Top Llama 4 Maverick Alternatives

Gemini 3.6 Flash is a new Google Gemini model designed for efficient, high-quality AI agents and production workloads. It builds on Gemini 3.5 Flash with improvements in coding, knowledge work, multimodal understanding, computer use, and complex workflow execution. Google positions Gemini 3.6 Flash as the workhorse model in the Flash series, optimized for the balance of quality, speed, reliability, and cost. The model is designed to reduce verbosity, use fewer output tokens, take fewer reasoning steps, and require fewer tool calls during multi-step tasks. Google says Gemini 3.6 Flash uses 17% fewer output tokens than 3.5 Flash on the Artificial Analysis Index and can reduce output usage even more on some coding benchmarks. It is priced at $1.50 per 1 million input tokens and $7.50 per 1 million output tokens, giving developers a lower-cost option for agentic workflows than 3.5 Flash. Gemini 3.6 Flash shows gains in benchmarks for software engineering, ML research, computer use, and knowledge work. It can support use cases such as code migration, document parsing, financial data analysis, chart interpretation, report drafting, visual interface building, and multi-agent orchestration. Built-in computer use is available through the Gemini API and Gemini Enterprise, helping agents interact with digital tools more reliably. Google also says the model ships with enhanced Frontier Safety safeguards for CBRN and cyber offense misuse while minimizing refusals for beneficial use cases. By combining lower cost, stronger task performance, multimodal understanding, built-in computer use, and safety improvements, Gemini 3.6 Flash is built for teams that need scalable AI agents across software, enterprise, and productivity workflows.

MiMo-V2.6-Flash is an open-source, natively omnimodal AI model from Xiaomi MiMo built for users that need strong agentic and multimodal capabilities at a comparatively low operating cost. It is the efficiency-oriented model in the MiMo-V2.6 family, complementing the higher-capability MiMo-V2.6-Pro model. MiMo-V2.6-Flash supports software engineering, terminal-based workflows, tool use, automation, computer interaction, visual reasoning, and other multi-step agent tasks. Its multimodal abilities allow it to work with text, images, video, rendered environments, and other visual inputs when completing complex tasks. Xiaomi demonstrates the MiMo-V2.6 family generating frontend interfaces, presentation decks, 3D scenes, Blender assets, interactive worlds, and other visual outputs from natural-language or reference-based instructions. The models can also coordinate multiple agents, verify rendered results, and iteratively refine generated content based on visual feedback. In embodied simulation environments, MiMo-V2.6 can process multi-view camera feeds and continuously reason about actions such as object grasping, matching, and placement. MiMo-V2.6-Flash was trained with large-scale reinforcement learning across heterogeneous coding, general-agent, visual, and cybersecurity environments. Xiaomi reports that the Flash training run completed approximately 30 reinforcement learning steps across roughly 750,000 trajectories and significantly improved performance on held-out software engineering and automation evaluations. The company has released the MiMo-V2.6 series together with technical documentation, training environments, and reinforcement learning code so researchers can inspect and reproduce portions of the training approach. MiMo-V2.6-Flash is available through MiMo Desktop, AI Studio, MiMo Code, the Xiaomi MiMo API Platform, OpenRouter, and the project’s open-source distribution channels.

Gemini 3.7 Flash is Google’s intelligent workhorse model built for coding, agents, software engineering, knowledge work, web development, and complex business workflows. The model delivers substantial improvements across debugging, issue resolution, first-pass code accuracy, and production-ready code generation. Developers can use Gemini 3.7 Flash to move from prompt to working implementation with fewer revisions and stronger reliability. Its software engineering capabilities make it useful for resolving issues, generating code, improving applications, and supporting agentic coding workflows. For web development, the model can create more functional layouts and feature-complete applications in fewer prompts. It also performs well when following design requirements from screenshots, images, visual references, and complete design systems. Gemini 3.7 Flash supports knowledge-heavy domains such as finance, law, and biosciences with improved reasoning and accuracy. Its complex-document understanding helps users analyze dense materials, extract meaning, and work through specialized information more effectively. The model also supports real-world workflow automation, making it useful for business processes that require structured reasoning and task execution. Multimodal capabilities extend its use cases to interactive web experiences, data stories, robotics, and dynamically generated 3D content. By combining coding strength, agentic execution, web development capability, design adherence, document intelligence, multimodal reasoning, and workflow automation, Gemini 3.7 Flash helps teams build and execute more complex work.

