Alternatives to Grok 4.8

GPT-6 Astra is an advanced general-purpose AI model from OpenAI designed for demanding agentic, technical, scientific, and professional workflows. The model combines reasoning with computer-use capabilities that let it interact with applications, websites, development environments, productivity software, and specialized tools. It can perform activities such as online research, CRM updates, form completion, calendar organization, data analysis, software testing, troubleshooting, and frontend quality assurance. Astra is also trained for professional artifact creation, including documents, presentations, spreadsheets, analyses, websites, applications, and visual designs that follow provided templates and business conventions. Its software engineering capabilities support complex coding, codebase understanding, debugging, database migration, verification, and extended autonomous development tasks. OpenAI has also introduced a Codex context system for Astra that can maintain notes across context windows and search earlier conversation and tool history instead of relying exclusively on repeated summarization. Scientific capabilities span areas such as mathematics, biology, chemistry, health, data analysis, and the use of specialized research software. The model includes strengthened alignment and cybersecurity safeguards intended to help it remain within authorized task boundaries while supporting legitimate activities such as secure code review and vulnerability remediation. GPT-6 Astra is available across supported ChatGPT plans and developer platforms, including the OpenAI API and Amazon Web Services infrastructure.

Gemini 4 Argon is a frontier AI model from Google DeepMind built to sustain deep reasoning across complex, long-running professional workflows. Google designed the model for demanding work spanning software engineering, finance, legal tasks, enterprise knowledge work, cybersecurity defense, and creative writing. Argon supports coding, reasoning, multimodality, and multi-step task execution, allowing it to work across workflows that require information gathering, analysis, tool use, and extended problem solving. Its output token limit has been increased from 64,000 to 1 million tokens, giving the model additional capacity for lengthy reasoning and generation within a single trajectory. On DeepSWE v1.1, Google reports a score of 77.9% for real-world long-horizon software engineering, while its AutomationBench score of 51.3% measures performance on end-to-end business workflows. Google also reports strong results on evaluations covering finance, legal work, visual analysis, and long-video understanding, including a 91.7% score on LVBench. For cybersecurity teams, Argon can autonomously discover, validate, and patch software vulnerabilities and achieved a reported 68% score on CWE-bench v1. Google is initially providing the model to selected cyber defenders through its Fairwind Program while strengthening safeguards before expanding access to developers, enterprises, and consumers. Argon is planned to launch at an introductory price of $2 per million input tokens and $10 per million output tokens, with cached input tokens receiving a 95% discount from the standard input price.

Claude Mythos 5.1 represents Anthropic's latest advancement in the Mythos-class of models, tailored for sophisticated applications in cybersecurity, biology, scientific investigation, programming, and extensive knowledge-oriented tasks. While it shares the same foundational architecture as Claude Fable 5.1, it is differentiated by unique safety measures: Fable 5.1 is widely accessible, whereas Mythos 5.1 is limited to select trusted access programs designed with specific safeguards for cybersecurity and life sciences research. This model establishes a new benchmark in performance for autonomous coding and showcases unparalleled cyber capabilities among all Anthropic models released thus far. In the realm of scientific research, Mythos 5.1 can effectively handle specialized tools and intricate workflows related to molecular design, computational biology, and various technical fields. During testing by Anthropic, it successfully engineered high-affinity protein binders for multiple targets, achieving its highest recorded hit rate to date. Additionally, it demonstrated proficiency in optimizing seven different open-source deep learning models focused on protein and genomics. By pushing the boundaries of what is possible, Mythos 5.1 is positioned to make significant contributions to future research and development endeavors.

Claude Fable 5.1 is a frontier AI model from Anthropic built for demanding coding, research, knowledge work, and autonomous multi-step workflows. It delivers higher performance than Claude Fable 5 across benchmarks covering scientific research, terminal-based coding, business automation, computer use, multidisciplinary reasoning, and agentic software development. The model is particularly suited to long-running tasks where it must investigate problems, use tools, maintain context, verify intermediate work, and continue operating with limited supervision. Early evaluations highlighted improvements in areas such as root-cause analysis, code review, browser automation, financial research, document drafting, slide creation, and complex engineering workflows. Fable 5.1 also introduces lower cache-read pricing, which Anthropic says can reduce costs by roughly 25% for typical workloads and considerably more for context-heavy agentic tasks. Enterprise customers can use new privacy-oriented safeguard options that are designed to support zero-data-retention-style deployments while still maintaining misuse protections. Cybersecurity safeguards have also been refined to intervene less often on legitimate defensive work while continuing to restrict higher-risk activities such as exploit generation. Anthropic offers the model through Claude Code, Claude Cowork, Claude.ai, the Claude API, and supported cloud platforms. Claude Fable 5.1 is intended for developers, researchers, enterprises, and professional teams that need strong reasoning and coding performance without relying on Anthropic’s most restricted-access model tier.

GPT-6 Sol is a frontier AI model from OpenAI built for professional knowledge work, software development, business automation, computer use, and agentic applications. It brings many of the advances introduced with GPT-6 Astra to a model optimized for greater cost efficiency and higher-volume use. GPT-6 Sol can apply configurable reasoning effort to complex tasks, giving developers and users control over how much computation is used for different workloads. Its coding capabilities support long-horizon software engineering, codebase modification, debugging, testing, and agent-driven development in tools such as Codex. For professional workflows, the model can work across applications and tools to complete multi-step tasks in areas such as sales, marketing, operations, finance, support, and HR. OpenAI also reports improved factuality compared with GPT-5.6 Sol, reducing errors on its internal evaluation of difficult real-world conversations. Computer-use capabilities allow Sol-powered agents to navigate software interfaces and perform extended workflows that require multiple actions and decisions. Enhanced prompt caching provides higher cache-hit rates and discounts on reused input context, making the model better suited to persistent agents and long conversations. GPT-6 Sol is available to developers through the OpenAI API as gpt-6-sol and is also offered in ChatGPT Work and Codex.

