industry benchmark for finding vulnerabilities, the model demonstrates frontier-level autonomous vulnerability discovery and surpasses both Gemini 3.5 Flash Cyber and significantly larger frontier models. Google also evaluated it on an internal benchmark covering complex codebases across 20 programming languages, where it achieved a success rate exceeding 70% in discovering a wide range of vulnerabilities. Gemini 3.8 Flash Cyber prioritizes vulnerability fixing over offensive capabilities such as exploitation, equipping defenders with expert capabilities that can help them maintain an advantage over attackers. -
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Atlas by World Labs
World Labs
Atlas is a next-generation omni world model for spatial intelligence that natively operates on text, images, video, and 3D. Built as a multimodal autoregressive diffusion transformer, it combines inputs into a shared spatial context and generates what comes next while staying consistent in 3D with everything it has seen and imagining what lies beyond it. Atlas supports world generation, reconstruction, and simulation across a broad range of tasks. It can generate images and videos from one or more reference images with pixel-perfect camera control, producing long, coherent videos with manually designed camera paths. For spatial reconstruction, Atlas can recreate real-world scenes from sparse input images, generate novel views, and produce explicit 3D outputs such as point clouds and 3D Gaussian splats. More input views provide additional context, allowing the model to reduce imagination and create increasingly faithful reconstructions. Atlas also models how worlds evolve over time. -
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K2 Horizon
Institute of Foundation Models
K2 Horizon is a connected fleet of six open models spanning 375B-A23B, 36B-A4B, 32B, 7B, 3.7B, and 0.9B, designed to deliver strong performance across reasoning, mathematics, coding, agentic tasks, and general capabilities. The models share core architecture, vocabulary, training methodology, interfaces, evaluation infrastructure, and deployment tooling, making it easier to move between sizes and route workloads dynamically. The 375B-A23B model is the fleet’s most capable option for complex reasoning, software engineering, research, and long-horizon agentic work, while the 32B and 36B-A4B models target powerful local deployment. The 36B-A4B model introduces Mixture-of-Value Attention, combining sparse attention with Mixture-of-Experts layers to activate about 4 billion parameters per token while approaching the performance of the dense 32B model. -
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MAI-Transcribe-2
Microsoft AI
MAI-Transcribe-2 is Microsoft AI’s most capable transcription model yet, designed to deliver fast, accurate speech recognition across a broad range of real-world audio. It supports speaker diarization to distinguish speakers and attribute words to the right person, along with word-level timestamps for precise alignment, search, navigation, and editing. Keyword biasing helps recognize domain-specific terminology, abbreviations, names, and other terms that can be difficult to distinguish from context alone. Developers can choose between configurable transcription styles: a verbatim setting that preserves filler words and false starts for compliance and analysis, or a clean setting that removes fillers for more readable captions, notes, and published transcripts. The model supports code-switching for conversations that naturally move between languages, including blended language pairs such as Hinglish and Spanglish, and can automatically identify the language being spoken. -
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Mercury 2.5
Inception
Mercury 2.5 is Inception’s most capable production model yet and a significant step up in quality over Mercury 2 while maintaining the same low-latency serving profile. It is the most capable diffusion LLM on the market and, according to Inception, the largest diffusion language model ever trained. Mercury 2.5 delivers a 40% increase in intelligence over Mercury 2, with performance comparable to cost-optimized frontier models such as GPT-5.6 Luna (Low), Gemini 3.5 Flash-Lite, and Claude Haiku 4.5. It generates at 1,107 tokens per second on widely available NVIDIA GPUs and supports a 260K-token context window. Capabilities include tunable reasoning, parallel tool calls, and schema-aligned JSON. The model is designed for latency-sensitive workloads where many model calls may happen inside a single interaction. In search agents and RAG pipelines, it can support planning, query rewriting, reranking, fact structuring, source summarization, and answer checking while keeping calls fast. -
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StepAudio 3
StepFun
StepAudio 3 is StepFun’s next-generation audio model family, built to understand, generate, and interact through voice, sound, and music. The lineup includes StepAudio 3 Realtime for natural full-duplex conversation, StepAudio 3 ASR for speech recognition, StepAudio 3 TTS for speech synthesis, StepAudio 3 Gen for general-purpose audio generation, and StepAudio 3 Music for long-form music creation. Realtime is designed around a continuous listen-converse-think-act loop, understanding not only words but also hesitation, laughter, emotion, pauses, backchannels, and interruptions. It can think while speaking, reason through harder questions without breaking conversational flow, and use tools to complete tasks once it understands the user’s intent. StepAudio 3 Gen unifies zero-shot TTS, voice design, vocal generation, sound effects, music, and mixed audio generation within one framework, while StepAudio 3 Music supports text-controlled songs, instrumentals, vocal arrangement, and more. -
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Qwen3.8-Omni-Flash
Alibaba
