List of the Top Multimodal Models for Amazon Bedrock in 2026 - Page 2

Grok 4.3 is a next-generation AI model from xAI that expands on the capabilities of the Grok 4 series with improved reasoning, real-time intelligence, and automation features. It is designed to handle complex, multi-step tasks such as coding, research, and decision-making with greater accuracy and consistency. The model integrates real-time data from the web and X, allowing it to provide up-to-date answers and insights. Grok 4.3 supports multimodal functionality, enabling it to process and generate content across text, images, and other formats. It operates within the SuperGrok Heavy tier, which offers enhanced compute power and access to advanced features. The model includes long-context capabilities, allowing it to analyze large datasets and extended conversations effectively. It also supports tool use and integrations, enabling it to interact with external systems and automate workflows. Grok 4.3 benefits from the multi-agent “heavy” configuration, which improves performance on complex reasoning tasks. It is optimized for speed, responsiveness, and real-time interaction. The model can be used for a wide range of applications, including software development, research, and business analysis. It builds on Grok’s foundation as an AI assistant integrated with modern platforms and environments. The system continues to evolve with ongoing updates and feature enhancements. Overall, Grok 4.3 represents a powerful AI solution for users seeking real-time intelligence and advanced automation capabilities.

The newest version of the open-source AI framework, which can be customized and utilized across different platforms, is available in several configurations: 1B, 3B, 11B, and 90B, while still offering the option to use Llama 3.1. Llama 3.2 includes a selection of large language models (LLMs) that are pretrained and fine-tuned specifically for multilingual text processing in 1B and 3B sizes, whereas the 11B and 90B models support both text and image inputs, generating text outputs. This latest release empowers users to build highly effective applications that cater to specific requirements. For applications running directly on devices, such as summarizing conversations or managing calendars, the 1B or 3B models are excellent selections. On the other hand, the 11B and 90B models are particularly suited for tasks involving images, allowing users to manipulate existing pictures or glean further insights from images in their surroundings. Ultimately, this broad spectrum of models opens the door for developers to experiment with creative applications across a wide array of fields, enhancing the potential for innovation and impact.

The latest iteration in the Llama series, Llama 3.3, marks a notable leap forward in the realm of language models, designed to improve AI's abilities in both understanding and communication. It features enhanced contextual reasoning, more refined language generation, and state-of-the-art fine-tuning capabilities that yield remarkably accurate, human-like responses for a wide array of applications. This version benefits from a broader training dataset, advanced algorithms that allow for deeper comprehension, and reduced biases when compared to its predecessors. Llama 3.3 excels in various domains such as natural language understanding, creative writing, technical writing, and multilingual conversations, making it an invaluable tool for businesses, developers, and researchers. Furthermore, its modular design lends itself to adaptable deployment across specific sectors, ensuring consistent performance and flexibility even in expansive applications. With these significant improvements, Llama 3.3 is set to transform the benchmarks for AI language models and inspire further innovations in the field. It is an exciting time for AI development as this new version opens doors to novel possibilities in human-computer interaction.

GPT-5.5 Pro represents a new class of AI designed to transform how work gets done across digital environments. It combines advanced reasoning, tool usage, and task execution capabilities to handle complex, multi-step workflows with minimal human intervention. The model excels in areas such as software engineering, data analysis, business operations, and scientific research, where it can plan tasks, gather information, test solutions, and refine outputs continuously. It supports creating applications, generating reports, building spreadsheets, and navigating software systems as part of a complete workflow. A key capability is its integration with workspace agents—custom AI agents that can be built once and deployed across teams to automate entire processes. These agents can run tasks on schedules, interact with tools like CRM systems, messaging platforms, and document editors, and keep workflows moving without constant supervision. Organizations can define permissions, approval checkpoints, and monitoring to maintain control over automated processes. GPT-5.5 Pro also enhances collaboration by enabling teams to standardize workflows and scale best practices across the organization. With enterprise-grade security and governance, it ensures safe deployment in complex environments. Its ability to persist through ambiguity and long tasks makes it highly effective for execution-heavy work. By reducing manual intervention and increasing speed, it allows teams to focus on higher-value activities. Ultimately, GPT-5.5 Pro enables businesses and professionals to operate at a significantly higher level of productivity and efficiency.