GPT-6 Luna is a lightweight, cost-efficient model in OpenAI’s GPT-6 family built for coding, professional tasks, computer use, and high-volume AI applications. The model incorporates advances from the same generation as GPT-6 Astra while emphasizing lower inference costs and greater efficiency for everyday workloads. API pricing is $0.10 per million input tokens and $0.50 per million output tokens, making Luna suitable for applications that process large volumes of requests. In professional work, GPT-6 Luna can execute multi-step workflows involving business applications, tools, and structured tasks across functions such as sales, marketing, operations, support, finance, and HR. For software engineering, the model can work on real codebases, perform extended development tasks, and operate within coding agents such as Codex. Its computer-use capabilities allow AI agents to interact with software interfaces and carry out longer workflows that require repeated actions and decisions. OpenAI also reports substantial factuality improvements over GPT-5.6 Luna, with higher reasoning settings enabling stronger performance on difficult factual questions. GPT-6 prompt caching provides higher cache-hit rates and discounted cached input, helping persistent agents and long conversations reuse context more efficiently. GPT-6 Luna is available through ChatGPT Work, Codex, the OpenAI API, and the ChatGPT desktop app for eligible users.

Grok 4.6 is an advanced xAI model built for long-running agents, coding, knowledge work, interactive applications, and visual project creation. It improves on Grok 4.5 with a focus on staying with complex tasks across many steps, whether the user is researching a topic, analyzing information, working across a codebase, or building a polished application. The model was trained through a longer supplemental run that used curated model-generated reasoning data, advanced technical concepts, engineering data, and an improved training recipe. Grok 4.6 was also trained with SFT and RL across domains such as STEM, software engineering, knowledge work, kernel optimization, web development, computer-aided design, and agentic coding. It is designed to turn ambitious ideas into working projects by researching unfamiliar domains, defining application structure, building core interactions, and iterating through feedback. The model shows stronger first passes on visual and interactive projects, helping users establish structure and visual language more quickly. Grok 4.6 also demonstrates more self-testing and verification during longer trajectories. It is available through Cursor, Grok Build, the xAI API, OpenRouter, Vercel, Cloudflare, and other partners, with pricing starting at $2 per million input tokens and $6 per million output tokens. By combining frontier reasoning, agentic coding, long-running task execution, visual project generation, API access, and broad developer availability, Grok 4.6 helps builders move from idea to working software faster.

Grok 4.7 is SpaceXAI’s advanced AI model for software development, professional knowledge work, and multi-step agentic tasks. It is built on a larger base model than Grok 4.6 and uses a longer reinforcement learning training run weighted toward difficult problems that require extended execution time. The model is better at checking its own work, maintaining longer context, and completing complex workflows that span many steps. Native support for the Grok Bot harness improves its conversational abilities and makes it more capable across general knowledge and interactive work. Grok 4.7 is positioned for use in coding, terminal automation, legal workflows, document and presentation creation, electrical engineering, healthcare reasoning, and other professional tasks. SpaceXAI also reports improvements across software engineering benchmarks including CursorBench and DeepSWE as well as professional-work evaluations such as AA Briefcase. The model includes a redesigned safety system intended to improve refusal behavior, jailbreak resistance, and handling of potentially dangerous cybersecurity and biological requests. Developers can access Grok 4.7 through Grok Build, Cursor, the Grok API, third-party coding tools, cloud providers, and model-routing platforms. Grok 4.7 is priced starting at $2 per million input tokens and $6 per million output tokens, with a faster variant available at a higher price.

Muse Spark Open is an open-weight foundation model developed by Meta to support multimodal reasoning, autonomous agents, and complex software development tasks. The model features 816 billion total parameters with approximately 42 billion active parameters and a context window supporting up to one million tokens. Its architecture is designed to process multimodal information while maintaining context across lengthy workflows, extensive codebases, and multistep assignments. Muse Spark Open provides capabilities for software engineering, code generation, debugging, application development, and automated testing. Its agentic functionality supports external tool interactions, task planning, computer-based workflows, and execution of complex objectives over extended sessions. The model can run locally using two Apple M5 Ultra Mac Studio systems with the MLX framework, enabling organizations to process information without relying on hosted inference services. Developers can deploy the model through SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, llama.cpp, and other supported inference engines. Customization is supported through PyTorch and TorchTitan, allowing organizations to adapt the model to specialized applications and workflows. Muse Spark Open is distributed under the Muse Model License and is available through Hugging Face, major cloud model repositories, and hosted API providers including Together AI, Fireworks AI, and OpenRouter.

Kimi K3.1 Pro is an advanced multimodal AI reasoning model developed by Moonshot AI for software engineering, autonomous agents, technical design, and analytical knowledge work. Built on the Kimi K3 architecture, the model uses approximately 2.8 trillion total parameters with 104 billion active parameters and supports a one-million-token context window. Its enhanced reasoning capabilities enable complex, multistep problem-solving across programming, scientific analysis, financial research, and business operations. Coding features include source code generation, debugging, repository-level modifications, automated testing, and end-to-end application development. The model can create interactive websites, Three.js environments, games, and software projects using development frameworks and engines such as Unity, Unreal Engine, and Godot. Its spatial reasoning capabilities support CAD code generation, parametric modeling, 3D asset creation, and geometry adjustments through tools such as Blender. Financial analysis capabilities include spreadsheet modeling, document-based research, numerical reasoning, and investment analysis using information from multiple sources. Kimi K3.1 Pro supports text and image inputs, external tool use, and long-running agentic workflows that require planning and iterative execution. The model is accessible through Kimi applications and APIs, with an additional Highspeed version designed to deliver approximately two to three times faster responses.

