Best AI Models - Page 24
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Gemini 3.1 Flash TTS
Google
Gemini 3.1 Flash TTS is Google’s latest text-to-speech model designed to deliver highly expressive, controllable, and scalable AI-generated speech for developers and enterprises. Available in Google AI Studio and Gemini Enterprise Agent Platform, it focuses on precise control over how audio is generated, allowing users to shape delivery through natural language prompts and an extensive system of more than 200 audio tags that define pacing, tone, emotion, and style. It supports over 70 languages and regional variants, along with a library of 30 prebuilt voices, enabling users to generate speech ranging from professional narration to conversational or stylized performances. Developers can embed instructions directly into text inputs to guide vocal expression, combining pacing, emotion, and pauses in a structured prompting framework that produces nuanced, high-fidelity audio output. Gemini 3.1 Flash TTS is optimized for real-world applications. -
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ERNIE-Image
Baidu
ERNIE-Image is an open text-to-image generation model developed by Baidu, designed to deliver high-quality visuals with strong instruction accuracy and controllability. It is built on a single-stream Diffusion Transformer (DiT) architecture with around 8 billion parameters, allowing it to achieve state-of-the-art performance among open-weight image models while remaining relatively efficient. The model includes a built-in prompt enhancement system that expands simple user inputs into richer, structured descriptions, improving the quality and consistency of generated images. ERNIE-Image is optimized for complex instruction following, enabling accurate rendering of text within images, structured layouts, and multi-element compositions, making it particularly suitable for use cases like posters, comics, and multi-panel designs. It supports multilingual prompts, including English, Chinese, and Japanese, broadening accessibility and usability across regions. -
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Sarvam-M
Sarvam
Sarvam-M is a multilingual, hybrid-reasoning large language model designed to deliver strong performance across Indian languages, mathematical reasoning, and programming tasks within a single, efficient system. Built on top of Mistral-Small, it is a 24-billion-parameter text-only model that has been enhanced through supervised fine-tuning, reinforcement learning with verifiable rewards, and inference optimizations to improve both accuracy and efficiency. The model is specifically trained to handle more than ten major Indic languages, supporting native scripts, romanized text, and code-mixed inputs, enabling seamless multilingual communication across diverse linguistic contexts. Sarvam-M introduces a hybrid reasoning approach that allows it to switch between “thinking” mode for complex tasks like math, logic, and coding, and faster response mode for everyday interactions, balancing performance and speed. -
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GPT-5.5 Thinking
OpenAI
GPT-5.5 Thinking is an advanced AI capability from OpenAI designed to handle complex, multi-step tasks with greater intelligence and autonomy. It enables users to provide high-level instructions while the model plans, executes, and refines tasks independently. The system excels in areas such as coding, research, data analysis, and document creation. It can navigate across tools, check its own work, and adapt to ambiguous or incomplete inputs. GPT-5.5 Thinking is optimized for both speed and efficiency, delivering high-quality outputs while using fewer computational resources. It also supports long-context understanding, allowing it to process large datasets and extended workflows. Strong safeguards are built in to ensure responsible and secure usage. Overall, it represents a shift toward more autonomous, agent-like AI that can complete real-world tasks end-to-end. -
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Happy Horse
Alibaba
Happy Horse is an AI video generation and editing platform that helps users turn creative ideas into cinematic videos. The platform supports video creation from text, reference inputs, and first-frame prompts, giving creators flexible ways to bring visual concepts to life. Users can also edit videos by modifying details and refining generated results. Happy Horse features a creative community showcase with short films, featured videos, and AI cinema projects. The platform includes credits for generation, promotional offers, and tools for experimenting with imaginative video concepts. Happy Horse helps creators, artists, filmmakers, and storytellers capture ideas quickly and transform them into expressive AI-generated video content. -
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MiMo-V2.5-Pro
Xiaomi Technology