Claude Mythos 5.2 is a frontier AI model developed by Anthropic to support advanced cybersecurity research, biomedical discovery, autonomous software engineering, and complex scientific reasoning. The model shares the same underlying architecture as Claude Fable 5.2 but is made available to verified organizations that require access to capabilities restricted by Fable's cybersecurity and biology safeguards. Its cybersecurity research capabilities include analyzing source code, identifying vulnerabilities, investigating software weaknesses, validating security findings, and supporting authorized penetration testing activities. Claude Mythos 5.2 can examine historical software versions to independently rediscover known vulnerabilities, helping researchers evaluate security risks and prioritize remediation efforts. Anthropic reports that the model identified 10.2% more vulnerabilities than Claude Opus 5.5 and 14.4% more than Claude Mythos 5.1 in its internal vulnerability rediscovery evaluations. Its published cybersecurity benchmark results include 76.3% on CyScenarioBench and 89.6% on CVE Rediscovery, reflecting improvements in technical security reasoning and vulnerability identification. In biological research, the model supports protein engineering, computational biology, molecular analysis, and scientific problem-solving, achieving 85.1% on a de novo protein binder design evaluation. The underlying model also demonstrates strong performance in software engineering, mathematics, scientific experimentation, visual reasoning, and agentic workflows that require multiple stages of planning and execution. Anthropic reports that Claude Mythos 5.2 generates output up to 30% faster than Mythos 5.1, with an optional fast mode that increases token generation speed by up to 2.5 times.

Pixtral Large is a comprehensive multimodal model developed by Mistral AI, boasting an impressive 124 billion parameters that build upon their earlier Mistral Large 2 framework. The architecture consists of a 123-billion-parameter multimodal decoder paired with a 1-billion-parameter vision encoder, which empowers the model to adeptly interpret diverse content such as documents, graphs, and natural images while maintaining excellent text understanding. Furthermore, Pixtral Large can accommodate a substantial context window of 128,000 tokens, enabling it to process at least 30 high-definition images simultaneously with impressive efficiency. Its performance has been validated through exceptional results in benchmarks like MathVista, DocVQA, and VQAv2, surpassing competitors like GPT-4o and Gemini-1.5 Pro. The model is made available for research and educational use under the Mistral Research License, while also offering a separate Mistral Commercial License for businesses. This dual licensing approach enhances its appeal, making Pixtral Large not only a powerful asset for academic research but also a significant contributor to advancements in commercial applications. As a result, the model stands out as a multifaceted tool capable of driving innovation across various fields.

Amazon Nova signifies a groundbreaking advancement in foundation models (FMs), delivering sophisticated intelligence and exceptional price-performance ratios, exclusively accessible through Amazon Bedrock. The series features Amazon Nova Micro, Amazon Nova Lite, and Amazon Nova Pro, each tailored to process text, image, or video inputs and generate text outputs, addressing varying demands for capability, precision, speed, and operational expenses. Amazon Nova Micro is a model centered on text, excelling in delivering quick responses at an incredibly low price point. On the other hand, Amazon Nova Lite is a cost-effective multimodal model celebrated for its rapid handling of image, video, and text inputs. Lastly, Amazon Nova Pro distinguishes itself as a powerful multimodal model that provides the best combination of accuracy, speed, and affordability for a wide range of applications, making it particularly suitable for tasks like video summarization, answering queries, and solving mathematical problems, among others. These innovative models empower users to choose the most suitable option for their unique needs while experiencing unparalleled performance levels in their respective tasks. This flexibility ensures that whether for simple text analysis or complex multimodal interactions, there is an Amazon Nova model tailored to meet every user's specific requirements.

