Best AI Models - Page 10
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ERNIE X1
Baidu
ERNIE X1 is an advanced conversational AI model developed by Baidu as part of their ERNIE (Enhanced Representation through Knowledge Integration) series. Unlike previous versions, ERNIE X1 is designed to be more efficient in understanding and generating human-like responses. It incorporates cutting-edge machine learning techniques to handle complex queries, making it capable of not only processing text but also generating images and engaging in multimodal communication. ERNIE X1 is often used in natural language processing applications such as chatbots, virtual assistants, and enterprise automation, offering significant improvements in accuracy, contextual understanding, and response quality.Starting Price: $0.28 per 1M tokens -
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Reka Flash 3
Reka
Reka Flash 3 is a 21-billion-parameter multimodal AI model developed by Reka AI, designed to excel in general chat, coding, instruction following, and function calling. It processes and reasons with text, images, video, and audio inputs, offering a compact, general-purpose solution for various applications. Trained from scratch on diverse datasets, including publicly accessible and synthetic data, Reka Flash 3 underwent instruction tuning on curated, high-quality data to optimize performance. The final training stage involved reinforcement learning using REINFORCE Leave One-Out (RLOO) with both model-based and rule-based rewards, enhancing its reasoning capabilities. With a context length of 32,000 tokens, Reka Flash 3 performs competitively with proprietary models like OpenAI's o1-mini, making it suitable for low-latency or on-device deployments. The model's full precision requires 39GB (fp16), but it can be compressed to as small as 11GB using 4-bit quantization. -
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Athene-V2
Nexusflow
Athene-V2 is Nexusflow's latest 72-billion-parameter model suite, fine-tuned from Qwen 2.5 72B, designed to compete with GPT-4o across key capabilities. This suite includes Athene-V2-Chat-72B, a state-of-the-art chat model that matches GPT-4o in multiple benchmarks, excelling in chat helpfulness (Arena-Hard), code completion (ranking #2 on bigcode-bench-hard), mathematics (MATH), and precise long log extraction. Additionally, Athene-V2-Agent-72B balances chat and agent functionalities, offering concise, directive responses and surpassing GPT-4o in Nexus-V2 function calling benchmarks focused on complex enterprise-level use cases. These advancements underscore the industry's shift from merely scaling model sizes to specialized customization, illustrating how targeted post-training processes can finely optimize models for distinct skills and applications. -
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NVIDIA Llama Nemotron
NVIDIA
NVIDIA Llama Nemotron is a family of advanced language models optimized for reasoning and a diverse set of agentic AI tasks. These models excel in graduate-level scientific reasoning, advanced mathematics, coding, instruction following, and tool calls. Designed for deployment across various platforms, from data centers to PCs, they offer the flexibility to toggle reasoning capabilities on or off, reducing inference costs when deep reasoning isn't required. The Llama Nemotron family includes models tailored for different deployment needs. Built upon Llama models and enhanced by NVIDIA through post-training, these models demonstrate improved accuracy, up to 20% over base models, and optimized inference speeds, achieving up to five times the performance of other leading open reasoning models. This efficiency enables handling more complex reasoning tasks, enhances decision-making capabilities, and reduces operational costs for enterprises. -
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AlphaCodium
Qodo
AlphaCodium is a research-driven AI tool developed by Qodo to enhance coding with iterative, test-driven processes. It helps large language models improve their accuracy by enabling them to engage in logical reasoning, testing, and refining code. AlphaCodium offers an alternative to basic prompt-based approaches by guiding AI through a more structured flow paradigm, which leads to better mastery of complex code problems, particularly those involving edge cases. It improves performance on coding challenges by refining outputs based on specific tests, ensuring more reliable results. AlphaCodium is benchmarked to significantly increase the success rates of LLMs like GPT-4o, OpenAI o1, and Sonnet-3.5. It supports developers by providing advanced solutions for complex coding tasks, allowing for enhanced productivity in software development. -
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Reve Image
Reve
Reve Image is an AI-powered tool designed to generate high-quality images based on detailed user prompts. It excels in prompt adherence, aesthetics, and typography, making it ideal for creating visually appealing graphics and designs with accurate text integration. Reve Image is built to follow instructions precisely, producing images that meet both creative and practical requirements. While image generation is the initial offering, Reve Image aims to expand its capabilities further, with users encouraged to sign up for future updates and releases. -