NVIDIA Nemotron 3.5 Super VL is a multimodal foundation model developed for AI agents that require reasoning, visual understanding, coding, and multistep task execution. Its 120-billion-parameter mixture-of-experts architecture activates approximately 12 billion parameters per token, enabling efficient inference while maintaining substantial model capacity. The model processes text, images, documents, and video within a unified system that combines multimodal interpretation with instruction following and tool use. Its document intelligence capabilities include extracting information from tables, analyzing charts, interpreting page layouts, and processing multipage documents. Video understanding features support visual information retrieval, content review, incident analysis, and reasoning across recorded sequences. The model also supports software engineering tasks such as code analysis, error identification, debugging, testing, and recommending fixes. Developers can customize Nemotron 3.5 Super VL using NVIDIA NeMo for supervised fine-tuning, low-rank adaptation, and reinforcement learning with domain-specific data. Its inference optimizations include speculative decoding, context reuse, multi-token prediction, and NVFP4 quantization to improve throughput and reduce computational requirements. The model is available with open weights and supports deployment across compatible local hardware, enterprise data centers, and cloud infrastructure.

NVIDIA Nemotron 3.5 Bolt is a series of efficient AI models developed by NVIDIA for running autonomous agents and conversational applications directly on local hardware. The family consists of Bolt 74B-A9B 24GB and Bolt E4B 4GB, which address different performance and memory requirements. Bolt 74B-A9B uses a mixture-of-experts architecture with 74 billion total parameters and approximately 9 billion active parameters to balance reasoning capabilities with computational efficiency. Native 2-bit quantization enables the model to operate within a 24GB memory footprint on supported NVIDIA systems. Its agentic capabilities include web research, code generation, software debugging, information retrieval, and multistep task execution. The model can be integrated with local AI agent frameworks and development tools to complete assignments without sending application workloads to external inference services. Bolt E4B is optimized for fast conversational responses, multistep interactions, and embedded AI experiences such as virtual assistants and interactive game characters. NVIDIA supports local deployment of the Bolt family across compatible GeForce RTX, RTX PRO, DGX Spark, and Jetson hardware, depending on the model variant. Bolt 74B-A9B is available with open weights through Hugging Face and supports inference tools including llama.cpp, Ollama, LM Studio, Unsloth, and vLLM.

GPT-6.1 Sol is an upgraded OpenAI model that combines advanced intelligence with lower operating costs for coding, professional knowledge work, computer use, scientific research, and autonomous agents. OpenAI positions it as offering near-GPT-6 Astra intelligence at one-fifth of Astra's standard input and output token prices. The model delivers substantial improvements over GPT-6 Sol in software engineering, complex document understanding, business automation, and long-horizon computer-use workflows. On DeepSWE v1.1, GPT-6.1 Sol matches GPT-6 Astra at approximately one-fifth of the cost and surpasses GPT-6 Sol's highest score by 6.4 percentage points at lower reasoning effort. On AutomationBench, it scores 4.8 percentage points higher than GPT-6 Sol at the same reasoning setting and 2.2 points above Opus 5.5 at medium reasoning effort. GPT-6.1 Sol also improves computer use, coming within 2.1 percentage points of GPT-6 Astra on the OSWorld 2.0 offline set at maximum reasoning effort while costing roughly one-seventh as much per task. For scientific workflows, the model more than doubles GPT-6 Sol's Terminal-Bench Science 0.1 score at maximum effort while reducing average task cost by more than half. Factuality has also improved, with the share of responses containing a factual error at low reasoning effort falling from 11.4% with GPT-6 Sol to 7.7% with GPT-6.1 Sol on OpenAI's difficult error-focused evaluation. Developers can access GPT-6.1 Sol through the OpenAI API for $2 per million input tokens, $0.10 per million cached input tokens, and $10 per million output tokens, while eligible users can access it through ChatGPT Work and Codex.

Muse Wonder is an advanced AI foundation model developed by Meta to support personal AI agents, professional knowledge work, and software engineering tasks. The model is part of the Muse Wonder series, which includes Muse Wonder and the larger Muse Wonder Full variant. Its Active Memory capability maintains important contextual information across conversations and retrieves relevant details when they become useful for subsequent tasks. Active Focus enables the model to pursue multistep objectives, evaluate alternative approaches, respond to obstacles, and continue working toward completion. Muse Wonder supports applications involving legal document analysis, financial information, health-related questions, and business workflow automation. Its coding capabilities include generating applications, modifying existing codebases, tracking project requirements, and iterating through testing and debugging cycles. The model can operate as part of agentic systems that interact with browsers, external applications, and connected tools to complete user-directed activities. Meta has evaluated Muse Wonder on benchmarks covering personal assistance, professional agent tasks, long-term memory, sustained execution, and software development. Muse Wonder is accessible through the Meta Model API and Muse Code, with plans to incorporate its capabilities into the Muse assistant and additional AI-powered experiences.