Amazon Nova Canvas is a sophisticated tool for generating images that creates high-quality visuals from text or image prompts. It also includes a range of features that enable users to edit images using text commands, adjust color palettes and layouts, and has built-in safeguards to promote the ethical use of artificial intelligence. This versatility makes it appealing not only to creative professionals but also to everyday users seeking to enhance their projects. Furthermore, its user-friendly interface ensures that even those with minimal experience can effectively utilize its capabilities.

Amazon Nova Reel is a sophisticated video creation tool that allows users to easily produce high-quality videos from text and images. This cutting-edge platform offers customization via natural language commands, enabling users to adjust visual styles and timing, while also providing options for camera movements. Additionally, it incorporates built-in safeguards to ensure responsible use of AI. Thanks to its intuitive interface, creators can freely explore their artistic ideas while remaining compliant with ethical standards, making it a versatile choice for both amateurs and professionals.

Amazon Nova Lite is an efficient multimodal AI model built for speed and cost-effectiveness, handling image, video, and text inputs seamlessly. Ideal for high-volume applications, Nova Lite provides fast responses and excellent accuracy, making it well-suited for tasks like interactive customer support, content generation, and media processing. The model supports fine-tuning on diverse input types and offers a powerful solution for businesses that prioritize both performance and budget.

Claude Opus 4.1 marks a significant iterative improvement over its earlier version, Claude Opus 4, with a focus on enhancing capabilities in coding, agentic reasoning, and data analysis while keeping deployment straightforward. This latest iteration achieves a remarkable coding accuracy of 74.5 percent on the SWE-bench Verified, alongside improved research depth and detailed tracking for agentic search operations. Additionally, GitHub has noted substantial progress in multi-file code refactoring, while Rakuten Group highlights its proficiency in pinpointing precise corrections in large codebases without introducing errors. Independent evaluations show that the performance of junior developers has seen an increase of about one standard deviation relative to Opus 4, indicating meaningful advancements that align with the trajectory of past Claude releases.

Claude Sonnet 4.5 marks a significant milestone in Anthropic's development of artificial intelligence, designed to excel in intricate coding environments, multifaceted workflows, and demanding computational challenges while emphasizing safety and alignment. This model establishes new standards, showcasing exceptional performance on the SWE-bench Verified benchmark for software engineering and achieving remarkable results in the OSWorld benchmark for computer usage; it is particularly noteworthy for its ability to sustain focus for over 30 hours on complex, multi-step tasks. With advancements in tool management, memory, and context interpretation, Claude Sonnet 4.5 enhances its reasoning capabilities, allowing it to better understand diverse domains such as finance, law, and STEM, along with a nuanced comprehension of coding complexities. It features context editing and memory management tools that support extended conversations or collaborative efforts among multiple agents, while also facilitating code execution and file creation within Claude applications. Operating at AI Safety Level 3 (ASL-3), this model is equipped with classifiers designed to prevent interactions involving dangerous content, alongside safeguards against prompt injection, thereby enhancing overall security during use. Ultimately, Sonnet 4.5 represents a transformative advancement in intelligent automation, poised to redefine user interactions with AI technologies and broaden the horizons of what is achievable with artificial intelligence. This evolution not only streamlines complex task management but also fosters a more intuitive relationship between technology and its users.

Claude Opus 4.5 represents a major leap in Anthropic’s model development, delivering breakthrough performance across coding, research, mathematics, reasoning, and agentic tasks. The model consistently surpasses competitors on SWE-bench Verified, SWE-bench Multilingual, Aider Polyglot, BrowseComp-Plus, and other cutting-edge evaluations, demonstrating mastery across multiple programming languages and multi-turn, real-world workflows. Early users were struck by its ability to handle subtle trade-offs, interpret ambiguous instructions, and produce creative solutions—such as navigating airline booking rules by reasoning through policy loopholes. Alongside capability gains, Opus 4.5 is Anthropic’s safest and most robustly aligned model, showing industry-leading resistance to strong prompt-injection attacks and lower rates of concerning behavior. Developers benefit from major upgrades to the Claude API, including effort controls that balance speed versus capability, improved context efficiency, and longer-running agentic processes with richer memory. The platform also strengthens multi-agent coordination, enabling Opus 4.5 to manage subagents for complex, multi-step research and engineering tasks. Claude Code receives new enhancements like Plan Mode improvements, parallel local and remote sessions, and better GitHub research automation. Consumer apps gain better context handling, expanded Chrome integration, and broader access to Claude for Excel. Enterprise and premium users see increased usage limits and more flexible access to Opus-level performance. Altogether, Claude Opus 4.5 showcases what the next generation of AI can accomplish—faster work, deeper reasoning, safer operation, and richer support for modern development and productivity workflows.

