Best AI Models in Brazil - Page 11
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Compare the Top AI Models in Brazil as of July 2026 - Page 11
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1
GPT-5.5 Pro
OpenAI
GPT-5.5 Pro is an advanced AI model designed to handle complex, real-world work with greater autonomy and efficiency. It understands user intent quickly and can execute multi-step tasks such as coding, research, data analysis, and document creation with minimal guidance. The model is built to plan, use tools, and refine its outputs until tasks are complete. It excels in knowledge work, software development, and analytical problem-solving. With strong reasoning and persistence, GPT-5.5 Pro can manage long-running workflows across tools and systems. It delivers high-quality results while maintaining speed and efficiency. Overall, it enables individuals and teams to complete demanding tasks faster and more accurately.Starting Price: $30 per 1M tokens (input) -
2
Qwen3.6-27B
Alibaba
Qwen3.6-27B is a dense, open source multimodal language model in the Qwen3.6 series, designed to deliver flagship-level performance in coding, reasoning, and agent-based workflows while maintaining a relatively efficient parameter size of 27 billion. It is positioned as a high-performance general model that “punches above its weight,” achieving results competitive with or superior to significantly larger models on key benchmarks, particularly in agentic coding tasks. It supports both thinking and non-thinking modes, allowing it to dynamically balance deep reasoning with fast responses depending on the task, and integrates capabilities across text and multimodal inputs such as images and video. Built as part of the Qwen3.6 family, the model emphasizes real-world usability, stability, and developer productivity, incorporating improvements driven by community feedback and practical deployment needs.Starting Price: Free -
3
KAT-Coder-Pro V2
StreamLake
KAT-Coder is an agentic AI coding system designed to go beyond traditional autocomplete tools by enabling end-to-end software development workflows driven by reasoning, planning, and execution. It is positioned as a flagship coding model within the KAT ecosystem, built specifically for “agentic coding,” where the model does not just generate snippets but can diagnose issues, propose fixes, run tests, and iterate across multiple files as part of a continuous development loop. It integrates directly with developer environments through API endpoints and proxy layers compatible with tools like Claude Code, allowing seamless use inside existing IDE workflows without changing the interface developers are already familiar with. KAT-Coder is trained using a multi-stage pipeline that includes supervised fine-tuning and large-scale reinforcement learning, enabling it to understand programming context, and reason over complex tasks.Starting Price: $0.30 per month -
4
Gemini Deep Research Max
Google
Gemini Deep Research is Google’s next-generation autonomous research agent, designed to plan, execute, and synthesize complex, multi-step research tasks across the web and private data sources into high-quality, structured outputs. Built on top of advanced Gemini models such as Gemini 3.1 Pro, it introduces a system where the AI can break down a user’s query into sub-tasks, search across multiple sources, evaluate relevance, and iteratively refine results before producing a comprehensive, cited report. It is positioned as a “step change” in long-horizon research workflows, enabling autonomous exploration of both public web content and custom enterprise data while maintaining context and coherence across extended reasoning chains. It supports features such as MCP (Model Context Protocol) integration, native visualizations, and significantly improved analytical quality, allowing users to generate insights.Starting Price: Free -
5
GPT-Realtime-1.5
OpenAI
GPT-Realtime-1.5 is a flagship voice AI model from OpenAI designed for real-time audio interactions and conversational applications. It supports both audio input and output, making it ideal for voice agents and customer support systems. The model delivers fast performance with high responsiveness, enabling natural, real-time conversations. It can process multiple input types, including text, audio, and images, while generating both text and audio responses. With a 32,000-token context window, it can handle extended conversations and maintain context effectively. The model is optimized for high-performance use cases where speed and accuracy are critical. It also supports function calling, allowing integration with external tools and workflows. Overall, it provides a powerful solution for building interactive, real-time voice applications.Starting Price: $4.00 per 1M tokens (input) -
6
DeepSeek-V4-Pro
DeepSeek