Qwen3.8-Max is a large-scale AI model from Qwen built for coding, coworking, research, long-horizon planning, and multimodal agent workflows. It is positioned as the most capable model in the Qwen family to date, with open weights announced for release after launch. The model uses a 2.4 trillion-parameter architecture with 95 billion active parameters and is available through QwenCloud. Qwen3.8-Max is designed to complete complex, open-ended goals end to end rather than only answer isolated prompts. In coding workflows, it can write and run code, create self-evolving harnesses, normalize requirements into issues, execute tasks through agents, run tests, trigger CI checks, and iterate through feedback. Its autonomous coding examples include a 10+ day project run, a research-paper reproduction and improvement loop, and a 24-hour online competition solution that beat most participating human teams. For professional work, Qwen3.8-Max is built to handle multi-step, tool-heavy workflows across compliance, design, food operations, engineering, rehabilitation, sports analytics, and quantitative research. The model also supports long-horizon decision-making, including autonomous chip-design optimization and extended e-commerce operations simulations. Its multimodal capabilities cover images, complex PDFs, long videos, visual production, interface inspection, frontend reconstruction, Blender visualization, interactive applications, and visual feedback loops. Qwen3.8-Max can be integrated through QwenCloud APIs and used with agent frameworks or coding assistants such as Claude Code, Codex, Qoder CLI, Qwen Code, and OpenClaw. By combining agentic coding, reasoning controls, multimodal understanding, visual self-correction, long-context workflows, tool use, and open-weight availability, Qwen3.8-Max helps developers and organizations build autonomous AI systems that can produce dependable deliverables.

Inkling is an open-weights multimodal AI model from Thinking Machines built to support customization, agentic workflows, coding, reasoning, vision, audio, and enterprise AI use cases. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, 256 routed experts per MoE layer, and six routed experts active per token. It supports context windows up to 1 million tokens and was pretrained on 45 trillion tokens across text, images, audio, and video. Inkling is designed as a broad foundation model rather than a narrowly optimized benchmark model, giving it balanced capabilities across reasoning, coding, factuality, instruction following, vision, audio, tool use, and safety. Its controllable thinking effort lets developers adjust how much computation and generated reasoning the model uses, helping teams balance quality, latency, and cost for different production needs. The model can run agentic coding tasks, use tools, create web apps, generate polished multi-page artifacts, reason over long contexts, and work through iterative refinement loops. For multimodal tasks, Inkling can process images, answer questions about visual content, transcribe and reason over audio, follow spoken instructions, and combine visual reasoning with code-based tools such as Python. Thinking Machines trained Inkling for calibration, instruction following, factual reliability, refusal behavior, and safety across multiple modalities, including evaluations for dangerous capabilities and human-AI threat vectors. Inkling is available on Tinker for fine-tuning, with 64K and 256K context options, an Inkling Playground for testing, cookbook recipes, and support for multimodal post-training workflows. Its full weights are available on Hugging Face, and deployment support is available through APIs and infrastructure partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, SGLang, vLLM, llama.cpp, and transformers.