Xiaomi MiMo-V2.5-Pro is an advanced open-source AI model designed to handle complex, long-horizon tasks with strong agentic capabilities. It features a Mixture-of-Experts architecture with over one trillion parameters and a large context window of up to one million tokens. The model is built to perform sophisticated reasoning, coding, and problem-solving across extended workflows. It demonstrates high performance on benchmark tests related to software engineering, reasoning, and general intelligence. MiMo-V2.5-Pro can autonomously complete complex projects, such as building full software systems or optimizing engineering designs. It uses hybrid attention mechanisms to balance efficiency and performance across long contexts. The model is also optimized for token efficiency, reducing computational cost while maintaining strong results. By combining scalability, efficiency, and advanced reasoning, MiMo-V2.5-Pro represents a major step forward in open-source AI models. -
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MiMo-V2.5
Xiaomi Technology
Xiaomi MiMo-V2.5 is an advanced open-source AI model designed to combine strong agentic capabilities with native multimodal understanding. It can process and reason across text, images, and audio within a single unified system. The model uses a sparse Mixture-of-Experts architecture with hundreds of billions of parameters for efficient performance. It supports an extended context window of up to one million tokens, enabling long and complex workflows. MiMo-V2.5 is built to handle tasks such as coding, reasoning, and multimodal analysis with high accuracy. It incorporates dedicated visual and audio encoders to enhance perception and cross-modal reasoning. The model demonstrates strong benchmark performance across coding, reasoning, and multimodal tasks. By combining multimodality, efficiency, and agentic intelligence, MiMo-V2.5 advances the capabilities of open-source AI systems. -
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NVIDIA Alpamayo
NVIDIA
NVIDIA Alpamayo is an open ecosystem of AI models, simulation tools, and datasets designed to accelerate the development of autonomous vehicles with human-like reasoning capabilities. It is built around a family of Vision-Language-Action (VLA) models that combine visual perception, language-based reasoning, and action planning, enabling vehicles to interpret complex driving environments and make decisions step by step. Unlike traditional systems that rely mainly on pattern recognition, Alpamayo introduces chain-of-thought reasoning, allowing autonomous systems to understand rare or unpredictable “long-tail” scenarios and explain their decisions for improved safety and transparency. It integrates seamlessly with NVIDIA’s full autonomous driving stack, covering training, simulation, and deployment, so developers can build advanced systems without creating core infrastructure from scratch. -
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SubQ
Subquadratic
SubQ is a large language model developed by Subquadratic, designed specifically for long-context reasoning tasks. It can process up to 12 million tokens in a single prompt, allowing it to analyze entire codebases, long histories, and complex datasets at once. The model uses a sub-quadratic sparse-attention architecture that improves efficiency by focusing only on the most relevant relationships in the data. This approach reduces computational overhead while maintaining strong performance on large-scale tasks. SubQ is optimized for use cases such as software engineering, coding agents, and long-context retrieval. It delivers fast processing speeds and operates at a lower cost compared to many traditional models. Developers can access SubQ through APIs or integrate it into coding tools for enhanced workflows. Its architecture enables scalable AI reasoning without the limitations of standard transformer models. -
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ERNIE 5.1
Baidu
ERNIE 5.1 is Baidu’s latest large language model designed to deliver advanced reasoning, agentic AI capabilities, creative writing, and world knowledge performance while operating with significantly improved efficiency. The model builds on the foundation of ERNIE 5.0 while reducing total parameters and training costs, allowing it to achieve flagship-level intelligence at a fraction of the computational expense of comparable models. ERNIE 5.1 performs strongly across international benchmarks for reasoning, search, knowledge, and agentic tasks, ranking among the top global AI models and leading among Chinese-developed models on multiple leaderboards. The platform introduces a new fully asynchronous reinforcement learning infrastructure that improves training efficiency, scalability, and stability for complex long-horizon AI tasks. ERNIE 5.1 also features advanced creative writing capabilities. -
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Command A+
Cohere AI