Qwen3.8-Omni-Flash is a next-generation native omnimodal model designed to strengthen agent capabilities in real-world productivity scenarios, advancing from understanding multimodal content to planning tasks, calling tools, and completing creative work. Built on the Qwen3.8-Flash-Next architecture, it accepts text, image, audio, and video inputs with a context window of up to 1 million tokens while maintaining strong text performance. Beyond coding, knowledge work, and GUI interaction, it extends agentic workflows centered on audio and video, including video editing, music video creation, film production and commentary, audiovisual summarization, and real-time conversations. The model improves long-form audio and audiovisual understanding through controllable descriptions, agentic evidence gathering, meeting understanding, and video-centered deep research. Users can specify the subject, time range, level of detail, and output format for video analysis, enabling overviews, etc. -
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Gemini 3.8 Flash TTS
Google
Gemini 3.8 Flash TTS is Google’s expressive text-to-speech model for creating custom voices, directed performances, and multilingual audio experiences. The model can generate original voices from natural-language prompts by specifying characteristics such as role, accent, tone, pacing, and vocal style across more than 100 languages and dialects. Users can also replicate an authorized voice from a short audio sample, with consent verification, SynthID watermarking, and C2PA credentials supporting responsible voice creation. Gemini 3.8 Flash TTS provides line-by-line performance control, long-form speech generation, two-speaker scene staging, and support for nonverbal cues such as laughs, sighs, gasps, and conversational backchanneling. It is suited to use cases including games, audiobooks, podcasts, dubbing, interactive voice agents, branded audio, and media localization. -
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Gemini 3.8 Flash-Lite TTS
Google
Gemini 3.8 Flash-Lite TTS is Google’s cost-efficient text-to-speech model designed for high-volume dubbing, audio production, and expressive voice-agent applications. The model provides fine-grained control over tone, pacing, expressive nuance, and line-by-line vocal delivery. It supports long-form speech generation while maintaining natural pacing, voice quality, and speaker consistency across extended content. Native two-speaker scene staging enables multi-turn dialogue with distinct voices and natural conversational turn-taking from a single script. Gemini 3.8 Flash-Lite TTS supports more than 100 languages and can add nonverbal cues and backchanneling to create more natural conversational audio. The model is available through the Gemini API and Google AI Studio, with deployment through Google Vids and planned enterprise availability through Gemini Enterprise. -
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FLUX 3 Action
Black Forest Labs
FLUX 3 Action is an open-weight 7B world-action model designed for action prediction in robotics and other latency-sensitive visual environments. Derived from the multimodal FLUX 3 backbone, it was pretrained on large-scale image, video, and audio data with a strong emphasis on video, then adapted through joint video-action training and fine-tuning for specific embodiments and action spaces. Given an instruction, camera observations, and robot joint positions, the model predicts motor commands together with their expected visual outcomes, allowing a robot to execute actions, observe the environment again, and continuously replan. Unlike approaches that separate visual prediction from control, FLUX 3 Action jointly models future video and actions, transferring world understanding learned from broad video pretraining into robot control. Its single-step 7B checkpoint reaches a 38.3% success rate on RoboLab-120. -
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MAI-Transcribe-2-Streaming
Microsoft AI
MAI-Transcribe-2-Streaming is a low-latency streaming transcription model built for real-time speech applications, delivering transcripts in 60 languages with automatic, continuous language detection. Rather than waiting for someone to finish speaking, it produces initial partial transcripts just over 100 ms after receiving audio, revises them as more context arrives, and commits stable text quickly. This allows voice applications to begin reasoning, calling tools, or displaying live transcripts while a person is still speaking. Microsoft reports that the model ranks No. 1 for both final and partial transcript accuracy on Artificial Analysis. MAI-Voice-2.1 complements it with multilingual text-to-speech across 23 languages and 26 locales, allowing a single voice to switch languages while maintaining the same speaker identity and adopting native accents. -
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Beam
Reflection
Beam is Reflection’s first open-weight model, a sparse Mixture-of-Experts model with 501 billion total parameters and 23 billion active parameters, built for coding, reasoning, and agentic workloads. Its capabilities come from large-scale pretraining and reinforcement learning, including training on 23.8 trillion diverse, curated, high-quality tokens from the web, public sources, and proprietary licensed datasets. Beam was trained with a particular focus on coding and agentic performance and is designed to deliver competitive open-weight capabilities with efficient inference compute. It supports complex software engineering, terminal, STEM, web search, tool-use, and general knowledge tasks, with reinforcement learning designed to improve multi-step reasoning, tool use, and adaptation to environment feedback. Users can control the tradeoff between performance and token usage through a reasoning effort parameter. -
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Genie 4
Google