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Qwen2.5-VL-32B
Alibaba
Qwen2.5-VL-32B is a state-of-the-art AI model designed for multimodal tasks, offering advanced capabilities in both text and image reasoning. It builds upon the earlier Qwen2.5-VL series, improving response quality with more human-like, formatted answers. The model excels in mathematical reasoning, fine-grained image understanding, and complex, multi-step reasoning tasks, such as those found in MathVista and MMMU benchmarks. Its superior performance has been demonstrated in comparison to other models, outperforming the larger Qwen2-VL-72B in certain areas. With improved image parsing and visual logic deduction, Qwen2.5-VL-32B provides a detailed, accurate analysis of images and can generate responses based on complex visual inputs. It has been optimized for both text and image tasks, making it ideal for applications requiring sophisticated reasoning and understanding across different media. -
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Magma
Microsoft
Magma is a cutting-edge multimodal foundation model developed by Microsoft, designed to understand and act in both digital and physical environments. The model excels at interpreting visual and textual inputs, allowing it to perform tasks such as interacting with user interfaces or manipulating real-world objects. Magma builds on the foundation models paradigm by leveraging diverse datasets to improve its ability to generalize to new tasks and environments. It represents a significant leap toward developing AI agents capable of handling a broad range of general-purpose tasks, bridging the gap between digital and physical actions. -
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Gen-4
Runway
Runway Gen-4 is a next-generation AI model that transforms how creators generate consistent media content, from characters and objects to entire scenes and videos. It allows users to create cohesive, stylized visuals that maintain consistent elements across different environments, lighting, and camera angles, all with minimal input. Whether for video production, VFX, or product photography, Gen-4 provides unparalleled control over the creative process. The platform simplifies the creation of production-ready videos, offering dynamic and realistic motion while ensuring subject consistency across scenes, making it a powerful tool for filmmakers and content creators. -
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Gemini 2.5 Flash
Google
Gemini 2.5 Flash is a powerful, low-latency AI model introduced by Google on Vertex AI, designed for high-volume applications where speed and cost-efficiency are key. It delivers optimized performance for use cases like customer service, virtual assistants, and real-time data processing. With its dynamic reasoning capabilities, Gemini 2.5 Flash automatically adjusts processing time based on query complexity, offering granular control over the balance between speed, accuracy, and cost. It is ideal for businesses needing scalable AI solutions that maintain quality and efficiency. -
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Amazon Nova Micro
Amazon
Amazon Nova Micro is an AI model designed for high-speed, low-cost text processing and generation. It excels in language understanding, translation, code completion, and mathematical problem-solving, providing fast responses with a generation speed of over 200 tokens per second. The model supports fine-tuning for text input and is ideal for applications requiring real-time processing and efficiency. With support for 200+ languages and a maximum of 128k tokens, Nova Micro is perfect for interactive AI applications that prioritize speed and affordability. -
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Amazon Nova Lite
Amazon
Amazon Nova Lite is a cost-efficient, multimodal AI model designed for rapid processing of image, video, and text inputs. It delivers impressive performance at an affordable price, making it ideal for interactive, high-volume applications where cost is a key consideration. With support for fine-tuning across text, image, and video inputs, Nova Lite excels in a variety of tasks that require fast, accurate responses, such as content generation and real-time analytics. -
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Amazon Nova Pro
Amazon
Amazon Nova Pro is a versatile, multimodal AI model designed for a wide range of complex tasks, offering an optimal combination of accuracy, speed, and cost efficiency. It excels in video summarization, Q&A, software development, and AI agent workflows that require executing multi-step processes. With advanced capabilities in text, image, and video understanding, Nova Pro supports tasks like mathematical reasoning and content generation, making it ideal for businesses looking to implement cutting-edge AI in their operations. -
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Gemini Live API