Command A+ is Cohere’s fastest and most powerful language model yet, an open-source enterprise workhorse built for complex reasoning, multimodal and multilingual agentic tasks, and efficient private deployment. It is a sparse mixture-of-experts model with 218B total parameters and 25B active parameters, designed for high-performance agentic workflows with minimal compute overhead. Command A+ unifies capabilities from across the Command family into one scalable model, supporting text, image, reasoning, and tool use with a 128K input context, 64K max generation, and support for 48 languages. It is optimized for reasoning, agentic workflows, RAG, multilingual work, and multimodal document processing, with support for vLLM and Transformers. Compared with earlier Command A models, it improves enterprise workload performance across multimodal understanding, retrieval, long-horizon tasks, complex reasoning, coding, translation, and document understanding. -
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MAI-Image-2.5
Microsoft AI
MAI-Image-2.5 is Microsoft AI’s strongest image model yet and the next step in the MAI-Image series. It launched ranked third on the Arena text-to-image leaderboard and performs well across a wide range of styles, following instructions closely, rendering text more reliably than before, and producing detailed, coherent images as intended. The model delivers a step change in quality over MAI-Image-2, with major improvements in text rendering, stylized illustration, and commercial imagery. It also shows strong visual reasoning across objects, scene structure, lighting, scale, and spatial relationships, helping turn simple directions into polished images. MAI-Image-2.5 is especially focused on the details that make professional creative work usable: sharper words on posters, cleaner labels on packaging, stronger product-shot structure, more deliberate scenes, better layouts, and more polished brand-forward visuals. -
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Qwen3.7-Plus
Alibaba
Qwen3.7-Plus is a multimodal agent model that unifies vision and language into a single, versatile agent foundation. Building on Qwen3.7’s agentic intelligence, it extends Qwen’s capabilities into visual understanding, visual reasoning, grounded interaction, and multimodal tool use, enabling agents to perceive, analyze, and act across text, images, documents, screens, and complex real-world contexts. It is designed for tasks that require more than static question answering, including visual search, document comprehension, chart and table analysis, screen understanding, GUI interaction, image-grounded reasoning, and agent workflows that combine perception with planning and execution. Qwen3.7-Plus strengthens the connection between language reasoning and visual evidence, allowing users to ask questions about images, interpret dense multimodal inputs, extract structured information, and generate responses that reflect both context and visual details. -
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MAI-Thinking-1
Microsoft AI
MAI-Thinking-1 is Microsoft AI’s reasoning model, built for complex problems that matter most, with competitive reasoning and strong software engineering performance in its weight class. It is a 35B-active, approximately 1T-total-parameter sparse Mixture of Experts model, giving it a smaller inference footprint than much larger models while still matching leading models on key software engineering benchmarks. Microsoft trained MAI-Thinking-1 from the ground up on enterprise-grade, clean, commercially licensed data, without distillation from third-party models, so its capabilities are learned rather than inherited. The model is part of Microsoft AI’s Hill-Climbing Machine, a co-designed development pipeline built to make every component of model development continually and reliably improve over time. MAI-Thinking-1 is designed for agentic coding environments where models must read code, edit files, run tests, observe failures, and recover from intermediate mistakes. -
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MAI-Transcribe-1.5
Microsoft AI
MAI-Transcribe-1.5 is Microsoft AI’s production-ready speech-to-text model for turning noisy audio into highly accurate, domain-aware transcripts across 43 languages. It delivers consistent, high-accuracy transcription across languages, accents, speaking styles, and challenging audio conditions, with automatic language detection included. The model is designed for real-world audio where speech often comes through conference rooms, phone lines, busy streets, low-quality recordings, background noise, and overlapping speakers. MAI-Transcribe-1.5 adapts transcription to domain-specific terminology, making it ready for captions, call analysis, accessibility, meeting transcription, doctor’s notes, pharma customer calls, content workflows, and other enterprise speech use cases out of the box. It uses contextual biasing to improve recognition of specialized vocabulary, names, industry language, and terms that generic transcription systems may miss. -
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MAI-Voice-2
Microsoft AI