Genie 4 is a generative AI world model developed by Google DeepMind that creates realistic, interactive virtual environments in real time. The model generates explorable worlds with dynamic visuals, native audio, physical interactions, and responsive human characters. Users can navigate generated environments using keyboard controls and modify scenes through natural language instructions, including changing weather conditions, moving cameras, and introducing objects. Its physics simulation capabilities model phenomena such as aerodynamics, collisions, and natural human movement. Genie 4 integrates with Google's Gemini models to support complex reasoning, multistep event simulation, and interactive environments that respond coherently to user actions. The model is available as a limited research preview for trusted testers, with planned access through Project Genie, Google AI Studio, and the Gemini API. -
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Timestral
Mistral AI
Timestral is a family of time-series foundation models and a Python inference package for generating multivariate probabilistic forecasts from historical sequences. The four model sizes (5M, 28M, 123M and 209M parameters) support past-only, known-future and categorical covariates, return quantile forecasts, and accept context windows up to 32,768 time steps. Developers can load pretrained weights from the Hugging Face Hub or from local files and run inference on CPU or GPU, then incorporate forecasts into monitoring, capacity-planning, energy, supply-chain, financial-risk, or maintenance workflows. Timestral supports zero-shot forecasting and can be adapted to domain data through Mistral Forge. The package is intended for data scientists, machine-learning engineers, and teams building forecasting systems; repository code is Apache 2.0, while model weights use a separate Modified MIT license. -
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BLOOM
BigScience
BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks. -
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NVIDIA NeMo Megatron
NVIDIA
NVIDIA NeMo Megatron is an end-to-end framework for training and deploying LLMs with billions and trillions of parameters. NVIDIA NeMo Megatron, part of the NVIDIA AI platform, offers an easy, efficient, and cost-effective containerized framework to build and deploy LLMs. Designed for enterprise application development, it builds upon the most advanced technologies from NVIDIA research and provides an end-to-end workflow for automated distributed data processing, training large-scale customized GPT-3, T5, and multilingual T5 (mT5) models, and deploying models for inference at scale. Harnessing the power of LLMs is made easy through validated and converged recipes with predefined configurations for training and inference. Customizing models is simplified by the hyperparameter tool, which automatically searches for the best hyperparameter configurations and performance for training and inference on any given distributed GPU cluster configuration. -
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ALBERT
Google
ALBERT is a self-supervised Transformer model that was pretrained on a large corpus of English data. This means it does not require manual labelling, and instead uses an automated process to generate inputs and labels from raw texts. It is trained with two distinct objectives in mind. The first is Masked Language Modeling (MLM), which randomly masks 15% of words in the input sentence and requires the model to predict them. This technique differs from RNNs and autoregressive models like GPT as it allows the model to learn bidirectional sentence representations. The second objective is Sentence Ordering Prediction (SOP), which entails predicting the ordering of two consecutive segments of text during pretraining. -
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ERNIE 3.0 Titan
Baidu
Pre-trained language models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. GPT-3 has shown that scaling up pre-trained language models can further exploit their enormous potential. A unified framework named ERNIE 3.0 was recently proposed for pre-training large-scale knowledge enhanced models and trained a model with 10 billion parameters. ERNIE 3.0 outperformed the state-of-the-art models on various NLP tasks. In order to explore the performance of scaling up ERNIE 3.0, we train a hundred-billion-parameter model called ERNIE 3.0 Titan with up to 260 billion parameters on the PaddlePaddle platform. Furthermore, We design a self-supervised adversarial loss and a controllable language modeling loss to make ERNIE 3.0 Titan generate credible and controllable texts. -
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EXAONE
LG
EXAONE is a large language model developed by LG AI Research with the goal of nurturing "Expert AI" in multiple domains. The Expert AI Alliance was formed as a collaborative effort among leading companies in various fields to advance the capabilities of EXAONE. Partner companies within the alliance will serve as mentors, providing skills, knowledge, and data to help EXAONE gain expertise in relevant domains. EXAONE, described as being akin to a college student who has completed general elective courses, requires additional intensive training to become an expert in specific areas. LG AI Research has already demonstrated EXAONE's abilities through real-world applications, such as Tilda, an AI human artist that debuted at New York Fashion Week, as well as AI applications for summarizing customer service conversations and extracting information from complex academic papers. -
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Jurassic-1
AI21 Labs
Jurassic-1 models come in two sizes, where the Jumbo version, at 178B parameters, is the largest and most sophisticated language model ever released for general use by developers. AI21 Studio is currently in open beta, allowing anyone to sign up and immediately start querying Jurassic-1 using our API and interactive web environment. Our mission at AI21 Labs is to fundamentally reimagine the way humans read and write by introducing machines as thought partners, and the only way we can achieve this is if we take on this challenge together. We’ve been researching language models since our Mesozoic Era (aka 2017 😉). Jurassic-1 builds on this research, and it is the first generation of models we’re making available for widespread use. -