GPT-6 Cyber is a frontier AI cybersecurity model developed by OpenAI to support autonomous vulnerability research, exploit validation, secure software development, and advanced security testing. The model is specifically trained to investigate complex software environments, discover exploitable vulnerabilities, and help security professionals develop effective remediation strategies. Its capabilities include source code analysis, reverse engineering, vulnerability reproduction, exploit development, and investigation of security weaknesses across large software projects. GPT-6 Cyber can conduct extended security research workflows that involve identifying potential attack paths, testing hypotheses, validating technical findings, and producing reproducible evidence for security teams. During OpenAI's research campaigns, the model identified 81 previously unknown high-severity Firefox vulnerabilities and helped develop candidate patches for affected components. It also reproduced 1,724 Linux kernel vulnerabilities and generated working exploits for 948 of them, demonstrating its ability to validate security issues at scale. On the ExploitGym benchmark, GPT-6 Cyber achieved a 48.5% success rate compared with 32.1% for GPT-5.6 Cyber, while achieving 40.3% vulnerability recall in an internal web penetration testing evaluation. The model incorporates specialized alignment training to follow authorized testing scopes, respect security restrictions, and avoid exploiting systems that fall outside the researcher's stated objectives. In OpenAI's ExploitGym Honeypot evaluation, GPT-6 Cyber avoided attacking out-of-scope services in 99.93% of tests without deployment safety instructions and 100% of tests when those instructions were included. Its deployment safeguards include misalignment monitoring, misuse detection, IP allowlisting for API access, human oversight recommendations, and additional protections for operational technology and critical infrastructure systems.

Amazon Titan is a suite of advanced foundation models from AWS, specifically designed to enhance generative AI applications with remarkable performance and flexibility. Drawing on over 25 years of AWS's deep-rooted knowledge in artificial intelligence and machine learning, Titan models support a diverse range of functions, such as text generation, summarization, semantic search, and image creation. These models emphasize the importance of responsible AI by incorporating safety features and fine-tuning options. Moreover, they facilitate customization through Retrieval Augmented Generation (RAG), which improves accuracy and relevance, making them ideal for both general and niche AI applications. The innovative architecture and powerful functionalities of Titan models mark a noteworthy progression in the realm of artificial intelligence, paving the way for more sophisticated AI solutions. Their ability to adapt to user-specific needs further underscores their significance in various industries.

Nova 2 Omni represents a groundbreaking advancement in technology, as it effectively combines multimodal reasoning and generation, enabling it to understand and produce a variety of content types such as text, images, video, and audio. Its impressive ability to handle extremely large inputs, which can range from hundreds of thousands of words to several hours of audiovisual content, allows for coherent analysis across different formats. Consequently, it can simultaneously process extensive product catalogs, lengthy documents, customer feedback, and complete video libraries, equipping teams with a single solution that negates the need for multiple specialized models. By consolidating mixed media within a cohesive workflow, Nova 2 Omni opens doors to new possibilities in both creative endeavors and operational efficiency. For example, a marketing team can provide product specifications, brand guidelines, reference images, and video materials to effortlessly craft a comprehensive campaign encompassing messaging, social media posts, and visuals, all through a simplified process. This remarkable efficiency not only boosts productivity but also encourages innovative approaches to marketing strategies, transforming the way teams collaborate and execute their plans. With such capabilities, organizations can look forward to enhanced creativity and streamlined operations like never before.

