Best AI Models in Brazil - Page 24
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Compare the Top AI Models in Brazil as of July 2026 - Page 24
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1
Microsoft Foundry Models
Microsoft
Microsoft Foundry Models is a unified model catalog that gives enterprises access to more than 11,000 AI models from Microsoft, OpenAI, Anthropic, Mistral AI, Meta, Cohere, DeepSeek, xAI, and others. It allows teams to explore, test, and deploy models quickly using a task-centric discovery experience and integrated playground. Organizations can fine-tune models with ready-to-use pipelines and evaluate performance using their own datasets for more accurate benchmarking. Foundry Models provides secure, scalable deployment options with serverless and managed compute choices tailored to enterprise needs. With built-in governance, compliance, and Azure’s global security framework, businesses can safely operationalize AI across mission-critical workflows. The platform accelerates innovation by enabling developers to build, iterate, and scale AI solutions from one centralized environment. -
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Grok 4.1
SpaceXAI
Grok 4.1 is an advanced AI model developed by Elon Musk’s xAI, designed to push the limits of reasoning and natural language understanding. Built on the powerful Colossus supercomputer, it processes multimodal inputs including text and images, with upcoming support for video. The model delivers exceptional accuracy in scientific, technical, and linguistic tasks. Its architecture enables complex reasoning and nuanced response generation that rivals the best AI systems in the world. Enhanced moderation ensures more responsible and unbiased outputs than earlier versions. Grok 4.1 is a breakthrough in creating AI that can think, interpret, and respond more like a human. -
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FLUX.2
Black Forest Labs
FLUX.2 is built for real production workflows, delivering high-quality visuals while maintaining character, product, and style consistency across multiple reference images. It handles structured prompts, brand-safe layouts, complex text rendering, and detailed logos with precision. The model supports multi-reference inputs, editing at up to 4 megapixels, and generates both photorealistic scenes and highly stylized compositions. With a focus on reliability, FLUX.2 processes real-world creative tasks—such as infographics, product shots, and UI mockups—with exceptional stability. It represents Black Forest Labs’ open-core approach, pairing frontier-level capability with open-weight models that invite experimentation. Across its variants, FLUX.2 provides flexible options for studios, developers, and researchers who need scalable, customizable visual intelligence. -
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Amazon Nova 2 Omni
Amazon
Nova 2 Omni is a fully unified multimodal reasoning and generation model capable of understanding and producing content across text, images, video, and speech. It can take in extremely large inputs, ranging from hundreds of thousands of words to hours of audio and lengthy videos, while maintaining coherent analysis across formats. This allows it to digest full product catalogs, long-form documents, customer testimonials, and complete video libraries all at the same time, giving teams a single system that replaces the need for multiple specialized models. With its ability to handle mixed media in one workflow, Nova 2 Omni opens new possibilities for creative and operational automation. A marketing team, for example, can feed in product specs, brand guidelines, reference images, and video content and instantly generate an entire campaign, including messaging, social content, and visuals, in one pass. -
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Amazon Nova 2 Sonic
Amazon
Nova 2 Sonic is Amazon’s real-time speech-to-speech model designed to deliver natural, flowing voice interactions without relying on separate systems for text and audio. It combines speech recognition, speech generation, and text processing in a single model, enabling smooth, human-like conversations that can shift effortlessly between voice and text. With expanded multilingual support and expressive voice options, it produces responses that sound more lifelike and contextually aware. Its one-million-token context window allows for long, continuous interactions without losing track of prior details. It supports asynchronous task handling, meaning users can continue speaking, change topics, or ask follow-up questions while background tasks, such as searching for information or completing a request, continue uninterrupted. This makes voice experiences feel more fluid and less bound by traditional turn-based dialog constraints. -
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Kling 2.5
Kuaishou Technology
Kling 2.5 is an AI video generation model designed to create high-quality visuals from text or image inputs. It focuses on producing detailed, cinematic video output with smooth motion and strong visual coherence. Kling 2.5 generates silent visuals, allowing creators to add voiceovers, sound effects, and music separately for full creative control. The model supports both text-to-video and image-to-video workflows for flexible content creation. Kling 2.5 excels at scene composition, camera movement, and visual storytelling. It enables creators to bring ideas to life quickly without complex editing tools. Kling 2.5 serves as a powerful foundation for visually rich AI-generated video content. -
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Hunyuan Motion 1.0
Tencent Hunyuan