Google
The Gemini Live API is a preview feature that enables low-latency, bidirectional voice and video interactions with Gemini. It allows end users to experience natural, human-like voice conversations and provides the ability to interrupt the model's responses using voice commands. The model can process text, audio, and video input, and it can provide text and audio output. New capabilities include two new voices and 30 new languages with configurable output language, configurable image resolutions (66/256 tokens), configurable turn coverage (send all inputs all the time or only when the user is speaking), configurable interruption settings, configurable voice activity detection, new client events for end-of-turn signaling, token counts, a client event for signaling the end of stream, text streaming, configurable session resumption with session data stored on the server for 24 hours, and longer session support with a sliding context window. -
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Amazon Nova Sonic
Amazon
Amazon Nova Sonic is a state-of-the-art speech-to-speech model that delivers real-time, human-like voice conversations with industry-leading price performance. It unifies speech understanding and generation into a single model, enabling developers to create natural, expressive conversational AI experiences with low latency. Nova Sonic adapts its responses based on the prosody of input speech, such as pace and timbre, resulting in more natural dialogue. It supports function calling and agentic workflows to interact with external services and APIs, including knowledge grounding with enterprise data using Retrieval-Augmented Generation (RAG). It provides robust speech understanding for American and British English across various speaking styles and acoustic conditions, with additional languages coming soon. Nova Sonic handles user interruptions gracefully without dropping conversational context and is robust to background noise. -
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Gen-4 Turbo
Runway
Runway Gen-4 Turbo is an advanced AI video generation model designed for rapid and cost-effective content creation. It can produce a 10-second video in just 30 seconds, significantly faster than its predecessor, which could take up to a couple of minutes for the same duration. This efficiency makes it ideal for creators needing quick iterations and experimentation. Gen-4 Turbo offers enhanced cinematic controls, allowing users to dictate character movements, camera angles, and scene compositions with precision. Additionally, it supports 4K upscaling, providing high-resolution outputs suitable for professional projects. While it excels in generating dynamic scenes and maintaining consistency, some limitations persist in handling intricate motions and complex prompts. -
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Seaweed
ByteDance
Seaweed is a foundational AI model for video generation developed by ByteDance. It utilizes a diffusion transformer architecture with approximately 7 billion parameters, trained on a compute equivalent to 1,000 H100 GPUs. Seaweed learns world representations from vast multi-modal data, including video, image, and text, enabling it to create videos of various resolutions, aspect ratios, and durations from text descriptions. It excels at generating lifelike human characters exhibiting diverse actions, gestures, and emotions, as well as a wide variety of landscapes with intricate detail and dynamic composition. Seaweed offers enhanced controls, allowing users to generate videos from images by providing an initial frame to guide consistent motion and style throughout the video. It can also condition on both the first and last frames to create transition videos, and be fine-tuned to generate videos based on reference images. -
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Amazon Nova Premier
Amazon
Amazon Nova Premier is the most advanced model in their Nova family, designed to handle complex tasks and act as a teacher for model distillation. Available on Amazon Bedrock, Nova Premier can process text, images, and video inputs, making it capable of managing intricate workflows, multi-step planning, and the precise execution of tasks across various data sources. The model features a context length of one million tokens, enabling it to handle large-scale documents and code bases efficiently. Furthermore, Nova Premier allows users to create smaller, faster, and more cost-effective versions of its models, such as Nova Pro and Nova Micro, for specific use cases through model distillation. -
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Phi-4-reasoning
Microsoft