DeepSeek-V4-Pro is a large-scale Mixture-of-Experts (MoE) language model designed for advanced reasoning, coding, and long-context understanding. It features 1.6 trillion total parameters with 49 billion activated parameters, enabling high performance while maintaining efficiency. The model supports an exceptionally large context window of up to one million tokens, allowing it to process extensive documents and workflows. It uses a hybrid attention architecture to optimize long-context performance and reduce computational cost. DeepSeek-V4-Pro is trained on over 32 trillion tokens, improving its knowledge and reasoning capabilities. It also includes advanced optimization techniques for stability and faster convergence during training. The model supports multiple reasoning modes, allowing users to balance speed and accuracy based on their needs. Overall, it provides a powerful open-source solution for complex AI tasks and large-scale applications.Starting Price: Free -
7
DeepSeek-V4-Flash
DeepSeek
DeepSeek-V4-Flash is a high-efficiency Mixture-of-Experts (MoE) language model designed for fast, scalable reasoning and text generation. It features 284 billion total parameters with 13 billion activated parameters, delivering strong performance while optimizing computational cost. The model supports an extensive context window of up to one million tokens, enabling it to process large documents and complex workflows with ease. Its hybrid attention architecture enhances long-context efficiency by reducing memory and compute requirements. Trained on over 32 trillion tokens, DeepSeek-V4-Flash demonstrates solid capabilities across knowledge, reasoning, and coding tasks. It is designed for scenarios where speed and efficiency are critical, offering a balance between performance and resource usage. The model also supports multiple reasoning modes, allowing users to adjust between faster outputs and deeper analysis.Starting Price: Free -
8
Cartesia Sonic-3
Cartesia
Cartesia Sonic-3 is a real-time, streaming text-to-speech (TTS) model designed to generate ultra-realistic, expressive voice output with extremely low latency, enabling AI systems to speak as fluidly as humans in live interactions. Built on advanced state space model architecture, Sonic delivers high-quality speech while achieving near-instant response times, with audio generation beginning in as little as 40–100 milliseconds, making conversations feel seamless rather than delayed. It is optimized for conversational AI use cases, acting as the “voice layer” for AI agents by converting text into natural-sounding speech that includes emotional nuance such as excitement, empathy, or even laughter. It supports more than 40 languages with native-level voices and accent localization, allowing developers to build globally accessible applications with consistent quality across regions.Starting Price: $4 per month -
9
Cartesia Ink-Whisper
Cartesia
Cartesia Ink is a family of real-time streaming speech-to-text (STT) models designed to power fast, natural conversations in voice AI applications, acting as the “voice input” layer that converts spoken language into accurate text instantly. Its flagship model, Ink-Whisper, is specifically engineered for conversational environments, delivering ultra-low latency transcription with a time-to-complete-transcript as fast as 66 milliseconds, enabling fluid, human-like interactions without noticeable delays. Unlike traditional transcription systems built for batch processing, Ink is optimized for live dialogue, handling fragmented, variable-length audio through dynamic chunking, which reduces errors and improves responsiveness during pauses, interruptions, or rapid exchanges.Starting Price: $4 per month -
10
Modulate Velma
Modulate
Velma is a voice-native AI model developed by Modulate as part of a broader voice intelligence platform, designed to understand conversations directly from audio rather than relying on text transcripts. Unlike traditional systems that convert speech into text and analyze it with language models, Velma uses an Ensemble Listening Model (ELM), a specialized architecture that processes multiple dimensions of voice simultaneously, including tone, emotion, pacing, intent, and behavioral signals. This allows it to capture the full meaning of a conversation, not just the words spoken, recognizing nuances such as stress, deception, sarcasm, or escalation in real time. It operates by combining hundreds of specialized detectors, each focused on specific aspects of speech like emotional state, inappropriate conduct, or synthetic voice indicators, and then fusing those signals into higher-level insights about what is happening in a conversation.Starting Price: $0.25 per hour -
11
Nemotron 3 Nano Omni
NVIDIA