GPT-6.2 Astra is a reportedly experimental frontier AI model associated with OpenAI's Astra family of advanced reasoning and autonomous task execution systems. The model has not been officially announced, but references to the identifier gpt-6.2-astra have appeared in screenshots of Codex usage analytics and reported response records. These observations have prompted speculation that OpenAI may be testing an updated Astra model through limited or experimental deployment. The model is expected to build on GPT-6 Astra's capabilities in complex reasoning, software engineering, and autonomous execution of professional workflows. Potential software development applications include generating source code, debugging applications, analyzing repositories, implementing features, and coordinating automated testing. Its anticipated reasoning capabilities may support mathematical analysis, scientific problem-solving, technical documentation, and research tasks requiring multiple stages of evaluation. GPT-6.2 Astra could also provide improvements in long-horizon agentic workflows that require planning, interacting with external tools, maintaining task context, and executing complex operations. Integration with development environments such as Codex may allow AI agents to work with project files, terminal commands, software repositories, and programming tools. Improvements in instruction adherence, tool execution reliability, task persistence, and accurate reporting of completed actions may be important objectives for a future Astra release. However, no independently verified benchmarks establish whether GPT-6.2 Astra improves on earlier models, and its relationship to other rumored OpenAI model variants remains uncertain. OpenAI has not confirmed the model's architecture, supported modalities, context window, reasoning settings, API pricing, commercial availability, or official launch schedule.

GPT-6.2 Sol is a reportedly experimental artificial intelligence model associated with OpenAI's Sol family of reasoning and professional productivity models. Although OpenAI has not officially announced the model, references to the identifier gpt-6.2-sol have appeared in screenshots reportedly captured from Google Cloud Console quota settings. These observations suggest possible preparation for internal testing, cloud deployment, or future availability, but they do not establish that the model has been released. GPT-6.2 Sol is expected to build on GPT-6.1 Sol's capabilities in complex reasoning, software engineering, professional knowledge work, and autonomous task execution. Potential software development applications include generating code, identifying programming errors, analyzing software repositories, implementing features, and validating changes through automated testing. Its reasoning capabilities may support mathematical analysis, scientific research, technical documentation, business planning, and other knowledge-intensive activities requiring structured problem-solving. The model could also support agentic workflows involving external tools, application integrations, information retrieval, and multistep task execution across business systems. Improvements in long-context processing, factual reliability, instruction adherence, and efficient tool execution may be relevant development objectives, although none have been confirmed specifically for GPT-6.2 Sol. The broader Sol model family has been designed to balance advanced intelligence with computational efficiency, making it relevant to organizations deploying AI across demanding workloads. If officially released, GPT-6.2 Sol may become available through OpenAI's developer APIs, coding tools, or enterprise products, but its distribution channels remain unconfirmed.

OpenAI Bel is a rumored artificial intelligence foundation model associated with a reportedly large-scale pretraining initiative at OpenAI. The project became the subject of online discussion following unofficial reports published in August 2026. These reports describe Bel as a possible successor to an earlier training effort identified by the codename Doug. According to the speculation, Bel could provide a pretrained foundation for future AI systems developed through additional training and refinement. Some reports connect the project to OpenAI's Astra model family and potential successors to GPT-6, although these relationships have not been confirmed. Unofficial sources have suggested a parameter count exceeding 10 trillion, but no technical documentation substantiates that figure. OpenAI has not disclosed the model's architecture, training data composition, computational requirements, or supported input and output modalities. Its capabilities in natural language processing, software development, mathematical reasoning, multimodal understanding, and autonomous task execution have not been publicly established. No verified benchmarks, independent evaluations, or published model cards are available for Bel. The project has no confirmed public interface, API endpoint, licensing terms, pricing structure, or release date. Bel remains an unverified model codename rather than a commercially available AI system with documented features and capabilities.