Hunyuan Motion (also known as HY-Motion 1.0) is a state-of-the-art text-to-3D motion generation AI model that uses a billion-parameter Diffusion Transformer with flow matching to turn natural language prompts into high-quality, skeleton-based 3D character animation in seconds. It understands descriptive text in English and Chinese and produces smooth, physically plausible motion sequences that integrate seamlessly into standard 3D animation pipelines by exporting to skeleton formats such as SMPL or SMPLH and common formats like FBX or BVH for use in Blender, Unity, Unreal Engine, Maya, and other tools. The model’s three-stage training pipeline (large-scale pre-training on thousands of hours of motion data, fine-tuning on curated sequences, and reinforcement learning from human feedback) enhances its ability to follow complex instructions and generate realistic, temporally coherent motion. -
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Molmo 2
Ai2
Molmo 2 is a new suite of state-of-the-art open vision-language models with fully open weights, training data, and training code that extends the original Molmo family’s grounded image understanding to video and multi-image inputs, enabling advanced video understanding, pointing, tracking, dense captioning, and question-answering capabilities; all with strong spatial and temporal reasoning across frames. Molmo 2 includes three variants: an 8 billion-parameter model optimized for overall video grounding and QA, a 4 billion-parameter version designed for efficiency, and a 7 billion-parameter Olmo-backed model offering a fully open end-to-end architecture including the underlying language model. These models outperform earlier Molmo versions on core benchmarks and set new open-model high-water marks for image and video understanding tasks, often competing with substantially larger proprietary systems while training on a fraction of the data used by comparable closed models. -
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Seedance 2.0
ByteDance
Seedance 2.0 is ByteDance’s advanced AI video generation platform built to turn creative inputs into cinematic-quality videos. It supports text prompts, images, audio, and video, blending them into polished visuals with smooth transitions and native sound. The platform uses sophisticated multimodal and motion synthesis to preserve visual consistency and character identity across multiple scenes. Users can combine up to twelve reference assets in a single project, enabling complex storytelling without manual editing. Seedance 2.0 automatically plans camera movement and pacing, giving creators director-level control with minimal effort. The system is capable of producing high-resolution video output, including 1080p and above. Its rapid popularity highlights its ability to generate engaging animated and narrative-driven content from simple inputs. -
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Lyria 3
Google
Lyria 3 is Google DeepMind’s most advanced AI music generation model, designed to create high-fidelity, professional-grade audio from simple prompts. It enables users to describe a track in natural language and refine details such as tempo, vocal style, and instrumentation for greater creative control. The model can generate cohesive songs that flow naturally from start to finish across a wide range of genres and global languages. Lyria 3 also supports image-to-music composition, allowing users to upload visuals and transform them into custom soundtracks. Built with input from musicians and producers, it understands rhythm, arrangement, and musical structure at a deeper level. Users can export crisp, polished tracks suitable for background ambience, content creation, or mainstage productions. Integrated into Gemini and other creative tools, Lyria 3 empowers creators to explore, experiment, and express ideas through AI-driven music. -
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GPT-5.4
OpenAI
GPT-5.4 is an advanced artificial intelligence model developed by OpenAI to support complex professional and technical work. The model combines improvements in reasoning, coding, and agent-based workflows into a single system designed for real-world productivity tasks. GPT-5.4 can generate, analyze, and edit documents, spreadsheets, presentations, and other work outputs with greater accuracy and efficiency. It also features improved tool integration, enabling the model to interact with software environments and external tools to complete multi-step workflows. With enhanced context capabilities supporting up to one million tokens, GPT-5.4 can process and reason over very large amounts of information. The model also improves factual accuracy and reduces errors compared to earlier versions. By combining strong reasoning, coding ability, and tool use, GPT-5.4 helps users complete complex tasks faster and with fewer iterations. -
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MiMo-V2-Omni
Xiaomi Technology
MiMo-V2-Omni is an advanced multimodal AI model designed to handle a wide range of real-world tasks across text, code, and other data formats. It is built to support agentic workflows, enabling seamless execution of complex, multi-step processes. The model integrates strong reasoning, tool usage, and contextual understanding to deliver reliable outputs. With its ability to process diverse inputs, it enhances productivity across development, automation, and enterprise use cases. MiMo-V2-Omni focuses on delivering consistent performance in both general and specialized tasks. -
13
Lyria 3 Pro
Google
Lyria 3 Pro is an advanced AI music generation model developed by Google DeepMind that enables users to create longer, high-quality music tracks with enhanced structure and control. It allows the generation of tracks up to three minutes long, supporting detailed composition elements such as intros, verses, choruses, and bridges. The model is designed to better understand musical structure, making it easier to produce cohesive and dynamic audio outputs. Lyria 3 Pro is integrated across multiple Google platforms, including Gemini Enterprise Agent Platform, Google AI Studio, and the Gemini app. It supports a wide range of use cases, from content creation and video production to large-scale audio generation for businesses. The model also includes safeguards to prevent imitation of specific artists and ensures responsible AI usage through built-in protections and watermarking. Overall, Lyria 3 Pro enhances creative workflows by providing powerful, customizable music generation capabilities. -