Gemini 3.5 Pro is Google’s anticipated Pro-tier model for the Gemini 3.5 series, designed for advanced AI workloads that demand stronger reasoning, coding ability, multimodal understanding, and agentic performance. It is expected to sit above faster Gemini Flash models by focusing on depth, accuracy, complex instruction following, and high-quality problem solving. The model is intended for tasks where users need an AI system to plan, reason, analyze, generate code, work across context, and support sophisticated digital workflows. Gemini 3.5 Pro is expected to be useful for software development, autonomous agents, enterprise automation, research assistance, technical analysis, workflow orchestration, and productivity applications. It will likely build on the broader Gemini 3 family’s strengths in multimodal input, tool use, grounding, file handling, code execution, and connected AI experiences. For developers, Gemini 3.5 Pro could provide a powerful foundation for coding copilots, agentic development tools, internal business assistants, customer support automation, and data-heavy applications. For enterprises, it is positioned for higher-stakes workflows where better reasoning and reliability are more important than simply minimizing cost or latency. The model may also appeal to teams building AI systems that need to maintain context across multi-step tasks and adapt as information changes. Because Gemini 3.5 Pro has been discussed by Google but is not yet listed as a standard available model in current official model pages, it should be described as upcoming or anticipated rather than fully launched. Its release is expected to strengthen Google’s Gemini lineup by giving users a more capable Pro option within the Gemini 3.5 generation. For organizations already evaluating Gemini models, Gemini 3.5 Pro is likely to be most relevant when the workload requires maximum intelligence, advanced reasoning, and production-grade AI assistance for complex tasks.

Muse Spark 1.1 is an advanced multimodal reasoning model from Meta Superintelligence Labs built for agentic work, coding, computer use, tool calling, and multimodal understanding. It is a major upgrade from Muse Spark and is designed to push the performance-efficiency frontier for AI systems that need to plan, reason, act, and coordinate across complex workflows. The model can operate across external apps, native tools, MCP servers, custom skills, browsers, scripts, images, videos, PDFs, audio, and developer environments. Muse Spark 1.1 is especially strong in agentic orchestration, where it can gather context, make plans, delegate work to parallel subagents, and manage execution across multiple steps. As a subagent, it can follow a defined role, use available tools appropriately, and escalate back to a main agent when needed. Its 1 million token context window helps it remember past actions, retrieve information from earlier in a project, and compact long sessions while keeping important details available for later work. For computer-use tasks, Muse Spark 1.1 can navigate unfamiliar interfaces, adapt to changing requirements, and choose whether to click through an interface or write scripts when automation is faster. In software engineering, the model can diagnose complex bugs, implement new features, perform large code migrations, build web applications, inspect screenshots, trace issues to code, and validate fixes. Its multimodal capabilities allow it to inspect visual and audio information, generate detailed image and video captions, create visual-to-code artifacts, and combine perception with action in practical workflows. Developers can access Muse Spark 1.1 through Meta’s new Model API public preview, and everyday users can try it in Thinking mode in the Meta AI app.

Gemini 3.5 Flash is Google’s next-generation frontier AI model engineered to combine advanced reasoning, multimodal intelligence, agentic automation, and high-speed performance for developers, enterprises, and everyday users. As the first publicly released model in the Gemini 3.5 family, the platform is designed to execute complex long-horizon workflows while delivering fast response speeds and strong performance across coding, reasoning, multimodal understanding, and AI-driven automation tasks. Gemini 3.5 Flash significantly advances Google’s agentic AI capabilities by enabling AI systems to plan, execute, iterate, and manage multi-step workflows such as software engineering, codebase maintenance, financial analysis, application development, infrastructure operations, and large-scale enterprise automation. Powered by the updated Antigravity harness, the model can coordinate collaborative subagents that work together to complete demanding workflows under supervision while maintaining high reliability and operational efficiency. Gemini 3.5 Flash also demonstrates advanced multimodal capabilities by generating dynamic graphics, interactive web interfaces, animations, and visually rich experiences that support developers and businesses building AI-powered applications and user experiences. The model achieves frontier-level performance across multiple coding, agentic, and multimodal benchmarks while operating at significantly faster output speeds compared to many competing frontier AI systems, helping reduce workflow latency and operational costs. Google has integrated Gemini 3.5 Flash across a broad ecosystem that includes the Gemini app, AI Mode in Google Search, Google AI Studio, Android Studio, Gemini Enterprise Agent Platform, and enterprise AI products to provide global access to advanced AI automation capabilities.