MiMo-V2.6-Flash is Xiaomi MiMo’s efficiency-focused open-source omnimodal model for coding, automation, visual work, and agentic applications. It is designed to provide a balance between model capability, inference cost, and practical performance across a broad range of workloads. The model can perform software engineering tasks, use tools, execute multi-step workflows, and interact with computer environments. Its multimodal capabilities support applications such as frontend development, presentation design, 3D content creation, game development, and visual reasoning. MiMo-V2.6 can also use multi-view visual inputs in embodied simulation environments to reason about scenes and guide actions through feedback loops. Xiaomi trained the Flash model using reinforcement learning over roughly 750,000 trajectories spanning coding, general agents, visual tasks, and cybersecurity environments. During that training process, Xiaomi reports substantial gains in long-horizon software engineering and general workflow performance compared with the model’s earlier checkpoints. The company has open-sourced the broader MiMo-V2.6 release along with its technical report, reinforcement learning environments, and RL code to support research and reproducibility. MiMo-V2.6-Flash can be accessed through MiMo Desktop, AI Studio, MiMo Code, the MiMo API Platform, OpenRouter, and Xiaomi MiMo’s open-source distribution channels.

MiMo-V2.6-Pro is an open-source, natively omnimodal AI model from Xiaomi MiMo designed for coding, agentic workflows, visual creation, research, and computer-based tasks. It is the highest-capability model in the MiMo-V2.6 series and was trained using a large-scale reinforcement learning process focused on verifiable, complex tasks. The model can handle software engineering, automation, tool use, visual reasoning, and long-running agent workflows across multiple environments. Its multimodal capabilities extend beyond traditional coding into 3D scene generation, Blender modeling, embodied simulation, frontend development, presentation design, video creation, and music composition. MiMo-V2.6-Pro can take text, images, video, and other visual inputs and coordinate multi-agent workflows to produce and iteratively refine complex outputs. Research applications demonstrated by Xiaomi include materials discovery, literature analysis, computational simulation, hypothesis generation, and formalizing mathematical proofs in Lean. Xiaomi has released the model alongside its technical report, reinforcement learning environments, and RL code so researchers can study and reproduce the training approach. MiMo-V2.6-Pro is also available in an UltraSpeed configuration that provides substantially faster output for latency-sensitive workflows. Users can access the model through MiMo Desktop, AI Studio, MiMo Code, the MiMo API Platform, OpenRouter, and Hugging Face.

Grok 4.5 is SpaceXAI’s smartest model, designed to excel at coding, agentic workflows, engineering tasks, and knowledge work. The model was trained on large-scale datasets covering coding, science, engineering, and math, with additional reinforcement learning focused on multi-step software engineering. It is built to perform well on real engineering workflows, including debugging, terminal-based tasks, complex code generation, Rust and C/C++ development, and app building from minimal prompts. Grok 4.5 is served at fast-model speeds while using fewer output tokens on comparable coding tasks, helping teams complete technical work more quickly and cost-effectively. The model is also available in Grok Build, where it can help create Excel models, PowerPoint presentations, Word documents, diagrams, business review decks, and research-supported productivity assets. Developers can access Grok 4.5 through the SpaceXAI API, Cursor, and Grok Build, with simple API key setup and support for direct integration into coding and automation workflows. Its pricing is positioned for high-intelligence work at scale, with per-million-token rates for both input and output usage. Grok 4.5 is also trained for agentic execution, allowing it to handle longer technical rollouts and multi-step problem solving more effectively. For developers, engineering teams, and knowledge workers, Grok 4.5 provides a powerful AI model for software creation, office automation, technical reasoning, and production-grade agent workflows.

SWE-2 is a software engineering model from Cognition built for agentic coding tasks that require strong performance at lower computational and monetary cost. It is post-trained from the Kimi K3 base model and extends Cognition’s earlier SWE-1.7 training approach with a new reinforcement learning method for jointly optimizing multiple reasoning-effort settings. Medium, high, and maximum effort modes provide different tradeoffs between speed, cost, exploration, and verification depending on task complexity. The model is trained to inspect only the parts of a codebase that are likely to matter, helping it reach implementation faster and reduce unnecessary exploration. SWE-2 can generate and modify code, run tests, analyze repositories, work through terminal tasks, and verify whether implementations satisfy user requirements. Cognition also reports improvements in end-to-end test creation, regression detection, instruction following, and re-deriving conclusions when challenged. Its training process incorporates cost-aware rewards, length-weighted reward baselines, expanded reinforcement learning environments, and hardened verifiers intended to improve both efficiency and reliability. SWE-2 is positioned as a cost-efficient alternative to larger frontier coding models while remaining competitive on software engineering benchmarks such as FrontierCode, DeepSWE, and Terminal-Bench. The model is available in Devin Desktop and Devin CLI and is being introduced to additional Cognition products including Devin Web and Fusion.

Claude Sonnet 5 is Anthropic's newest Sonnet-class language model, built to provide advanced reasoning, coding, autonomous tool use, and agentic workflow capabilities at a lower cost than larger foundation models. The model is capable of planning multi-step tasks, interacting with browsers and terminals, using external tools, and completing sophisticated work with minimal human intervention. Compared to Claude Sonnet 4.6, Sonnet 5 delivers substantial improvements across coding, reasoning, knowledge work, and AI agent performance while narrowing the capability gap with Anthropic's Opus family of models. Anthropic also reports improvements in safety, including lower rates of hallucinations, reduced undesirable behaviors, stronger resistance to prompt injection attacks, and better handling of malicious requests. Developers can access Sonnet 5 through the Claude platform and API using competitive introductory pricing, making it easier to deploy production AI applications without significantly increasing costs. The model supports a wide range of agentic workflows by allowing users to adjust effort levels to balance performance, speed, and token usage for different tasks. Anthropic also expanded usage limits across its services to support more demanding workloads generated by increasingly capable AI agents. Claude Sonnet 5 is positioned as a practical model for organizations that need powerful AI automation without the higher operating costs associated with frontier-scale models. By combining improved intelligence, stronger safety, flexible pricing, and enhanced agentic behavior, Claude Sonnet 5 enables developers to build more autonomous and reliable AI systems.