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Claude Mythos
Anthropic
Claude Mythos Preview is a highly advanced AI model developed with strong capabilities in cybersecurity, particularly in identifying and exploiting software vulnerabilities. It demonstrates the ability to autonomously discover zero-day vulnerabilities across major operating systems, browsers, and critical software systems. The model can also generate complex exploit chains, including privilege escalation and remote code execution attacks. Its capabilities extend beyond vulnerability detection to reverse engineering and exploit development in both open-source and closed-source environments. Mythos Preview operates through agentic workflows, enabling it to analyze codebases, test hypotheses, and validate exploits independently. These abilities represent a significant leap compared to previous models, which struggled with exploit generation. Overall, Claude Mythos Preview highlights a new era where AI can both strengthen and challenge global cybersecurity practices. -
15
ChatGPT Images 2.0
OpenAI
ChatGPT Images 2.0 is a next-generation AI image generation system developed by OpenAI to create high-quality visuals from text prompts. It introduces advanced visual reasoning, allowing the model to “think” through prompts before generating images. The system significantly improves text rendering, making it possible to include accurate and readable text inside images. It supports multilingual content, enabling users to generate visuals with text in multiple languages. ChatGPT Images 2.0 can produce multiple consistent images from a single prompt, maintaining characters and objects across variations. The model also offers higher resolution outputs and better control over layout and composition. It is designed to move beyond simple image generation into practical design use cases like presentations, marketing visuals, and UI mockups. By combining reasoning with image creation, it delivers more accurate and usable visual results. -
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Grok Voice Think Fast 1.0
SpaceXAI
Grok Voice Think Fast 1.0 is an advanced voice AI model developed by xAI, designed to handle complex, real-world conversational workflows. It excels in multi-step tasks across customer support, sales, and enterprise applications. The model is built for fast, natural conversations while maintaining high accuracy and responsiveness. It supports real-time reasoning without adding latency, allowing it to process and respond intelligently during live interactions. Grok Voice can accurately capture and confirm structured data such as names, addresses, and account details, even in noisy or challenging conditions. It is optimized for global use with support for over 25 languages. The model is capable of handling interruptions, accents, and ambiguous inputs with ease. Overall, it enables businesses to deploy efficient, scalable voice agents for high-volume interactions. -
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TML-interaction-small
Thinking Machines Lab
TML-Interaction-Small is a real-time multimodal interaction model developed by Thinking Machines Lab to enable more natural and collaborative human-AI communication across audio, video, and text. Unlike traditional turn-based AI systems that rely on external scaffolding and delayed interactions, TML-Interaction-Small is designed around continuous micro-turn exchanges that allow the model to perceive, respond, listen, speak, and react simultaneously in real time. The model uses a time-aware architecture that processes 200ms interaction windows, enabling seamless interruptions, simultaneous speech, visual cue detection, and live collaborative workflows without requiring separate dialog management systems. TML-Interaction-Small supports capabilities such as real-time conversation, proactive interjections, live translation, visual monitoring, tool usage, browsing, and asynchronous reasoning through coordination with a background model. -
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Gemini 4
Google
Gemini 4 is Google’s next-generation Gemini model family currently in development after the release of Gemini 3.6 Flash and Gemini 3.5 Flash-Lite. Google has confirmed that pre-training for Gemini 4 has begun, positioning it as the company’s most ambitious model training effort yet. The model is expected to advance Google’s frontier AI work across reasoning, coding, multimodal understanding, agentic workflows, and enterprise AI use cases. Because Gemini 4 has not been publicly released yet, official pricing, model cards, benchmarks, API details, and availability have not been published. Gemini 4 follows Google’s broader Gemini strategy of building models for developers, enterprises, consumer apps, and AI-powered products across Google’s ecosystem. Built for the next stage of AI agents and intelligent applications, Gemini 4 is likely to become a major foundation for future Google AI products once it becomes available. -
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Llama
Meta
Llama (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as Llama enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like Llama is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a Llama model card that details how we built the model in keeping with our approach to Responsible AI practices. -
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OPT
Meta
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models. -
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T5
Google
With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself. -
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PanGu-α
Huawei