MiniMax M3 is an open-weight multimodal foundation model from MiniMax that brings together coding capability, agentic reasoning, native multimodality, and long-context processing in one model. It is designed for demanding AI workflows where a system needs to understand large amounts of information, reason through multi-step tasks, use tools, and work with different input types. MiniMax M3 supports a context window of up to 1 million tokens, making it useful for large code repositories, long documents, multi-file analysis, research workflows, enterprise automation, and persistent agent memory. The model uses MiniMax Sparse Attention, an architecture built to improve efficiency at very long context lengths by reducing the cost of attention. MiniMax M3 is natively multimodal and can work with text, images, and video inputs, allowing it to support richer workflows than text-only language models. It is positioned for coding, software engineering, tool invocation, browser-style retrieval, computer-use-style tasks, and autonomous task decomposition. The model’s architecture includes a large total parameter count with a smaller number of activated parameters, supporting more efficient inference through a mixture-of-experts design. Developers can use MiniMax M3 to build coding assistants, AI agents, document intelligence systems, multimodal analysis tools, and automated enterprise workflows. Its long-context design helps reduce the need to compress or split large inputs, allowing teams to keep more project context available during reasoning. The model is available through open-weight releases and hosted API providers, giving developers multiple ways to test, deploy, or integrate it into applications. MiniMax M3 helps organizations build advanced AI systems that combine long memory, multimodal understanding, coding strength, and agentic execution.

Gemini 3.5 Flash Cyber is a specialized model tailored for cybersecurity, building on the foundations of Gemini 3.5 Flash, and optimized to effectively identify, validate, and resolve vulnerabilities at scale. Its central aim is to bolster defensive security operations, allowing organizations to swiftly identify critical vulnerabilities and create reliable patches before they can be exploited by malicious actors. The impressive combination of performance and efficiency provided by Flash serves as an excellent foundation for code scanning, evaluating security concerns, verifying the authenticity of findings, and proposing accurate remediation strategies across large software environments. Within the CodeMender framework, multiple Gemini 3.5 Flash Cyber agents work together harmoniously, integrating their insights into a unified report that improves the system’s ability to analyze vulnerabilities from diverse angles and enhance the overall quality of the results. This collaborative approach ensures outstanding performance on CyberGym, a benchmark for measuring cybersecurity effectiveness, while also promoting ongoing advancements in vulnerability management practices. In addition, the capabilities of Gemini 3.5 Flash Cyber not only streamline security workflows but also significantly bolster an organization’s resilience against potential threats, making it an indispensable tool in the landscape of modern cybersecurity. As organizations navigate increasingly complex environments, the advantages offered by this model become even more critical.

The Nova 2 Lite is an advanced reasoning model designed to efficiently tackle common AI-related tasks involving text, images, and video content. It generates coherent, context-aware responses while granting users the ability to customize the "thinking depth," which dictates the internal reasoning process prior to delivering an answer. This adaptability allows teams to choose between rapid replies and more comprehensive solutions according to their unique requirements. Its efficacy shines in scenarios such as customer service chatbots, streamlined documentation automation, and improvements in overall business workflows. The Nova 2 Lite consistently performs well in standard evaluation tests, frequently equaling or exceeding the performance of comparable compact models across various benchmarks, underscoring its reliable comprehension and quality of outputs. Among its standout features are the ability to analyze complex documents, derive accurate insights from video content, generate practical code snippets, and offer well-supported answers based on the data provided. Furthermore, its inherent flexibility positions it as an invaluable resource for a wide array of industries aiming to enhance their AI-powered initiatives, ensuring that organizations can confidently leverage advanced technologies to meet their evolving demands.