MAI-Voice-2 is Microsoft AI’s most expressive and natural-sounding text-to-speech model to date, built for production voice experiences where fidelity, language coverage, speaker consistency, and emotional range directly shape the user experience. It is designed for assistants, customer support, audiobooks, accessibility experiences, games, podcasts, courses, simulations, and creator workflows where voice quality must sound natural, fluid, and trustworthy. It expands from English-only support to 15 languages while maintaining naturalness and expressiveness, with support for English, Italian, French, German, Hindi, Spanish, Portuguese, Korean, Chinese, Turkish, Russian, Thai, Dutch, Romanian, and Hungarian. MAI-Voice-2 offers granular emotion control through tags such as sad, whispered, and excited, along with role-based expressive speech for experiences like motivational trainers, sports commentators, or character voices. -
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Aion 1.0 Instruct
Microsoft
Aion-1.0-Instruct is a pre-release small language model introduced in Microsoft Edge as a developer preview for early testing and feedback. It is designed to power Edge’s on-device Prompt and Writing Assistance APIs, giving web developers a faster, smaller, and more efficient model for AI-powered browser experiences. Microsoft previously used Phi-4-mini for these APIs, but its hardware requirements limited availability across devices. Aion-1.0-Instruct expands support to significantly more devices, including machines with less capable GPUs and, through CPU inference, devices without a GPU, while still delivering strong quality for a wide range of web use cases. The model is available in Edge Canary and Dev channels, allowing developers to evaluate it in real-world web scenarios, test API interoperability, and provide feedback before final optimizations. Aion-1.0-Instruct is meant to help developers build AI features directly into websites and browser extensions. -
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Aion 1.0 Plan
Microsoft
Aion 1.0 Plan is Microsoft’s local agentic reasoning model for Windows, designed to bring fully agentic workflows onto the device without cloud dependency or per-token cost. It is a 14-billion-parameter reasoning and tool-calling model with a 32K context length, shipping in-box as part of Windows on capable devices. Unlike smaller on-device models focused on everyday text intelligence, Aion 1.0 Plan is built for local agentic reasoning, enabling applications to understand user intent, invoke tools, manage files, and orchestrate sub-agents directly on the device. It belongs to Microsoft’s new generation of on-device small language models purpose-built for local execution, representing the progression from efficient text intelligence at scale to more capable local planning and action. Aion 1.0 Plan is part of Windows’ broader push toward “unmetered intelligence,” where frontier models handle the hardest problems while local models support continuous, lower-cost agent workflows. -
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Miso TTS
Miso TTS
Miso Labs builds emotive foundation models for voice, designed to help developers create voice agents that feel fast, warm, and human instead of robotic or delayed. Its flagship model, Miso TTS, is an 8-billion-parameter transformer model for state-of-the-art emotive speech and dialogue generation, with open source weights available on Hugging Face and API access coming soon. Miso is built for real-time conversational voice, responding in 110ms to preserve natural flow and avoid the awkward pauses common in AI voice agents. It supports one-shot voice cloning, allowing users to clone a voice from a ten-second audio clip while keeping the agent’s voice consistent from the first second of a call to the last. Miso Labs also emphasizes local and sovereign deployment, with open source models built for local use and on-premises hosting and support available for enterprise teams that need to keep sensitive data in-house. -
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Holo3.1
H Company
Holo3.1 is H Company’s family of fast and local computer-use agents, built to operate across web, desktop, and mobile environments while integrating more smoothly into different agent frameworks and deployment targets. Based on the Qwen family, Holo3.1 improves robustness across the environments where computer-use agents are actually deployed, addressing the distribution shifts that appear across mobile devices, alternative agent harnesses, and different execution frameworks. The release expands Holo3’s capabilities beyond browser and desktop control, with major gains in mobile automation, including AndroidWorld improvements from 67% to 79.3% for the 35B-A3B model and from 58% to 71% for the smaller 4B and 9B variants. Holo3.1 also introduces native support for function-calling protocols in addition to structured JSON outputs, helping teams deploy the model inside third-party agent stacks with near-parity between function-calling and native execution. -