PanGu-α is developed under the MindSpore and trained on a cluster of 2048 Ascend 910 AI processors. The training parallelism strategy is implemented based on MindSpore Auto-parallel, which composes five parallelism dimensions to scale the training task to 2048 processors efficiently, including data parallelism, op-level model parallelism, pipeline model parallelism, optimizer model parallelism and rematerialization. To enhance the generalization ability of PanGu-α, we collect 1.1TB high-quality Chinese data from a wide range of domains to pretrain the model. We empirically test the generation ability of PanGu-α in various scenarios including text summarization, question answering, dialogue generation, etc. Moreover, we investigate the effect of model scales on the few-shot performances across a broad range of Chinese NLP tasks. The experimental results demonstrate the superior capabilities of PanGu-α in performing various tasks under few-shot or zero-shot settings. -
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Megatron-Turing
NVIDIA
Megatron-Turing Natural Language Generation model (MT-NLG), is the largest and the most powerful monolithic transformer English language model with 530 billion parameters. This 105-layer, transformer-based MT-NLG improves upon the prior state-of-the-art models in zero-, one-, and few-shot settings. It demonstrates unmatched accuracy in a broad set of natural language tasks such as, Completion prediction, Reading comprehension, Commonsense reasoning, Natural language inferences, Word sense disambiguation, etc. With the intent of accelerating research on the largest English language model till date and enabling customers to experiment, employ and apply such a large language model on downstream language tasks - NVIDIA is pleased to announce an Early Access program for its managed API service to MT-NLG mode. -
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Galactica
Meta
Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. Galactica is a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. -
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PanGu-Σ
Huawei
Significant advancements in the field of natural language processing, understanding, and generation have been achieved through the expansion of large language models. This study introduces a system which utilizes Ascend 910 AI processors and the MindSpore framework to train a language model with over a trillion parameters, specifically 1.085T, named PanGu-{\Sigma}. This model, which builds upon the foundation laid by PanGu-{\alpha}, takes the traditionally dense Transformer model and transforms it into a sparse one using a concept known as Random Routed Experts (RRE). The model was efficiently trained on a dataset of 329 billion tokens using a technique called Expert Computation and Storage Separation (ECSS), leading to a 6.3-fold increase in training throughput via heterogeneous computing. Experimentation indicates that PanGu-{\Sigma} sets a new standard in zero-shot learning for various downstream Chinese NLP tasks. -
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OpenELM
Apple
OpenELM is an open-source language model family developed by Apple. It uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy compared to existing open language models of similar size. OpenELM is trained on publicly available datasets and achieves state-of-the-art performance for its size. -
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LTM-2-mini
Magic AI
LTM-2-mini is a 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels. For each decoded token, LTM-2-mini’s sequence-dimension algorithm is roughly 1000x cheaper than the attention mechanism in Llama 3.1 405B1 for a 100M token context window. The contrast in memory requirements is even larger – running Llama 3.1 405B with a 100M token context requires 638 H100s per user just to store a single 100M token KV cache.2 In contrast, LTM requires a small fraction of a single H100’s HBM per user for the same context. -
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OpenAI o3-mini-high
OpenAI
The o3-mini-high model from OpenAI advances AI reasoning by refining deep problem-solving in coding, mathematics, and complex tasks. It features adaptive thinking time with adjustable reasoning modes (low, medium, high) to optimize performance based on task complexity. Outperforming the o1 series by 200 Elo points on Codeforces, it delivers high efficiency at a lower cost while maintaining speed and accuracy. As part of the o3 family, it pushes AI problem-solving boundaries while remaining accessible, offering a free tier and expanded limits for Plus subscribers. -
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Grounded Language Model (GLM)
Contextual AI
Contextual AI introduces its Grounded Language Model (GLM), engineered specifically to minimize hallucinations and deliver highly accurate, source-based responses for retrieval-augmented generation (RAG) and agentic applications. The GLM prioritizes faithfulness to the provided data, ensuring responses are grounded in specific knowledge sources and backed by inline citations. With state-of-the-art performance on the FACTS groundedness benchmark, the GLM outperforms other foundation models in scenarios requiring high accuracy and reliability. The model is designed for enterprise use cases like customer service, finance, and engineering, where trustworthy and precise responses are critical to minimizing risks and improving decision-making. -
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ERNIE 4.5 Turbo
Baidu
ERNIE 4.5 Turbo, unveiled by Baidu at the 2025 Baidu Create conference, is a cutting-edge AI model designed to handle a variety of data inputs, including text, images, audio, and video. It offers powerful multimodal processing capabilities that enable it to perform complex tasks across industries such as customer support automation, content creation, and data analysis. With enhanced reasoning abilities and reduced hallucinations, ERNIE 4.5 Turbo ensures that businesses can achieve higher accuracy and reliability in AI-driven processes. Additionally, this model is priced at just 1% of GPT-4.5’s cost, making it a highly cost-effective alternative for enterprises looking for top-tier AI performance.