GLM-5.3-Flash is an efficiency-focused multimodal AI model from Z.ai that combines advanced reasoning, coding, agentic execution, and visual intelligence. It is the first GLM-5-series model designed with native multimodal capabilities, allowing it to work directly with both textual and visual inputs. The architecture uses 320 billion total parameters while activating only 18 billion at a time, significantly reducing the amount of computation required for inference. A hybrid attention design blends linear attention for local information with sparse attention for retrieving important context from much larger inputs. Z.ai also uses technologies such as IndexPool and Manifold-Constrained Hyper-Connections to improve memory efficiency, latency, and model scaling. The model can operate with context windows of up to one million tokens, making it suitable for large repositories, lengthy documents, extended agent sessions, and complex multimodal workflows. GLM-5.3-Flash was trained on a 30-trillion-token multimodal corpus intended to strengthen reasoning across code, images, interfaces, documents, spreadsheets, presentations, and other business artifacts. In software development scenarios, the model can visually inspect rendered applications, evaluate its own output, and iteratively correct layout, functionality, or interaction issues. Z.ai’s reported benchmark results show large improvements over GLM-5.2 in areas such as software engineering and automation, while placing GLM-5.3-Flash close to leading frontier systems on several coding and agentic evaluations. Before its formal release, the model was anonymously tested under the name ox-alpha on OpenCode and OpenRouter, where Z.ai says it became one of the most widely used models during its testing period. GLM-5.3-Flash is available through Z.ai’s API and coding products as well as through downloadable weights on Hugging Face, with deployment support for SGLang, vLLM, and TokenSpeed.

Amazon Nova 2 Pro is engineered for organizations that need frontier-grade intelligence to handle sophisticated reasoning tasks that traditional models struggle to solve. It processes text, images, video, and speech in a unified system, enabling deep multimodal comprehension and advanced analytical workflows. Nova 2 Pro shines in challenging environments such as enterprise planning, technical architecture, agentic coding, threat detection, and expert-level problem solving. Its benchmark results show competitive or superior performance against leading AI models across a broad range of intelligence evaluations, validating its capability for the most demanding use cases. With native web grounding and live code execution, the model can pull real-time information, validate outputs, and build solutions that remain aligned with current facts. It also functions as a master model for distillation, allowing teams to produce smaller, faster versions optimized for domain-specific tasks while retaining high intelligence. Its multimodal reasoning capabilities enable analysis of hours-long videos, complex diagrams, transcripts, and multi-source documents in a single workflow. Nova 2 Pro integrates seamlessly with the Nova ecosystem and can be extended using Nova Forge for organizations that want to build their own custom variants. Companies across industries—from cybersecurity to scientific research—are adopting Nova 2 Pro to enhance automation, accelerate innovation, and improve decision-making accuracy. With exceptional reasoning depth and industry-leading versatility, Nova 2 Pro stands as the most capable solution for organizations advancing toward next-generation AI systems.

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.

Claude Opus 4, the most advanced model in the Claude family, is built to handle the most complex software engineering tasks with ease. It outperforms all previous models, including Sonnet, with exceptional benchmarks in coding precision, debugging, and complex multi-step workflows. Opus 4 is tailored for developers and teams who need a high-performance AI that can tackle challenges over extended periods—perfect for real-time collaboration and long-duration tasks. Its efficiency in multi-agent workflows and problem-solving makes it ideal for companies looking to integrate AI into their development process for sustained impact. Available via the Anthropic API, Amazon Bedrock, and Gemini Enterprise Agent Platform, Opus 4 offers a robust tool for teams working on cutting-edge software development and research.

Amazon Nova Pro is a robust AI model that supports text, image, and video inputs, providing optimal speed and accuracy for a variety of business applications. Whether you’re looking to automate Q&A, create instructional agents, or handle complex video content, Nova Pro delivers cutting-edge results. It is highly efficient in performing multi-step workflows and excels at software development tasks and mathematical reasoning, all while maintaining industry-leading cost-effectiveness and responsiveness. With its versatility, Nova Pro is ideal for businesses looking to implement powerful AI-driven solutions across multiple domains.

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.