Phi-4-reasoning is a 14-billion parameter transformer-based language model optimized for complex reasoning tasks, including math, coding, algorithmic problem solving, and planning. Trained via supervised fine-tuning of Phi-4 on carefully curated "teachable" prompts and reasoning demonstrations generated using o3-mini, it generates detailed reasoning chains that effectively leverage inference-time compute. Phi-4-reasoning incorporates outcome-based reinforcement learning to produce longer reasoning traces. It outperforms significantly larger open-weight models such as DeepSeek-R1-Distill-Llama-70B and approaches the performance levels of the full DeepSeek-R1 model across a wide range of reasoning tasks. Phi-4-reasoning is designed for environments with constrained computing or latency. Fine-tuned with synthetic data generated by DeepSeek-R1, it provides high-quality, step-by-step problem solving. -
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Phi-4-reasoning-plus
Microsoft
Phi-4-reasoning-plus is a 14-billion parameter open-weight reasoning model that builds upon Phi-4-reasoning capabilities. It is further trained with reinforcement learning to utilize more inference-time compute, using 1.5x more tokens than Phi-4-reasoning, to deliver higher accuracy. Despite its significantly smaller size, Phi-4-reasoning-plus achieves better performance than OpenAI o1-mini and DeepSeek-R1 at most benchmarks, including mathematical reasoning and Ph.D. level science questions. It surpasses the full DeepSeek-R1 model (with 671 billion parameters) on the AIME 2025 test, the 2025 qualifier for the USA Math Olympiad. Phi-4-reasoning-plus is available on Azure AI Foundry and HuggingFace. -
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Phi-4-mini-reasoning
Microsoft
Phi-4-mini-reasoning is a 3.8-billion parameter transformer-based language model optimized for mathematical reasoning and step-by-step problem solving in environments with constrained computing or latency. Fine-tuned with synthetic data generated by the DeepSeek-R1 model, it balances efficiency with advanced reasoning ability. Trained on over one million diverse math problems spanning multiple levels of difficulty from middle school to Ph.D. level, Phi-4-mini-reasoning outperforms its base model on long sentence generation across various evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. It features a 128K-token context window and supports function calling, enabling integration with external tools and APIs. Phi-4-mini-reasoning can be quantized using Microsoft Olive or Apple MLX Framework for deployment on edge devices such as IoT, laptops, and mobile devices. -
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DeepSeek-Coder-V2
DeepSeek
DeepSeek-Coder-V2 is an open source code language model designed to excel in programming and mathematical reasoning tasks. It features a Mixture-of-Experts (MoE) architecture with 236 billion total parameters and 21 billion activated parameters per token, enabling efficient processing and high performance. The model was trained on an extensive dataset of 6 trillion tokens, enhancing its capabilities in code generation and mathematical problem-solving. DeepSeek-Coder-V2 supports over 300 programming languages and has demonstrated superior performance on benchmarks such surpassing other models. It is available in multiple variants, including DeepSeek-Coder-V2-Instruct, optimized for instruction-based tasks; DeepSeek-Coder-V2-Base, suitable for general text generation; and lightweight versions like DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct, designed for environments with limited computational resources. -
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HunyuanCustom
Tencent
HunyuanCustom is a multi-modal customized video generation framework that emphasizes subject consistency while supporting image, audio, video, and text conditions. Built upon HunyuanVideo, it introduces a text-image fusion module based on LLaVA for enhanced multi-modal understanding, along with an image ID enhancement module that leverages temporal concatenation to reinforce identity features across frames. To enable audio- and video-conditioned generation, it further proposes modality-specific condition injection mechanisms, an AudioNet module that achieves hierarchical alignment via spatial cross-attention, and a video-driven injection module that integrates latent-compressed conditional video through a patchify-based feature-alignment network. Extensive experiments on single- and multi-subject scenarios demonstrate that HunyuanCustom significantly outperforms state-of-the-art open and closed source methods in terms of ID consistency, realism, and text-video alignment. -
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SWE-1
Windsurf
SWE-1 is the first family of software engineering models developed by Windsurf, designed to optimize the entire software engineering process. Comprising three models—SWE-1, SWE-1-lite, and SWE-1-mini—this innovative family of models tackles more than just coding by supporting a wide range of engineering tasks. SWE-1 outperforms other models, providing powerful, multi-surface, long-horizon task management and AI-driven insights that significantly accelerate software development. This groundbreaking approach allows for more efficient problem-solving and an AI-powered workflow that integrates seamlessly with user actions. -
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Xgen-small
Salesforce