Claude Fable 5 is Anthropic’s most capable generally available AI model, built to tackle demanding tasks across software development, research, business analysis, scientific exploration, and enterprise productivity. The model demonstrates state-of-the-art performance in coding, reasoning, visual understanding, long-context processing, and autonomous task execution. Claude Fable 5 can analyze large codebases, interpret complex documents and datasets, generate detailed reports, and assist with advanced decision-making processes. Its enhanced memory capabilities allow it to remain effective during long-running workflows and multi-step projects. The model also delivers strong performance in image analysis, chart interpretation, scientific reasoning, and technical problem-solving. Anthropic has incorporated advanced safety classifiers that detect certain high-risk topics and automatically redirect those interactions to a more restricted model experience. These safeguards are designed to reduce misuse while still providing productive assistance for legitimate users. Claude Fable 5 is available through the Claude platform and API, enabling developers and organizations to integrate advanced AI capabilities into their applications and workflows. The platform is designed to help businesses improve productivity, accelerate innovation, and streamline complex knowledge work.

Seed2.1 represents a groundbreaking advancement in productivity tools, featuring two distinct AI models, Pro and Turbo, tailored for varying levels of user needs. Designed to address intricate challenges encountered in everyday tasks, workplace responsibilities, and innovative ventures, this agent significantly enhances capabilities in areas such as general assistance, code development, multimodal comprehension, knowledge application, and reasoning processes. For demanding office tasks and intricate daily consultations, Seed2.1 adeptly manages a range of multi-step processes, including project management, document handling, tool utilization, data analysis, solution formulation, content organization, and synthesis of outcomes. In the realm of software development, Seed2.1 optimizes end-to-end processes within enterprise-level workflows, covering aspects like requirement gathering, software architecture, feature development, debugging, environment configuration, and quality assurance. Additionally, the model is proficient in comprehending entire codebases, effectively coordinating updates across numerous files, and ensuring the delivery of sustainable, production-ready software engineering solutions. Ultimately, Seed2.1 not only enhances productivity but also empowers users to tackle complex challenges with confidence.

Claude Haiku 5.5 is an Anthropic AI model optimized for high-volume applications where latency, efficiency, and reasoning capability are important. Anthropic positions the model for workloads including classification, routing, information extraction, and subagent tasks. Adaptive thinking is enabled by default, allowing Claude Haiku 5.5 to determine when reasoning is useful and how much reasoning to apply to a request. Developers can configure the effort parameter to adjust the balance between response quality, processing speed, and cost. The model has a 1 million token context window, compared with 200,000 tokens for Claude Haiku 4.5, enabling it to process substantially larger prompts and datasets. Its maximum output length has also increased from 64,000 to 128,000 tokens, with thinking tokens counting toward the configured output limit. Claude Haiku 5.5 includes browser use capabilities through the Claude API and Google Cloud, expanding its usefulness for AI agents and workflows involving web interaction. The model uses the newer tokenizer shared with Claude 4.7 and later models, causing identical text to consume approximately 30% more tokens than on Claude Haiku 4.5, although the exact difference varies by content. Claude Haiku 5.5 is suited to developers and organizations building scalable AI applications, routing systems, extraction pipelines, subagent architectures, and other workloads that benefit from a combination of fast responses and configurable reasoning.

Claude Opus 4.8 is Anthropic’s newest flagship AI model built to improve coding performance, reasoning accuracy, agentic task execution, and collaborative AI workflows for developers, enterprises, and advanced productivity use cases. The model serves as an upgrade to Claude Opus 4.7, delivering measurable improvements across benchmarks related to coding, practical reasoning, software engineering, and autonomous task management while maintaining the same pricing structure for standard usage. One of the most significant improvements in Claude Opus 4.8 is its enhanced honesty and judgment during complex tasks, reducing the likelihood of unsupported claims, hidden errors, or overlooked flaws in generated code and analytical outputs. Anthropic’s evaluations show that Opus 4.8 is substantially less likely than previous versions to allow software defects or reasoning mistakes to pass without flagging uncertainty or requesting clarification. The platform introduces new effort control settings that allow users to adjust how deeply the model reasons through tasks, balancing response quality, processing depth, speed, and token usage depending on workflow requirements. Claude Opus 4.8 also powers new dynamic workflow functionality in Claude Code, enabling the model to coordinate hundreds of parallel subagents within a single session to handle large-scale software engineering tasks such as codebase migrations and extensive automation projects. The model supports high-speed fast mode processing, now significantly more affordable than previous versions, while also offering higher-effort reasoning modes optimized for difficult coding and operational workflows.

Inkling is Thinking Machines’ open-weights foundation model built for customization, multimodal reasoning, and agentic AI workflows. The model uses a Mixture-of-Experts architecture with 975 billion total parameters and 41 billion active parameters, making it large in capacity while activating only a subset of experts per token. Inkling supports up to a 1 million token context window and was pretrained on 45 trillion tokens spanning text, images, audio, and video. It is designed as a broad generalist model with strengths across coding, reasoning, instruction following, factuality, tool use, vision, audio understanding, forecasting, and safety. Developers can tune its thinking effort to trade off latency, cost, and performance, which is useful for production systems that need efficient reasoning at scale. Inkling can be fine-tuned on Tinker, tested in the Inkling Playground, and deployed through partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, vLLM, SGLang, llama.cpp, and Hugging Face transformers. The model can generate applications, operate tools, create styled artifacts, reason over visual and audio inputs, and support long refinement loops for collaborative work. Thinking Machines also previewed Inkling-Small, a lighter Mixture-of-Experts model with 276 billion total parameters and 12 billion active parameters for lower-cost and lower-latency workloads. By combining open weights, multimodal training, agentic capabilities, efficient reasoning, and fine-tuning support, Inkling gives builders a flexible AI foundation for specialized products and workflows.