NVIDIA Nemotron 3 Nano Omni is an open, omni-modal foundation model designed to unify perception and reasoning across text, images, audio, video, and documents within a single efficient architecture. It eliminates the need for separate models for each modality, reducing inference latency, orchestration complexity, and cost while maintaining consistent cross-modal context. It is purpose-built for agentic AI systems, acting as a perception and context sub-agent that gives larger AI agents the ability to “see, hear, and read” in real time across screens, recordings, and structured or unstructured data. It supports advanced multimodal reasoning tasks such as document understanding, speech recognition, long audio-video analysis, and computer-use workflows, enabling agents to interpret dynamic interfaces and complex environments. Built with a hybrid architecture optimized for long context and throughput, it can process large inputs like multi-page documents.Starting Price: Free -
12
OpenAI Moderation
OpenAI
The OpenAI Moderation API provides developers with a dedicated endpoint to automatically evaluate whether text or images contain potentially harmful or policy-violating content, enabling safer AI applications through real-time filtering and classification. It works by analyzing inputs (and optionally outputs) and returning structured results that indicate whether the content is flagged, along with detailed category labels such as hate, harassment, self-harm, sexual content, or violence. It is designed to be integrated directly into application workflows, allowing developers to take immediate action, such as blocking, filtering, or escalating content, before it reaches end users. Moderation models like “omni-moderation-latest” are optimized for speed and accuracy, supporting scalable use across high-volume applications while maintaining consistent safety standards.Starting Price: Free -
13
GPT-Realtime-Translate
OpenAI
GPT-Realtime-Translate is OpenAI’s live translation model for building multilingual voice experiences where each person can speak in their preferred language, hear the conversation translated in real time, and read real-time transcriptions. It supports more than 70 input languages and 13 output languages, making it useful for customer support, cross-border sales, education, events, media, and creator platforms serving global audiences. It is designed to preserve meaning while keeping pace with the speaker, even when people speak naturally, switch context, use regional pronunciation, or rely on domain-specific language. GPT-Realtime-Translate helps cross-language conversations feel more natural by combining lower latency, stronger fluency, and real-time speech translation in one API workflow. It can support live multilingual voice interactions, translate conversations as they happen, and make spoken content accessible to audiences.Starting Price: $0.034 per minute -
14
GPT‑Realtime‑Whisper
OpenAI
GPT-Realtime-Whisper is OpenAI’s streaming transcription model built for low-latency speech-to-text experiences in live products. It transcribes audio as people speak, helping voice-enabled apps feel faster, more responsive, and more natural, from captions that appear in the moment to meeting notes that keep up with the conversation. It makes live speech usable inside business workflows as it happens, so teams can power captions for meetings, classrooms, broadcasts, and events, generate notes and summaries while conversations are still in progress, build voice agents that need to understand users continuously, and create faster follow-up workflows for high-volume spoken interactions. It is part of a new generation of real-time voice models in the API that can reason, translate, and transcribe as people speak, moving real-time audio beyond simple call-and-response toward voice interfaces that can listen, translate, transcribe, and take action as a conversation unfolds.Starting Price: $0.017 per minute -
15
Realtime TTS-2
Inworld
Realtime TTS-2 from Inworld AI is a new generation of voice model built for real-time conversation: a voice model that feels as human as it sounds. It hears the full audio of an exchange, picks up the user’s tone, pacing, and emotional state, then takes voice direction in plain English, the way developers prompt an LLM. Instead of generating speech in isolation, it listens to prior turns of the exchange, so tone and pacing carry forward, and the same line can land differently after a joke than after bad news. Voice Direction lets developers steer delivery like a director would steer a voice actor, using natural-language descriptions rather than fixed emotion presets or sliders. Inline nonverbals like [sigh], [breathe], and [laugh] can be placed inside the text, and the model renders them as audio events. Realtime TTS-2 preserves one voice identity across more than 100 languages, including mid-utterance language switches.Starting Price: $25 per month -
16
NeuralWing
Emmi AI