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 Sonnet 4 is a breakthrough AI model, refining the strengths of Claude Sonnet 3.7 and delivering impressive results across software engineering tasks, coding, and advanced reasoning. With a robust 72.7% on SWE-bench, Sonnet 4 demonstrates remarkable improvements in handling complex tasks, clearer reasoning, and more effective code optimization. The model’s ability to execute complex instructions with higher accuracy and navigate intricate codebases with fewer errors makes it indispensable for developers. Whether for app development or addressing sophisticated software engineering challenges, Sonnet 4 balances performance and efficiency, offering an optimal solution for enterprises and individual developers seeking high-quality AI assistance.

Gemini 3 Pro represents a major leap forward in AI reasoning and multimodal intelligence, redefining how developers and organizations build intelligent systems. Trained for deep reasoning, contextual memory, and adaptive planning, it excels at both agentic code generation and complex multimodal understanding across text, image, and video inputs. The model’s 1-million-token context window enables it to maintain coherence across extensive codebases, documents, and datasets—ideal for large-scale enterprise or research projects. In agentic coding, Gemini 3 Pro autonomously handles multi-file development workflows, from architecture design and debugging to feature rollouts, using natural language instructions. It’s tightly integrated with Google’s Antigravity platform, where teams collaborate with intelligent agents capable of managing terminal commands, browser tasks, and IDE operations in parallel. Gemini 3 Pro is also the global leader in visual, spatial, and video reasoning, outperforming all other models in benchmarks like Terminal-Bench 2.0, WebDev Arena, and MMMU-Pro. Its vibe coding mode empowers creators to transform sketches, voice notes, or abstract prompts into full-stack applications with rich visuals and interactivity. For robotics and XR, its advanced spatial reasoning supports tasks such as path prediction, screen understanding, and object manipulation. Developers can integrate Gemini 3 Pro via the Gemini API, Google AI Studio, or Gemini Enterprise Agent Platform, configuring latency, context depth, and visual fidelity for precision control. By merging reasoning, perception, and creativity, Gemini 3 Pro sets a new standard for AI-assisted development and multimodal intelligence.

Gemini 2.5 Flash is an AI model designed to enhance the performance of real-time applications that demand low latency and high efficiency. Whether it's for virtual assistants, real-time summarization, or customer service, Gemini 2.5 Flash delivers fast, accurate results while keeping costs manageable. The model includes dynamic reasoning, where businesses can adjust the processing time to suit the complexity of each query. This flexibility ensures that enterprises can balance speed, accuracy, and cost, making it the perfect solution for scalable, high-volume AI applications.

Gemma 4 is a modern AI model introduced by Google and built on the Gemini architecture to provide enhanced performance and flexibility for developers and researchers. The model is designed to run efficiently on a single GPU or TPU, which makes powerful AI capabilities more accessible without requiring large-scale infrastructure. Gemma 4 focuses heavily on improving natural language understanding and text generation, enabling it to support a wide range of AI-powered applications. These capabilities allow developers to build systems such as conversational assistants, intelligent search tools, and automated content generation platforms. The architecture behind Gemma 4 enables the model to process language with greater accuracy while maintaining efficient computational requirements. This balance between performance and efficiency allows developers to experiment with advanced AI features without the need for extremely large computing environments. Gemma 4 is designed to be scalable so it can support both small development projects and larger enterprise applications. Researchers can also use the model to explore new approaches to machine learning and language processing. The model’s ability to run on widely available hardware makes it practical for organizations that want to integrate AI into their workflows. By combining strong language capabilities with efficient deployment requirements, Gemma 4 helps broaden access to advanced AI technology. Its design reflects a growing focus on creating models that are both powerful and practical for real-world use. As a result, Gemma 4 supports the continued expansion of AI applications across industries and research fields.

