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.

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.

A fast, cost-effective Anthropic model with a 200,000-token context window, optimized for high-throughput, real-time applications. It delivers strong reasoning and multimodal capabilities (text and images) at a lower cost, making it ideal for tasks like customer service automation, content moderation, and quick visual analysis. Claude 3.5 Haiku balances speed, efficiency, and performance.

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 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 most advanced multimodal model with an unprecedented 2,000,000-token context window, designed for complex reasoning and sophisticated multimodal tasks. It excels in deep content generation, advanced coding, and detailed image/video analysis, making it ideal for research, creative projects, and enterprise applications requiring extensive context retention.

An ultra-efficient version of Gemini 2.0 Flash with a 32,000-token context window, optimized for maximum performance at minimal cost. It retains core multimodal capabilities (text and images), making it suitable for high-volume, cost-sensitive applications like lightweight chatbots, basic image processing, and rapid text generation.

An open-weight Google model with an 8,000-token context window, designed for efficiency and local deployment. It offers improved reasoning, coding, and multilingual capabilities, making it ideal for edge devices, on-device applications, and research projects needing a compact yet powerful model.

Meta’s latest open-weight model with a 128,000-token context window, delivering near-top-tier performance with lower computational demands. It features enhanced reasoning, coding, and multilingual capabilities, making it a versatile choice for high-performance applications like academic research, software development, and global content generation.

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.

An efficient Mistral AI model with a 32,000-token context window, offering strong reasoning, instruction following, and coding capabilities. Its compact size makes it suitable for a wide range of applications, including content generation, task automation, and lightweight software development, balancing performance and resource efficiency.

An ultra-compact Mistral AI model with an 8,000-token context window, designed for resource-constrained environments like mobile devices and embedded systems. It delivers impressive performance for its size, excelling in lightweight tasks such as text generation, basic coding, and on-device automation with minimal latency.

Mistral AI’s flagship model with a 32,000-token context window, offering exceptional reasoning, coding, and multilingual capabilities. Scoring 81.2% on the MMLU benchmark, it excels in complex problem-solving, creative writing, and technical tasks, making it a powerful choice for advanced applications in research, development, and global communication.

A specialized Mistral AI model with a 32,000-token context window, purpose-built for content moderation. It detects and filters harmful or policy-violating content with high accuracy, leveraging specialized training for safety and compliance applications like social media monitoring and user-generated content review.

An enterprise-focused Mistral AI model with a 32,000-token context window, optimized for business applications and professional workflows. It features enhanced reasoning and document processing capabilities, making it ideal for tasks like contract analysis, report generation, and specialized knowledge extraction in corporate environments.

A mid-sized Mistral AI model with a 32,000-token context window, offering a balanced mix of performance and efficiency. It provides strong reasoning, coding, and instruction-following capabilities at a reasonable cost, making it suitable for production applications like customer support automation, content creation, and moderate-scale development tasks.

An advanced multimodal Mistral AI model with a 32,000-token context window, specializing in image understanding and visual reasoning. It excels in complex visual tasks like detailed image analysis, object recognition, and multimodal problem-solving, making it ideal for applications in design, education, and visual data interpretation.

A dedicated code generation model from Mistral AI with a 32,000-token context window, designed for software development and debugging. It excels in code completion, testing, and generation across multiple programming languages, offering enhanced contextual understanding for tasks like automated coding, script optimization, and developer assistance.

A reasoning-focused DeepSeek model with a 128,000-token context window, built on the Llama architecture. It offers advanced logical reasoning and problem-solving capabilities, excelling in complex task automation, mathematical analysis, and reasoning benchmarks, making it a strong choice for research and technical applications.

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.