NeuralWing is a real-time neural simulation and design optimization model for transonic aircraft aerodynamics. It is built around the largest 3D transonic wing dataset, created from 30,000 steady-state CFD simulations of a 3D wing in the transonic regime, with variations across four geometry parameters and two inflow conditions. Using Emmi’s AB-UPT surrogate model trained on this data, NeuralWing enables users to modify wing geometry, test optimizations, and maximize aerodynamic efficiency in seconds. The model supports transonic 3D wing simulation, geometry and inflow variations, real-time inference, and design-parameter optimization. Its inputs include a geometry mesh in STL format, speed, and angle of attack, while its outputs include pressure, friction, velocity fields, and integral forces such as lift and drag. Geometry meshes are created in real time from four design parameters in a differentiable manner, allowing fast exploration of design changes.Starting Price: Free -
17
NeuralMould
Emmi AI
NeuralMould is Emmi AI’s Large Engineering Model for injection molding, described as a new gold standard in AI for engineering: any geometry, any material, any injection gates, one model. It lets users select from a range of geometries and test injection, material, and gate placement parameters to simulate filling behavior in seconds, rapidly compare multiple scenarios, optimize process KPIs, and avoid frozen flow fronts. Injection molding simulation is highly complex because it involves multi-physics calculations that model transient flow of viscous plastic through thin-walled geometries under extreme temperature and pressure conditions. NeuralMould captures these phenomena across a wide range of injecting conditions and mold geometries, achieving performance comparable to traditional solvers with a fraction of the computation time. The model supports multi-material scenarios, fast prototyping, multi-gate configurations, and multiple process parameters.Starting Price: Free -
18
ESMC
Biohub
ESMC is the latest in the ESM family of protein language models, establishing a new frontier in representation learning for protein biology. Trained on billions of evolutionary sequences, it learns representations that reflect a mechanistic reduction of protein structure and function. The model is built on a transformer architecture, supports sequences as its core modality, and is trained on up to 6 billion proteins. ESMC is designed for protein science research, including structure prediction, function annotation, protein design, and understanding evolutionary relationships between proteins. It can generate novel proteins from partial sequence, structure, or functional constraints, helping researchers explore new possibilities in protein design and biological discovery. The Biohub Platform provides access to ESMC through the API and the ESM Python package, with quickstart resources for installing the package, creating an API key, connecting to the platform.Starting Price: Free -
19
ESMFold2
Biohub
ESMFold2 is the successor to ESMFold, setting a new state of the art for single-sequence structure prediction and enabling the generation of new functional proteins through searching the ESMC model’s latent space. The model predicts high-resolution, all-atom 3D structures of biomolecular complexes directly from sequence, with optional multiple sequence alignment input for enhanced accuracy on challenging targets. It is designed for structure prediction using sequence and structure modalities, with ESM representations powering a series of looped folding layers and a diffusion model projecting pairwise representations to atomic-resolution predictions. ESMFold2 predicts protein structures directly from amino acid sequences and outputs comprehensive structural information, including all-atom coordinates for backbone and side chains, confidence metrics, and optional distogram predictions for detailed structural analysis.Starting Price: Free -
20
Ideogram 4.0
Ideogram
Ideogram 4.0 is an open image model at the forefront of design, built for open weights, multilingual text, precise layout control, editable elements, and realistic 2K images. It is a state-of-the-art open-weight image model for developers and enterprises that want to build, fine-tune, and run visual intelligence on their own hardware. Ideogram 4.0 was trained with a describe-to-structure-to-recreate loop, first reading scenes, backgrounds, text, and objects as structured data, then learning to rebuild images from that representation. This approach is designed to help the model understand composition before recreating it, giving teams more control over layout, objects, typography, and visual structure. It is built for real design work, especially brand, advertising, fashion, marketing, food, apparel, social, photography, and illustration use cases. Ideogram has led on text rendering since launch, and 4.0 adds bounding-box layout control so headlines stay readable.Starting Price: Free -
21
Reve 2.0
Reve