GLM-Legal is an AI language model developed by Z.ai to support legal analysis, document review, compliance research, and autonomous legal workflows. The model is built on GLM-5.3-Flash and incorporates specialized legal training to improve performance on professional legal tasks. Its contract analysis capabilities help users examine agreements, identify problematic provisions, and recommend revisions based on specific client objectives. GLM-Legal can analyze collections of legal documents, identify contradictions between evidence sources, and connect factual findings to relevant legal arguments. The model also supports regulatory compliance research, including analysis of privacy laws, cross-border requirements, and jurisdiction-specific obligations. Its agentic architecture enables multistep legal workflows involving document ingestion, legal reasoning, source referencing, and structured deliverable generation. Integration with Z.ai's AutoClaw platform is intended to support document editing, tracked changes, comments, and other practical legal work processes. Z.ai reports that GLM-Legal achieved a 19.2% all-pass rate on Harvey's Legal Agent Benchmark and demonstrated competitive performance on LegalBench and PLawBench. The model is intended for law firms, corporate legal departments, legal technology providers, and independent practitioners seeking AI assistance with complex legal work.

GPT-5.5 is a next-generation AI system built for execution-heavy workflows across coding, research, business analysis, and scientific tasks. It can interpret complex instructions, break them into actionable steps, and carry them through to completion while interacting with tools and systems. The model supports creating applications, generating reports, analyzing datasets, and navigating software environments seamlessly. It also integrates with workspace agents—custom AI agents that automate recurring and multi-step processes across teams. These agents can handle tasks such as lead research, reporting, and workflow automation, either on demand or on schedules. GPT-5.5 enhances productivity by reducing manual effort and enabling continuous task execution across tools. With enterprise-grade safeguards and monitoring, it ensures secure and controlled automation. It is well-suited for organizations looking to scale operations and improve efficiency through AI-driven workflows.

Mistral Large 4 serves as a versatile multimodal language model, accessible via Mistral’s hosted inference API. It integrates capabilities for instruction adherence, reasoning, and agent-like functions, allowing it to process both images and text simultaneously. This model is designed for developers to facilitate various tasks such as chat completions, structured data outputs, executing functions, answering questions from documents, and enhancing tool-assisted workflows. Its range of applications spans conversational AI, software development assistance, cybersecurity evaluations, financial and legal processes, scientific inquiry, data extraction, and the interpretation of various document types, including charts and images. Aimed at developers, researchers, and organizations within the public sector, Mistral Large 4 is tailored for creating AI-driven applications across numerous fields, including engineering, manufacturing, finance, logistics, and scientific research. Furthermore, its adaptability makes it suitable for both small startups and large enterprises looking to leverage AI technologies effectively.

MiniMax M3.1 Flash is an advanced hosted language model designed specifically for developers who are creating coding assistants, AI agents, and various software applications. It can be accessed via MiniMax's API and MiniMax Code, allowing it to process inputs in text, image, and video formats while generating relevant text responses. Among its documented applications are functionalities such as code generation, debugging, complex reasoning tasks, tool utilization, analysis of codebases and documents, as well as enhancing computer workflows. The API boasts an impressive context window that can handle up to 1 million tokens, offers adjustable reasoning capabilities, and supports request formats that are compatible with both OpenAI and Anthropic standards. Charges for usage are based on the number of tokens used for input, output, and cached input. Comprehensive documentation is provided by MiniMax to assist with API integration and effective model utilization. M3.1 Flash is accessible through the hosted API, M Plan, and MiniMax Code, while there are plans to release model weights in the future, expanding its potential applications even further. This innovation represents a significant step forward in the integration of AI into software development processes.

Grok 4.3 is an advanced AI model developed by xAI to provide enhanced reasoning, real-time insights, and automation capabilities. It builds on the Grok 4 architecture, which already includes features like real-time web browsing, multimodal processing, and tool integration. The model is designed to handle complex tasks such as coding, research, and data analysis with improved accuracy and efficiency. Grok 4.3 is integrated with live data sources, including the web and X, allowing it to deliver timely and relevant information. It operates within the SuperGrok Heavy subscription tier, which provides access to its most powerful capabilities. The model supports long-context understanding, enabling it to process large amounts of information in a single session. It also includes multi-agent or “heavy” configurations that enhance problem-solving performance. Grok 4.3 is optimized for speed and responsiveness, making it suitable for real-time applications. It can generate content, answer questions, and assist with workflows across various domains. The platform continues to evolve with new features and improvements aimed at increasing reliability and performance. Overall, Grok 4.3 offers a powerful AI solution for users who need real-time, high-level intelligence and automation.