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Gemini 3.5 Live Translate
Google
Gemini 3.5 Live Translate is Google’s latest audio model for live speech-to-speech translation, delivering near real-time translation in more than 70 languages. The model automatically detects multilingual input and generates smooth, natural-sounding translated speech that preserves the speaker’s intonation, pacing, and pitch. Unlike turn-by-turn translation systems that wait for someone to finish speaking before responding, Gemini 3.5 Live Translate processes speech as it streams and generates translated audio continuously, balancing the need for context with the need to stay in sync. It stays only a few seconds behind the speaker throughout a session, helping conversations feel more fluid and natural, without awkward pauses. It is built for multilingual calls, meetings, lessons, broadcasts, live interpretation, dubbing, simultaneous translation, and voice translation applications. -
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North Mini Code
Cohere
North Mini Code is Cohere’s first agentic coding model for developers and the inaugural member of its next generation of powerful models. Small, efficient, and open-source, it is built for the sovereign developer ecosystem and designed to deliver strong software development performance without requiring extensive hardware. North Mini Code is a mixture-of-experts model with 30B total parameters and 3B active parameters, giving developers access to agentic coding capabilities in a compact and efficient form. The model is optimized for code generation, agentic software engineering, and terminal tasks, with a 256K total context length and up to 64K maximum generation. It is built for real-world developer workflows, including understanding and orchestrating sub-agents, mapping system architecture, running code reviews, and supporting coding agents that need to reason through complex software tasks. -
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Cartesia Sonic-3.5
Cartesia
Sonic 3.5 is Cartesia’s fastest, most natural text-to-speech model, built for expressive, real-time voice generation with sub-90ms latency and native support for 42 languages. It is designed to follow transcripts faithfully, voice confirmation codes, and heteronyms correctly without preprocessing, and stay expressive enough to carry a real conversation. It supports languages intended to deliver native-quality speech. Sonic 3.5 focuses on clean audio across every language and voice, with no artifacts to edit out, making it practical for production voice experiences where quality, speed, and consistency matter. Its expressive conversational delivery provides strong pacing and real emotional range, tuned for support and agent transcripts. Alphanumerics such as order numbers, phone numbers, IDs, and emails are spoken naturally in every language, while context-aware English pronunciation helps words like read, bass, and bow land correctly from the surrounding text. -
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Cartesia Ink 2
Cartesia
Ink 2 is Cartesia’s fastest, most accurate streaming speech-to-text model, built for production voice agents with the lowest word error rate and best turn detection of any streaming STT. It is designed to transcribe structured data such as phone numbers, dates, and emails correctly the first time, while also knowing when a speaker starts and finishes without requiring a separate voice activity detection system. Turn detection is built directly into the model, so voice agents can react to events instead of managing raw transcript segments. Ink 2 emits a full lifecycle of turn events, giving an agent clear signals for when to listen, interrupt, think, prepare a reply, cancel a premature response, or speak. The transcript property is cumulative within a turn, meaning each update contains the full text transcribed so far rather than a delta, and emitted text is final once sent. -
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SubQ 1.1 Small
Subquadratic
SubQ 1.1 Small is a long-context AI model from Subquadratic designed to reason over complete enterprise artifacts such as codebases, document collections, contracts, and financial filings. It uses Subquadratic Sparse Attention, or SSA, to reduce the high compute costs normally associated with processing very large context windows. The model delivers near-perfect long-context retrieval across 1M, 2M, 6M, and 12M token tests while using far less attention compute than dense attention. SubQ 1.1 Small also maintains strong general reasoning, coding, knowledge, and agentic task performance across multiple benchmarks. Its capabilities make it useful for financial analysis, legal review, contract work, software engineering, due diligence, and other workflows where information is spread across large artifacts. SubQ is built for organizations that want to move beyond fragmented retrieval pipelines and enable direct reasoning over massive bodies of information. -