Reve 2.0 is an AI creative studio for generating, editing, and remixing images with natural language and a drag-and-drop editor. It is designed to help users reimagine reality by creating polished visuals, refining existing images, and staying in flow from idea to finished creative. Users can start with a prompt, upload an image, make precise edits in plain language, and combine AI generation with direct visual control inside the editor. Reve 2.0 introduces the platform’s best image generation and editing model, with native 4K image generation and editing, state-of-the-art visual quality, and stronger creative control for producing high-fidelity results. It supports image creation, image editing, image remixing, and a more interactive workflow where users can change parts of a scene, adjust visual direction, explore variations, and build on previous outputs without needing traditional design tools.Starting Price: $7.99 per month -
22
Laguna XS.2
Poolside
Laguna XS.2 is Poolside’s open-weight agentic coding model, built as the lightest and fastest model in the Laguna family. It is a 33B total-parameter Mixture of Experts model with 3B activated parameters, trained completely in-house on 30T tokens. As Poolside’s newest generation model open to the community, Laguna XS.2 is a second-generation architecture and the company’s first open-weight model, built on the lessons learned from training Laguna M.1 across synthetic data and reinforcement learning. The model is designed for agentic coding workflows, where it can code, act, iterate quickly, and perform best inside Poolside’s coding agent. Laguna XS.2 is positioned as a strong model for rapid agentic iteration, especially for developers and teams that need a compact, efficient coding model rather than a heavier frontier system. It is released under an Apache 2.0 license, allowing the community to evaluate, fine-tune, quantize, serve, and build on the weights.Starting Price: Free -
23
Laguna M.1
Poolside
Laguna M.1 is Poolside’s most capable model for agentic coding, built and trained in-house for software development workflows. It is a 225B total-parameter Mixture of Experts model with 23B activated parameters, trained completely in-house on 30T tokens using 6,144 interconnected NVIDIA H200 GPUs. Poolside trained Laguna M.1 from scratch with its own data work, training codebase, and async on-policy reinforcement learning in its agent harness, all with agentic coding in mind. The model is designed to perform at its best inside Poolside’s coding agent, where it can reason through software tasks, interact with tools, edit code, run tests, and support longer autonomous development sessions. Laguna M.1 is built for developers and teams working on complex coding tasks that require stronger reasoning, architectural understanding, terminal use, and multi-step execution than lightweight models can provide.Starting Price: Free -
24
DiffusionGemma
Google
DiffusionGemma is an experimental open model that explores text diffusion, an exceptionally fast approach to text generation. Released under an Apache 2.0 license, this 26B Mixture of Experts (MoE) model moves beyond the sequential token-by-token processing of typical autoregressive Large Language Models (LLMs). Instead, it generates entire blocks of text simultaneously, delivering up to 4x faster text generation on GPUs. Built on the intelligence-per-parameter of the Gemma 4 family and Gemini Diffusion research, DiffusionGemma integrates a novel diffusion head designed to maximize generation speed. It is designed for researchers and developers exploring speed-critical, interactive local workflows such as in-line editing, rapid iteration, and non-linear text structures. By shifting the decode bottleneck from memory bandwidth to compute, it can generate more than 1,000 tokens per second on a single NVIDIA H100 and more than 700 tokens per second on an NVIDIA GeForce RTX 5090.Starting Price: Free -
25
Apple Foundation Models
Apple
The Apple Foundation Models framework lets developers perform tasks with Apple’s on-device model that specializes in language understanding, structured output, and tool calling. It provides access to the on-device large language model that powers Apple Intelligence, helping apps perform intelligent tasks specific to their use case. The text-based on-device model identifies patterns that allow it to generate new text appropriate for the request, and it can make decisions to call code written by the developer to perform specialized tasks. Developers can generate text content for a wide range of tasks, including summarization, entity extraction, text understanding, refinement, dialog for games, creative content generation, classification, and more. It also supports guided generation, allowing developers to generate entire Swift data structures with strong guarantees by using the Generable macro.Starting Price: Free -
26
HiDream O1 Image 1.5
HiDream.ai