Meet Gemma 3n, our state-of-the-art open multimodal model engineered for exceptional performance and efficiency on devices. Emphasizing responsive and low-footprint local inference, Gemma 3n sets the stage for a new era of intelligent applications that can be deployed while on the go. It possesses the ability to interpret and react to a combination of images and text, with upcoming plans to add video and audio capabilities shortly. This allows developers to build smart, interactive functionalities that uphold user privacy and operate smoothly without relying on an internet connection. The model features a mobile-centric design that significantly reduces memory consumption. Jointly developed by Google's mobile hardware teams and industry specialists, it maintains a 4B active memory footprint while providing the option to create submodels for enhanced quality and reduced latency. Furthermore, Gemma 3n is our first open model constructed on this groundbreaking shared architecture, allowing developers to begin experimenting with this sophisticated technology today in its initial preview. As the landscape of technology continues to evolve, we foresee an array of innovative applications emerging from this powerful framework, further expanding its potential in various domains. The future looks promising as more features and enhancements are anticipated to enrich the user experience.

Devstral 2 is an innovative, open-source AI model tailored for software engineering, transcending simple code suggestions to fully understand and manipulate entire codebases; this advanced functionality enables it to execute tasks such as multi-file edits, bug fixes, refactoring, managing dependencies, and generating code that is aware of its context. The suite includes a powerful 123-billion-parameter model alongside a streamlined 24-billion-parameter variant called “Devstral Small 2,” offering flexibility for teams; the larger model excels in handling intricate coding tasks that necessitate a deep contextual understanding, whereas the smaller model is optimized for use on less robust hardware. With a remarkable context window capable of processing up to 256 K tokens, Devstral 2 is adept at analyzing extensive repositories, tracking project histories, and maintaining a comprehensive understanding of large files, which is especially advantageous for addressing the challenges of real-world software projects. Additionally, the command-line interface (CLI) further enhances the model’s functionality by monitoring project metadata, Git statuses, and directory structures, thereby enriching the AI’s context and making “vibe-coding” even more impactful. This powerful blend of features solidifies Devstral 2's role as a revolutionary tool within the software development ecosystem, offering unprecedented support for engineers. As the landscape of software engineering continues to evolve, tools like Devstral 2 promise to redefine the way developers approach coding tasks.

GigaChat 3 Ultra is a breakthrough open-source LLM, offering 702 billion parameters built on an advanced MoE architecture that keeps computation efficient while delivering frontier-level performance. Its design activates only 36 billion parameters per step, combining high intelligence with practical deployment speeds, even for research and enterprise workloads. The model is trained entirely from scratch on a 14-trillion-token dataset spanning ten+ languages, expansive natural corpora, technical literature, competitive programming problems, academic datasets, and more than 5.5 trillion synthetic tokens engineered to enhance reasoning depth. This approach enables the model to achieve exceptional Russian-language capabilities, strong multilingual performance, and competitive global benchmark scores across math (GSM8K, MATH-500), programming (HumanEval+), and domain-specific evaluations. GigaChat 3 Ultra is optimized for compatibility with modern open-source tooling, enabling fine-tuning, inference, and integration using standard frameworks without complex custom builds. Advanced engineering techniques—including MTP, MLA, expert balancing, and large-scale distributed training—ensure stable learning at enormous scale while preserving fast inference. Beyond raw intelligence, the model includes upgraded alignment, improved conversational behavior, and a refined chat template using TypeScript-based function definitions for cleaner, more efficient interactions. It also features a built-in code interpreter, enhanced search subsystem with query reformulation, long-term user memory capabilities, and improved Russian-language stylistic accuracy down to punctuation and orthography. With leading performance on Russian benchmarks and strong showings across international tests, GigaChat 3 Ultra stands among the top five largest and most advanced open-source LLMs in the world. It represents a major engineering milestone for the open community.

GPT-4.1 is a cutting-edge AI model from OpenAI, offering major advancements in performance, especially for tasks requiring complex reasoning and large context comprehension. With the ability to process up to 1 million tokens, GPT-4.1 delivers more accurate and reliable results for tasks like software coding, multi-document analysis, and real-time problem-solving. Compared to its predecessors, GPT-4.1 excels in instruction following and coding tasks, offering higher efficiency and improved performance at a reduced cost.