Muse Wonder Full is a frontier AI model developed by Meta to support advanced reasoning, personal assistance, professional workflows, and autonomous software development. It is the larger model variant in the Muse Wonder series, which also includes the standard Muse Wonder model. The system combines Active Memory and Active Focus to maintain relevant context and sustain progress on complex assignments. Active Memory enables the model to preserve important information across conversations and retrieve details relevant to current objectives. Active Focus supports multistep execution by allowing the model to investigate alternative solutions, respond to obstacles, and adjust its approach as tasks evolve. Muse Wonder Full can assist with legal document analysis, financial reasoning, health-related information, and other knowledge-intensive activities. Its software engineering capabilities support code generation, application development, debugging, testing, and modifications across existing codebases. Meta has evaluated the model using benchmarks such as MemoryBench, FocusBench, SWE-Marathon, DeepSWE, and professional agent assessments. Muse Wonder Full is accessible through Muse Code subscriptions and is intended for tasks that require extended reasoning, contextual continuity, and coordinated AI agent execution.

xAI’s Grok 4 represents a major step forward in AI technology, delivering advanced reasoning, multimodal understanding, and improved natural language capabilities. Built on the powerful Colossus supercomputer, Grok 4 can process text and images, with video input support expected soon, enhancing its ability to interpret cultural and contextual content such as memes. It has outperformed many competitors in benchmark tests for scientific and visual reasoning, establishing itself as a top-tier model. Focused on technical users, researchers, and developers, Grok 4 is tailored to meet the demands of advanced AI applications. xAI has strengthened moderation systems to prevent inappropriate outputs and promote ethical AI use. This release signals xAI’s commitment to innovation and responsible AI deployment. Grok 4 sets a new standard in AI performance and versatility. It is poised to support cutting-edge research and complex problem-solving across various fields.

Grok 4.20 is a next-generation AI model created by xAI to advance the boundaries of machine reasoning and language comprehension. Powered by the Colossus supercomputer, it delivers high-performance processing for complex workloads. The model supports multimodal inputs, enabling it to analyze and respond to both text and images. Future updates are expected to expand these capabilities to include video understanding. Grok 4.20 demonstrates exceptional accuracy in scientific analysis, technical problem-solving, and nuanced language tasks. Its advanced architecture allows for deeper contextual reasoning and more refined response generation. Improved moderation systems help ensure responsible, balanced, and trustworthy outputs. This version significantly improves consistency and interpretability over prior iterations. Grok 4.20 positions itself among the most capable AI models available today. It is designed to think, reason, and communicate more naturally.

Grok-3, created by xAI, signifies a major leap forward in artificial intelligence technology, with aspirations to establish new standards in AI performance. This model is engineered as a multimodal AI, enabling it to interpret and analyze information from diverse channels such as text, images, and audio, thereby facilitating a more holistic interaction experience for users. Grok-3 is constructed on an unprecedented scale, utilizing tenfold the computational resources of its predecessor, harnessing the power of 100,000 Nvidia H100 GPUs within the Colossus supercomputer. Such remarkable computational capabilities are expected to significantly boost Grok-3's effectiveness across various domains, including reasoning, coding, and the real-time analysis of ongoing events by directly referencing X posts. With these advancements, Grok-3 is poised to not only surpass its previous iterations but also rival other prominent AI systems in the generative AI ecosystem, potentially reshaping user expectations and capabilities in the field. The implications of Grok-3's performance could redefine how AI is integrated into everyday applications, paving the way for more sophisticated technological solutions.

Grok 4.1, developed by Elon Musk’s xAI, represents a major step forward in multimodal artificial intelligence. Built on the Colossus supercomputer, it supports input from text, images, and soon video—offering a more complete understanding of real-world data. This version significantly improves reasoning precision, enabling Grok to solve complex problems in science, engineering, and language with remarkable clarity. Developers and researchers can leverage Grok 4.1’s advanced APIs to perform deep contextual analysis, creative generation, and data-driven research. Its refined architecture allows it to outperform leading models in visual problem-solving and structured reasoning benchmarks. xAI has also strengthened the model’s moderation framework, addressing bias and ensuring more balanced responses. With its multimodal flexibility and intelligent output control, Grok 4.1 bridges the gap between analytical computation and human intuition. It’s a model designed not just to answer questions, but to understand and reason through them.

Grok 4 Heavy represents xAI’s flagship AI model, leveraging a multi-agent architecture to deliver exceptional reasoning, problem-solving, and multimodal understanding. Developed using the Colossus supercomputer, it achieves a remarkable 50% score on the HLE benchmark, placing it among the leading AI models worldwide. This version can process text, images, and is expected to soon support video inputs, enabling richer contextual comprehension. Grok 4 Heavy is designed for advanced users, including developers and researchers, who demand state-of-the-art AI capabilities for complex scientific and technical tasks. Available exclusively through a $300/month SuperGrok Heavy subscription, it offers early access to future innovations like video generation. xAI has addressed past controversies by strengthening content moderation and removing harmful prompts. The platform aims to push AI boundaries while balancing ethical considerations. Grok 4 Heavy is positioned as a formidable competitor to other leading AI systems.

Grok 3 Think, the newest version of xAI's AI model, aims to significantly improve reasoning skills through sophisticated reinforcement learning techniques. It possesses the ability to analyze intricate issues for durations ranging from mere seconds to several minutes, enhancing its responses by revisiting previous steps, considering different options, and fine-tuning its strategies. This model has been developed on an unparalleled scale, showcasing outstanding proficiency in various tasks, including mathematics, programming, and general knowledge, and achieving notable success in competitions such as the American Invitational Mathematics Examination. Additionally, Grok 3 Think not only yields precise answers but also promotes transparency by enabling users to delve into the rationale behind its conclusions, thereby establishing a new benchmark for artificial intelligence in problem-solving. Its unique approach to transparency and reasoning offers users greater trust and understanding of AI decision-making processes.

Grok 4.1 Thinking is the reasoning-enabled version of Grok designed to handle complex, high-stakes prompts with deliberate analysis. Unlike fast-response models, it visibly works through problems using structured reasoning before producing an answer. This approach improves accuracy, reduces misinterpretation, and strengthens logical consistency across longer conversations. Grok 4.1 Thinking leads public benchmarks in general capability and human preference testing. It delivers advanced performance in emotional intelligence by understanding context, tone, and interpersonal nuance. The model is especially effective for tasks that require judgment, explanation, or synthesis of multiple ideas. Its reasoning depth makes it well-suited for analytical writing, strategy discussions, and technical problem-solving. Grok 4.1 Thinking also demonstrates strong creative reasoning without sacrificing coherence. The model maintains alignment and reliability even in ambiguous scenarios. Overall, it sets a new standard for transparent and thoughtful AI reasoning.

