AI Models

OpenAI's most advanced multimodal model, capable of processing and generating text, images, and potentially other data types in real-time. It features a 128,000-token context window, delivering improved reasoning, reduced latency, and enhanced instruction-following compared to previous models. GPT-4o achieves state-of-the-art performance across benchmarks like MMLU and excels in applications requiring real-time interaction, such as conversational agents, creative writing, and multimodal analysis.

A compact, cost-efficient variant of GPT-4o, retaining 70% of its multimodal performance with a 128,000-token context window. It supports text generation, image understanding, and code generation at a fraction of the cost, making it ideal for budget-conscious applications like lightweight chatbots, content generation, and educational tools. GPT-4o Mini balances performance and affordability while maintaining strong reasoning capabilities.

A highly efficient, affordable model designed for everyday tasks, featuring a 32,000-token context window. O3 Mini excels in rapid text and code generation with basic reasoning capabilities, making it perfect for high-volume applications such as customer support chatbots, automated responses, and simple scripting tasks. Its low cost and fast processing speed ensure scalability for routine operations.

OpenAI's powerful reasoning-focused model with exceptional understanding of complex tasks and nuanced instructions. With a massive context window of 1,000,000 tokens, it offers unprecedented depth in analysis and generation capabilities. O1 excels at multi-step reasoning, complex problem-solving, and handling extended conversations with retention of subtle details.

A preview release of OpenAI's next-generation model, offering enhanced reasoning, instruction following, and knowledge compared to GPT-4o. With a 128,000-token context window, it brings improved consistency in outputs and better handling of complex, multi-step tasks. This model represents an interim advancement toward future capabilities while maintaining API compatibility with existing applications.

A powerful, experimental long-context foundation model with a massive 1,000,000 token context window. Quasar Alpha excels at processing and reasoning over extensive documents, code repositories, and multi-part instructions, making it ideal for research, complex analysis, and tasks requiring integration of information across vast amounts of content.

A cost-effective embedding model designed for efficient text representation and semantic search applications. It converts text into numerical vectors that capture semantic meaning, enabling similarity comparisons, clustering, and classification. This compact model balances performance and efficiency for production-level semantic search, recommendation systems, and content organization.

Anthropic's flagship model introducing 'visible thinking,' which displays its reasoning process in real-time for enhanced transparency. With a 200,000-token context window, it offers top-tier reasoning, coding, and multimodal capabilities (text and images). Claude 3.7 Sonnet excels in complex tasks like software development, data analysis, and problem-solving, with low hallucination rates and robust safety features, making it a trusted choice for critical applications.

A highly capable Anthropic model with a 200,000-token context window, offering exceptional reasoning, coding, and multimodal (text and image) performance. It includes advanced safety guardrails and improved multilingual support, making it suitable for secure, complex tasks like legal analysis, technical writing, and visual data interpretation. Claude 3.5 Sonnet is known for its low hallucination rate and precise outputs.

An upgraded version of Claude 3.5 Haiku with a 200,000-token context window, featuring improved reasoning, coding, and visual understanding. It retains the original’s speed and cost-efficiency while enhancing performance on complex prompts, making it suitable for advanced real-time applications like technical support, code review, and detailed image-based tasks.

A balanced model in Anthropic's Claude 3.5 family, blending performance and efficiency with a 48,000-token context window. It delivers strong reasoning, factual accuracy, and balanced abilities across tasks like content generation, summarization, and basic coding. Claude 3.5 Haiku offers enhanced performance over previous generations while maintaining cost-effectiveness, making it suitable for personal productivity and everyday business applications.

An updated version of Anthropic's Claude 3.5 Sonnet model with enhanced reasoning capabilities and reduced hallucinations. It maintains the 200,000-token context window while providing improved performance on complex tasks, better instruction following, and more consistent outputs. This version offers refined capabilities for enterprise applications, professional content creation, and advanced reasoning tasks.

A premium multimodal model from Amazon’s Nova family, featuring a 300,000-token context window and top-tier accuracy, speed, and cost-efficiency. It excels in processing text, images, and videos with advanced reasoning, making it ideal for agentic workflows, enterprise analytics, and complex multimodal tasks like video summarization and document processing.

A high-performance model from Amazon’s Titan family with a 128,000-token context window, designed for enterprise applications. It offers complex reasoning, enhanced guardrails, and customization options, excelling in retrieval-augmented generation (RAG) and agent-based workflows in secure environments like financial analysis and compliance monitoring.