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Alpaca
Stanford Center for Research on Foundation Models (CRFM)
Instruction-following models such as GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat have become increasingly powerful. Many users now interact with these models regularly and even use them for work. However, despite their widespread deployment, instruction-following models still have many deficiencies: they can generate false information, propagate social stereotypes, and produce toxic language. To make maximum progress on addressing these pressing problems, it is important for the academic community to engage. Unfortunately, doing research on instruction-following models in academia has been difficult, as there is no easily accessible model that comes close in capabilities to closed-source models such as OpenAI’s text-DaVinci-003. We are releasing our findings about an instruction-following language model, dubbed Alpaca, which is fine-tuned from Meta’s LLaMA 7B model. -
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GradientJ
GradientJ
GradientJ provides everything you need to build large language model applications in minutes and manage them forever. Discover and maintain the best prompts by saving versions and comparing them across benchmark examples. Orchestrate and manage complex applications by chaining prompts and knowledge bases into complex APIs. Enhance the accuracy of your models by integrating them with your proprietary data. -
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PanGu Chat
Huawei
PanGu Chat is an AI chatbot developed by Huawei. PanGu Chat can converse like a human and answer any questions like ChatGPT does. -
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LTM-1
Magic AI
Magic’s LTM-1 enables 50x larger context windows than transformers. Magic's trained a Large Language Model (LLM) that’s able to take in the gigantic amounts of context when generating suggestions. For our coding assistant, this means Magic can now see your entire repository of code. Larger context windows can allow AI models to reference more explicit, factual information and their own action history. We hope to be able to utilize this research to improve reliability and coherence. -
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Reka
Reka
Our enterprise-grade multimodal assistant carefully designed with privacy, security, and efficiency in mind. We train Yasa to read text, images, videos, and tabular data, with more modalities to come. Use it to generate ideas for creative tasks, get answers to basic questions, or derive insights from your internal data. Generate, train, compress, or deploy on-premise with a few simple commands. Use our proprietary algorithms to personalize our model to your data and use cases. We design proprietary algorithms involving retrieval, fine-tuning, self-supervised instruction tuning, and reinforcement learning to tune our model on your datasets. -
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Samsung Gauss
Samsung
Samsung Gauss is a new AI model developed by Samsung Electronics. It is a large language model (LLM) that has been trained on a massive dataset of text and code. Samsung Gauss is able to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Samsung Gauss is still under development, but it has already learned to perform many kinds of tasks, including: Following instructions and completing requests thoughtfully. Answering your questions in a comprehensive and informative way, even if they are open ended, challenging, or strange. Generating different creative text formats, like poems, code, scripts, musical pieces, email, letters, etc. Here are some examples of what Samsung Gauss can do: Translation: Samsung Gauss can translate text between many different languages, including English, French, German, Spanish, Chinese, Japanese, and Korean. Coding: Samsung Gauss can generate code. -
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Flip AI
Flip AI
Our large language model (LLM) can understand and reason through any and all observability data, including unstructured data, so that you can rapidly restore software and systems to health. Our LLM has been trained to understand and mitigate thousands of critical incidents, across every type of architecture imaginable – giving enterprise developers access to the world’s best debugging expert. Our LLM was built to solve the hardest part of the software engineering process – debugging production incidents. Our model requires no training and works on any observability data system. It can learn based on feedback and finetune based on past incidents and patterns in your environment while keeping your data in your boundaries. This means you are resolving critical incidents using Flip in seconds. -
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VideoPoet
Google
VideoPoet is a simple modeling method that can convert any autoregressive language model or large language model (LLM) into a high-quality video generator. It contains a few simple components. An autoregressive language model learns across video, image, audio, and text modalities to autoregressively predict the next video or audio token in the sequence. A mixture of multimodal generative learning objectives are introduced into the LLM training framework, including text-to-video, text-to-image, image-to-video, video frame continuation, video inpainting and outpainting, video stylization, and video-to-audio. Furthermore, such tasks can be composed together for additional zero-shot capabilities. This simple recipe shows that language models can synthesize and edit videos with a high degree of temporal consistency.