HiDream O1 Image 1.5 is a next-generation text-to-image model tuned for sharp detail, stronger prompt adherence, and more reliable text rendering. It lets users create stunning AI images from text directly in the browser, with no local GPU, no installation, and one focused online studio for generating, reviewing, and downloading results. It converts natural-language prompts into high-resolution images with crisp edges, balanced lighting, coherent composition, and stable visual structure across supported aspect ratios. Built for prompt fidelity, HiDream O1 Image 1.5 follows long, structured prompts closely, keeping subjects, attributes, styles, and scene layouts brief, even across multi-part descriptions and negative prompts. Users can generate square, portrait, and landscape images in 1:1, 3:4, 4:3, 9:16, and 16:9 ratios, making outputs ready for social, web, poster, banner, product, and print draft workflows.Starting Price: $10 per month -
27
Sakana Fugu
Sakana AI
Sakana Fugu is an AI model and multi-agent AI system delivered through a single OpenAI-compatible API. The platform dynamically orchestrates a pool of powerful models to solve complex tasks without requiring users to manually choose models, assign roles, or design agent workflows. Fugu learns how to assemble and coordinate agents for coding, reasoning, research, cybersecurity, scientific analysis, and other quality-critical work. Users can choose between Fugu for balanced performance and latency or Fugu Ultra for harder, high-stakes tasks that need deeper expert coordination. The platform also allows users to control which models or providers can participate in the agent pool to support privacy, compliance, and organizational requirements. Sakana Fugu helps teams access collective AI intelligence through one endpoint while reducing single-vendor dependency and improving performance on complex multi-step workflows.Starting Price: $20/month -
28
Nex-N2-Pro
Nex-AGI
Nex-N2-Pro is an open source agentic model with Agentic Thinking, built for real-world productivity scenarios where reasoning must turn into executable, verifiable, and iterable action. Rather than treating reasoning, tool use, and environment execution as separate capabilities, Nex-N2 unifies them through a framework that connects requirement understanding, task planning, code implementation, environmental feedback, evaluation, and debugging, and continuous iteration into a single closed loop. Its thinking paradigm is unified across search, coding, and agentic tool calling, following a consistent structure of goal decomposition, state tracking, strategy adjustment, and self-verification, which is especially useful in mixed tasks such as coding workflows that include searches and tool calls. Adaptive Thinking lets the model decide when to think and how deeply, executing simple actions quickly while reasoning more thoroughly on critical decisions to allocate resources efficiently.Starting Price: Free -
29
Nex-N2-mini
Nex-AGI
Nex-N2-mini is an open source agentic model with Agentic Thinking, built for real-world productivity scenarios where fast instruction following, real-time tool execution, and cost-effective large-scale deployment matter. As part of the Nex-N2 family, it is designed to turn thinking into actions that are executable, verifiable, and iterable, rather than treating reasoning, tool use, and environment execution as separate capabilities. Nex-N2-mini uses the same unified Agentic Thinking framework as Nex-N2-Pro, connecting requirement understanding, task planning, code implementation, environmental feedback, evaluation, debugging, and continuous iteration into one closed loop. Its thinking paradigm stays consistent across search, coding, and agentic tool calling, following goal decomposition, state tracking, strategy adjustment, and self-verification, which is especially useful in mixed tasks where coding is interleaved with searches and tool calls.Starting Price: Free -
30
DeepSeek-OCR
DeepSeek
DeepSeek-OCR is an open source model for Contexts Optical Compression, built to explore the boundaries of visual-text compression and investigate the role of vision encoders from an LLM-centric viewpoint. It is designed to compress long contexts through optical 2D mapping, using DeepEncoder as the core engine and DeepSeek3B-MoE-A570M as the decoder. DeepEncoder maintains low activations under high-resolution input while achieving high compression ratios, keeping the number of vision tokens manageable for document understanding. The model supports OCR and document parsing workflows for images and PDFs, with inference through vLLM or Transformers. Users can run image OCR with streaming output, process PDFs with high concurrency, or run batch evaluation for benchmarks. DeepSeek-OCR can convert documents to Markdown, perform free OCR without layouts, parse figures, describe images in detail, and locate referenced text inside an image.Starting Price: Free