Devstral Small 2 is a condensed, 24 billion-parameter variant of Mistral AI's groundbreaking coding-focused models, made available under the adaptable Apache 2.0 license to support both local use and API access. Alongside its more extensive sibling, Devstral 2, it offers "agentic coding" capabilities tailored for low-computational environments, featuring a substantial 256K-token context window that enables it to understand and alter entire codebases with ease. With a performance score nearing 68.0% on the widely recognized SWE-Bench Verified code-generation benchmark, Devstral Small 2 distinguishes itself within the realm of open-weight models that are much larger. Its compact structure and efficient design allow it to function effectively on a single GPU or even in CPU-only setups, making it an excellent option for developers, small teams, or hobbyists who may lack access to extensive data-center facilities. Moreover, despite being smaller, Devstral Small 2 retains critical functionalities found in its larger counterparts, such as the capability to reason through multiple files and adeptly manage dependencies, ensuring that users enjoy substantial coding support. This combination of efficiency and high performance positions it as an indispensable asset for the coding community. Additionally, its user-friendly approach ensures that both novice and experienced programmers can leverage its capabilities without significant barriers.

GPT-5 Pro is OpenAI’s flagship AI model built to deliver exceptional reasoning power and precision for the most complex and nuanced problems across numerous domains. Utilizing advanced parallel computing techniques, it extends the GPT-5 architecture to think longer and more deeply, resulting in highly accurate and comprehensive responses on challenging tasks such as advanced science, health diagnostics, coding, and mathematics. This model consistently outperforms its predecessors on rigorous benchmarks like GPQA and expert evaluations, reducing major errors by 22% and gaining preference from external experts nearly 68% of the time over GPT-5 thinking. GPT-5 Pro is designed to adapt dynamically, determining when to engage extended reasoning for queries that benefit from it while balancing speed and depth. Beyond its technical prowess, it incorporates enhanced safety features, lowering hallucination rates and providing transparent communication when limits are reached or tasks cannot be completed. The model supports Pro users with unlimited access and integrates seamlessly into ChatGPT’s ecosystem, including Codex CLI for coding applications. GPT-5 Pro also benefits from improvements in reducing excessive agreeableness and sycophancy, making interactions feel natural and thoughtful. With extensive red-teaming and rigorous safety protocols, it is prepared to handle sensitive and high-stakes use cases responsibly. This model is ideal for researchers, developers, and professionals seeking the most reliable, insightful, and powerful AI assistant. GPT-5 Pro marks a major step forward in AI’s ability to augment human intelligence across complex real-world challenges.

OpenAI’s GPT-5 is the latest flagship AI language model, delivering unprecedented intelligence, speed, and versatility for a broad spectrum of tasks including coding, scientific inquiry, legal research, and financial analysis. It is engineered with built-in reasoning capabilities, allowing it to provide thoughtful, accurate, and context-aware responses that rival expert human knowledge. GPT-5 supports very large context windows—up to 400,000 tokens—and can generate outputs of up to 128,000 tokens, enabling complex, multi-step problem solving and long-form content creation. A novel ‘verbosity’ parameter lets users customize the length and depth of responses, while enhanced personality and steerability features improve user experience and interaction. The model integrates natively with enterprise software and cloud storage services such as Google Drive and SharePoint, leveraging company-specific data to deliver tailored insights securely and in compliance with privacy standards. GPT-5 also excels in agentic tasks, making it ideal for developers building advanced AI applications that require autonomy and multi-step decision-making. Available across ChatGPT, API, and developer tools, it transforms workflows by enabling employees to achieve expert-level results without switching between different models. Businesses can trust GPT-5 for critical work, benefiting from its safety improvements, increased accuracy, and deeper understanding. OpenAI continues to support a broad ecosystem, including specialized versions like GPT-5 mini and nano, to meet varied performance and cost needs. Overall, GPT-5 sets a new standard for AI-powered intelligence, collaboration, and productivity.