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Big Pickle
OpenCode
Big Pickle is an AI model available through OpenCode Zen, a curated model provider focused on coding-agent workflows. The model is designed for text-based input, reasoning tasks, function calling, and developer workflows that require long-context understanding. Big Pickle supports a large context window, making it useful for working across bigger codebases, project files, technical prompts, and multi-step coding tasks. It can be accessed through OpenCode Zen using an OpenAI-compatible API format, allowing developers to integrate it into agentic coding tools and automation workflows. The model is positioned as a free or low-cost option within OpenCode’s coding-agent ecosystem. Big Pickle helps developers experiment with AI-assisted coding, reasoning, tool use, and long-context automation without relying only on premium frontier models.Starting Price: Free -
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Ming-Flash Omni 2.0
Ant Group
Ming-Flash Omni 2.0 is a full-modal large language model from Ant Group, built on a unified multimodal architecture with “modal unity + task unity” as its core design philosophy. As part of the Ming series, it is designed to achieve cross-modal understanding and generation across text, images, audio, and video, allowing one model to see, hear, speak, and draw instead of relying on multiple specialized models. Ming-Flash Omni 2.0 follows the evolution of Ming-Light Omni and Ming-Flash Omni Preview, moving from unified architecture validation and hundred-billion-parameter scaling to a Data Scaling strategy that achieves open-source SOTA performance on multiple benchmarks. The model integrates four core capability modules: image-text understanding, video analysis, speech synthesis, and image generation or editing. For image-text understanding, Ming introduces structured knowledge graphs for fine-grained visual perception. -
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Nano Banana 2 Lite
Google
Nano Banana 2 Lite is Google’s fastest Gemini Image model in the Nano Banana family, built for high throughput, speed, and scale. Also known as Gemini 3.1 Flash Lite Image, it is designed for rapid ideation and high-velocity developer pipelines where speed, iteration, and efficient production are the primary constraints. Developers can use it as the recommended replacement for the first version of Nano Banana, gaining immediate benefits across key performance dimensions while continuing to build image-generation and editing workflows through Google AI Studio, the Gemini API, and Gemini Enterprise Agent Platform. Nano Banana 2 Lite is optimized for near-real-time, high-volume workflows where ultra-low latency is critical, delivering text-to-image outputs in just a few seconds and making it well-suited for interactive prototyping, visual drafting, creative exploration, and large-scale image generation. -
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LongCat-2.0
LongCat
LongCat-2.0 is a 1.6 trillion total-parameter Mixture-of-Experts language model built on AI ASIC superpods, with about 48 billion parameters activated per token and strong performance across coding and agentic tasks. It is a substantial step up from previous LongCat models, combining large-scale sparse architecture with dedicated post-training for real-world software engineering, tool use, long-context reasoning, and multi-step agent workflows. LongCat-2.0 is trained and deployed entirely on AI ASIC superpods, with pretraining spanning more than 35 trillion tokens and millions of accelerator-hours, demonstrating frontier-scale training on alternative hardware platforms. To strengthen long-horizon tasks, the model introduces LongCat Sparse Attention and is trained on hundreds of billions of tokens of 1M-context data, giving it native support for ultra-long context tasks and reliable long-document understanding. -
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Seed Audio 1.0
BytePlus
Seed Audio 1.0 is a non-streaming audio generation API based on HTTP, designed to generate complete audio from text prompts, reference audio, or reference images. It supports text-only generation, where audio is created directly from the prompt; reference-audio generation, where uploaded reference clips guide the output; and reference-image generation, where an image reference can be passed to generate audio from the text to be synthesized. Built as part of BytePlus Seed Speech, Audio 1.0 uses the seed-audio-1.0 model version and is positioned as an audio creation capability rather than a standard speech-only endpoint. It can generate voice, music, and sound effects in a single pass, making it useful for producing richer audio scenes without separately creating and mixing every track. The API is intended for developers building audio generation into applications, workflows, and production systems, with a request-based structure that lets teams submit prompts.