Xgen-small is an enterprise-ready compact language model developed by Salesforce AI Research, designed to deliver long-context performance at a predictable, low cost. It combines domain-focused data curation, scalable pre-training, length extension, instruction fine-tuning, and reinforcement learning to meet the complex, high-volume inference demands of modern enterprises. Unlike traditional large models, Xgen-small offers efficient processing of extensive contexts, enabling the synthesis of information from internal documentation, code repositories, research reports, and real-time data streams. With sizes optimized at 4B and 9B parameters, it provides a strategic advantage by balancing cost efficiency, privacy safeguards, and long-context understanding, making it a sustainable and predictable solution for deploying Enterprise AI at scale. -
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Gemini 2.5 Pro Deep Think
Google
Gemini 2.5 Pro Deep Think is a cutting-edge AI model designed to enhance the reasoning capabilities of machine learning models, offering improved performance and accuracy. This advanced version of the Gemini 2.5 series incorporates a feature called "Deep Think," allowing the model to reason through its thoughts before responding. It excels in coding, handling complex prompts, and multimodal tasks, offering smarter, more efficient execution. Whether for coding tasks, visual reasoning, or handling long-context input, Gemini 2.5 Pro Deep Think provides unparalleled performance. It also introduces features like native audio for more expressive conversations and optimizations that make it faster and more accurate than previous versions. -
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Molmo
Ai2
Molmo is a family of open, state-of-the-art multimodal AI models developed by the Allen Institute for AI (Ai2). These models are designed to bridge the gap between open and proprietary systems, achieving competitive performance across a wide range of academic benchmarks and human evaluations. Unlike many existing multimodal models that rely heavily on synthetic data from proprietary systems, Molmo is trained entirely on open data, ensuring transparency and reproducibility. A key innovation in Molmo's development is the introduction of PixMo, a novel dataset comprising highly detailed image captions collected from human annotators using speech-based descriptions, as well as 2D pointing data that enables the models to answer questions using both natural language and non-verbal cues. This allows Molmo to interact with its environment in more nuanced ways, such as pointing to objects within images, thereby enhancing its applicability in fields like robotics and augmented reality. -
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Veo 3
Google
Veo 3 is Google’s latest state-of-the-art video generation model, designed to bring greater realism and creative control to filmmakers and storytellers. With the ability to generate videos in 4K resolution and enhanced with real-world physics and audio, Veo 3 allows creators to craft high-quality video content with unmatched precision. The model’s improved prompt adherence ensures more accurate and consistent responses to user instructions, making the video creation process more intuitive. It also introduces new features that give creators more control over characters, scenes, and transitions, enabling seamless integration of different elements to create dynamic, engaging videos. -
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Lyria 2
Google
Lyria 2 is an advanced AI music generation model developed by Google, designed to help musicians compose high-fidelity music across a wide variety of genres and styles. The model generates professional-grade 48kHz stereo audio, capturing intricate details and nuances in different instruments and playing styles. With granular creative control, musicians can use text prompts to shape compositions, adjusting elements like key, BPM, and other characteristics to match their artistic vision. Lyria 2 accelerates the creative process by providing new starting points, suggesting harmonies, and drafting longer arrangements, helping musicians overcome writer's block and explore new creative possibilities. -
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Gemini Diffusion
Google DeepMind
Gemini Diffusion is our state-of-the-art research model exploring what diffusion means for language and text generation. Large-language models are the foundation of generative AI today. We’re using a technique called diffusion to explore a new kind of language model that gives users greater control, creativity, and speed in text generation. Diffusion models work differently. Instead of predicting text directly, they learn to generate outputs by refining noise, step by step. This means they can iterate on a solution very quickly and error correct during the generation process. This helps them excel at tasks like editing, including in the context of math and code. Generates entire blocks of tokens at once, meaning it responds more coherently to a user’s prompt than autoregressive models. Gemini Diffusion’s external benchmark performance is comparable to much larger models, whilst also being faster.