A cost-effective multimodal model from Amazon’s Nova family with a 128,000-token context window, optimized for high-throughput tasks. It supports text, image, and video processing, making it suitable for applications like customer interactions, document analysis, and lightweight visual content generation, all at a competitive price point.

An ultra-efficient text-only model from Amazon’s Nova family with a 128,000-token context window, designed for low-latency, high-throughput text processing. It’s ideal for simple tasks like chatbots, text summarization, and automated responses in cost-sensitive scenarios, offering minimal overhead and maximum scalability.

A fast, economical text model from Amazon’s Titan family with an 8,000-token context window, tailored for high-volume text processing. It provides reliable performance for enterprise use cases like conversational chat, text generation, and basic automation, balancing speed and cost-efficiency effectively.

An ultra-lightweight text model from Amazon’s Titan family with a 4,000-token context window, offering basic text processing at the lowest cost. It’s optimized for simple, high-volume applications like short-form content generation and lightweight chatbots, prioritizing affordability and efficiency.

A fast, efficient multimodal model from Google’s Gemini 2.0 family with a 128,000-token context window, supporting text, image, and basic video understanding. It delivers strong performance for everyday tasks like content creation, image analysis, and quick coding, offering a cost-effective solution with excellent speed and scalability.

Google's flagship model in the Gemini 2.0 family, combining strong reasoning, multimodal understanding, and extensive knowledge with a 128,000-token context window. It excels in complex tasks demanding sophisticated reasoning, such as in-depth analysis, nuanced content creation, and advanced coding projects. Gemini 2.0 Pro offers balanced capabilities across text, code, and image understanding, making it well-suited for professional applications and development workflows.

Google's advanced next-generation multimodal model with an exceptional 1,000,000 token context window, capable of handling massive inputs including entire books, legal files, or multimedia content. It offers improved reasoning, better instruction following, and enhanced capabilities across text, image, audio and video processing. The model excels at complex problem-solving, extensive document analysis, and creative content generation with remarkable consistency and factual accuracy.

An experimental Gemini model specialized in text-to-image generation capabilities with a 32,000-token context window. It converts detailed text prompts into high-quality, creative images with strong understanding of styles, concepts, and composition. This model bridges text understanding and visual creation, making it ideal for creative professionals, marketing content generation, and visual prototyping.

A compact multimodal model from Google's Gemma 3 family with a 128,000-token context window, capable of processing both text and images. It provides efficient performance for everyday tasks with minimal computational requirements, making it ideal for lightweight applications, personal projects, and educational tools where deployment efficiency is prioritized over maximum capability.

A mid-sized multimodal model in Google's Gemma 3 family with a 128,000-token context window, offering an excellent balance of performance and efficiency. It provides strong reasoning and multimodal capabilities for a wide range of applications, from content creation and analysis to development and research projects, with reasonable computational requirements.

The largest model in Google's Gemma 3 family with a 128,000-token context window, offering advanced multimodal capabilities for processing text and images. It delivers sophisticated reasoning, nuanced content generation, and robust code understanding, making it ideal for professional applications, research projects, and complex tasks requiring depth of analysis and substantial computational resources.

The flagship model in Meta's Llama 3.3 family with a 128,000-token context window, designed for enterprise and research applications. It delivers state-of-the-art performance across reasoning, coding, and language understanding, with enhanced multilingual support and robust safety guardrails. Llama 3.3 70B excels in complex tasks like detailed content creation, technical problem-solving, and advanced applications requiring sophisticated reasoning.

An efficient open-weight model from Meta's Llama 3.1 family with an 8,000-token context window, optimized for accessibility and local deployment. It provides strong performance with improved multilingual support and instruction following, ideal for cost-effective solutions like lightweight chatbots, educational tools, and on-device AI applications.

A sophisticated multimodal mixture-of-experts model from Meta with a 10,000,000-token context window, offering unprecedented document processing capabilities. It excels in multimodal understanding, processing text and images with exceptional reasoning and knowledge. As Meta's most advanced model, it delivers superior performance for enterprise applications, research, and complex analytical tasks.

A balanced multimodal mixture-of-experts model from Meta with a 10,000,000-token context window, offering comprehensive document processing capabilities. It provides strong multimodal understanding of text and images with 17 billion active parameters across 16 expert modules. This model delivers excellent performance for general applications requiring robust reasoning and image analysis.

A mid-sized model from Meta's Llama 3.2 family with a 128,000-token context window, offering improved reasoning and instruction following. It provides strong performance across general tasks with efficient resource utilization, making it suitable for applications requiring a balance of capability and deployment efficiency.

A highly compact model from Meta's Llama 3.2 family with a 32,000-token context window, designed for resource-constrained environments. It offers surprisingly strong performance for its size, making it ideal for edge devices, on-device applications, and scenarios requiring minimal computational resources.

A lightweight model from Meta's Llama 3.2 family with a 64,000-token context window, offering impressive capabilities for its compact size. It balances efficiency and performance for applications requiring local deployment or constrained resources while maintaining reasonable context handling and reasoning abilities.

The largest model in Meta's Llama 3.2 family with a 128,000-token context window, offering exceptional reasoning, instruction following, and knowledge encoding. It delivers state-of-the-art performance for complex tasks requiring sophisticated analysis, detailed content generation, and technical problem-solving, making it suitable for research and enterprise applications.

Mistral AI's flagship model with a 32,000-token context window, delivering exceptional performance across reasoning, coding, and knowledge-intensive tasks. It achieves top-tier scores on the MMLU benchmark (81.2%) and excels in complex reasoning, detailed content creation, and technical problem-solving, making it ideal for advanced applications in research, enterprise, and professional content generation.

A mid-sized model from Mistral AI's 3.1 series with a 32,000-token context window, offering strong performance with improved reasoning and instruction following. It provides an excellent balance between capability and cost, making it suitable for a wide range of production applications including content generation, customer support, and technical assistance.

A compact model from Mistral AI with an 8,000-token context window, designed for efficiency and accessibility. It provides solid performance for everyday tasks with minimal computational requirements, making it ideal for lightweight applications, personal tools, and scenarios requiring balanced performance and resource efficiency.

A large reasoning-focused model with a 128,000-token context window, built on the Llama architecture. It specializes in logical analysis, problem-solving, and handling complex multi-step reasoning chains, particularly for technical and analytical tasks. DeepSeek R1 Llama 70B excels in applications requiring depth of analysis, such as research assistance, technical Q&A, and complex data interpretation.

A versatile reasoning-focused foundation model with a 128,000-token context window, designed for comprehensive analytical tasks. It offers strong logical reasoning, consistent output quality, and reliable performance across domains. DeepSeek R1 excels in application areas requiring careful analysis, knowledge synthesis, and complex reasoning, such as research, education, and specialized knowledge work.

A next-generation model from DeepSeek with a 64,000-token context window, featuring enhanced reasoning, multilingual capabilities, and improved instruction following. It delivers advanced performance across domains with particular strength in technical content, code generation, and complex problem-solving while maintaining efficient response generation and processing requirements.

A mid-sized DeepSeek reasoning model with a 128,000-token context window, based on the Qwen architecture. It provides strong logical reasoning and complex task completion with lower computational demands, making it ideal for efficient analytical applications like data interpretation, automated reasoning, and technical problem-solving.

An open-source Mistral model with a 32,000-token context window, optimized with NVIDIA NeMo for superior performance and deployment flexibility. It features enhanced efficiency and hardware acceleration, making it suitable for high-performance inference on NVIDIA platforms, ideal for developers and researchers needing customizable, efficient AI solutions.

A specialized Perplexity model with a 128,000-token context window, built on Llama 3.1 for real-time information retrieval. It integrates web search capabilities and knowledge-intensive training, providing factually accurate responses for applications like research assistance, question answering, and current event analysis.

An advanced Perplexity model with a 128,000-token context window, combining strong reasoning with real-time information retrieval. It excels in complex analytical tasks requiring up-to-date data, such as market research, policy analysis, and detailed problem-solving, offering a blend of logical depth and factual accuracy.

Perplexity’s flagship model with a 128,000-token context window, featuring industry-leading online search and information synthesis. It provides real-time web access, source attribution, and high factual accuracy, making it ideal for knowledge-intensive applications like academic research, journalism, and real-time decision-making support.

An advanced multilingual model from Alibaba Cloud with a 128,000-token context window, offering excellent performance across languages and tasks. It features sophisticated reasoning and code generation, making it ideal for global applications like cross-lingual content creation, technical development, and enterprise-grade automation.

An optimized Qwen model with a 32,000-token context window, enhanced for production-grade applications. It offers strong multilingual support and efficient processing, tailored for enterprise use cases like customer service automation, multilingual document analysis, and high-throughput workflows requiring reliable performance.