Grok 4.1 Fast represents xAI’s leap forward in building highly capable agents that rely heavily on tool calling, long-context reasoning, and real-time information retrieval. It supports a robust 2-million-token window, enabling long-form planning, deep research, and multi-step workflows without degradation. Through extensive RL training and exposure to diverse tool ecosystems, the model performs exceptionally well on demanding benchmarks like τ²-bench Telecom. When paired with the Agent Tools API, it can autonomously browse the web, search X posts, execute Python code, and retrieve documents, eliminating the need for developers to manage external infrastructure. It is engineered to maintain intelligence across multi-turn conversations, making it ideal for enterprise tasks that require continuous context. Its benchmark accuracy on tool-calling and function-calling tasks clearly surpasses competing models in speed, cost, and reliability. Developers can leverage these strengths to build agents that automate customer support, perform real-time analysis, and execute complex domain-specific tasks. With its performance, low pricing, and availability on platforms like OpenRouter, Grok 4.1 Fast stands out as a production-ready solution for next-generation AI systems.

Qwen3.5 represents a major advancement in open-weight multimodal AI models, engineered to function as a native vision-language agent system. Its flagship model, Qwen3.5-397B-A17B, leverages a hybrid architecture that fuses Gated DeltaNet linear attention with a high-sparsity mixture-of-experts framework, allowing only 17 billion parameters to activate during inference for improved speed and cost efficiency. Despite its sparse activation, the full 397-billion-parameter model achieves competitive performance across reasoning, coding, multilingual benchmarks, and complex agent evaluations. The hosted Qwen3.5-Plus version supports a one-million-token context window and includes built-in tool use for search, code interpretation, and adaptive reasoning. The model significantly expands multilingual coverage to 201 languages and dialects while improving encoding efficiency with a larger vocabulary. Native multimodal training enables strong performance in image understanding, video processing, document analysis, and spatial reasoning tasks. Its infrastructure includes FP8 precision pipelines and heterogeneous parallelism to boost throughput and reduce memory consumption. Reinforcement learning at scale enhances multi-step planning and general agent behavior across text and multimodal environments. Overall, Qwen3.5 positions itself as a high-efficiency foundation for autonomous digital agents capable of reasoning, searching, coding, and interacting with complex environments.

Grok 3 DeepSearch represents a sophisticated research agent and model aimed at enhancing the reasoning and problem-solving skills of artificial intelligence, emphasizing deep search methodologies and iterative reasoning processes. In contrast to conventional models that depend primarily on pre-existing knowledge, Grok 3 DeepSearch is equipped to navigate various pathways, evaluate hypotheses, and rectify inaccuracies in real-time, drawing from extensive datasets while engaging in logical, chain-of-thought reasoning. Its design is particularly suited for tasks necessitating critical analysis, including challenging mathematical equations, programming obstacles, and detailed academic explorations. As a state-of-the-art AI instrument, Grok 3 DeepSearch excels in delivering precise and comprehensive solutions through its distinctive deep search functionalities, rendering it valuable across both scientific and artistic disciplines. This innovative tool not only streamlines problem-solving but also fosters a deeper understanding of complex concepts.

Grok-2 represents the cutting edge of artificial intelligence, showcasing remarkable engineering that challenges the limits of AI's potential. Drawing inspiration from the humor and intelligence found in the Hitchhiker's Guide to the Galaxy and the practicality of JARVIS from Iron Man, Grok-2 transcends typical AI models by serving as a true companion. With its comprehensive knowledge base extending to recent events, Grok-2 provides insights that are not only informative but also infused with humor, offering a refreshing perspective on human nature. Its features allow it to tackle a wide range of inquiries with exceptional helpfulness, frequently presenting solutions that are both creative and unconventional. Grok-2's development prioritizes honesty, intentionally steering clear of the biases of contemporary culture, and aims to remain a trustworthy source of both information and amusement in a world that grows more intricate by the day. This unique blend of attributes positions Grok-2 as an indispensable tool for those seeking clarity and connection in a rapidly evolving landscape.

ERNIE 5.1 is Baidu’s next-generation large language model engineered to provide advanced reasoning, autonomous agent capabilities, creative writing performance, and enterprise-grade AI intelligence with highly optimized efficiency. Built on the pre-training foundation of ERNIE 5.0, the model significantly reduces parameter size and computational requirements while still delivering leading performance across major international AI benchmarks. ERNIE 5.1 demonstrates strong capabilities in reasoning, mathematical problem solving, knowledge retrieval, search tasks, and agentic workflows that allow it to handle complex multi-step operations and decision-making scenarios. The platform introduces a fully asynchronous reinforcement learning architecture designed to improve scalability, training efficiency, resource utilization, and long-horizon task stability for large-scale AI development. Baidu also implemented a multi-stage reinforcement learning pipeline that separates expert capability training from unified capability fusion, allowing the model to specialize in areas such as coding, reasoning, search, and conversational intelligence without creating performance conflicts between domains. ERNIE 5.1 supports advanced creative generation with improved emotional understanding, narrative structure control, stylistic adaptability, and contextual awareness for writing-intensive applications. The model performs competitively against leading closed-source global AI systems in knowledge benchmarks, reasoning evaluations, and creative content generation tasks. ERNIE 5.1 is also integrated into creative production platforms, AI storytelling systems, roleplay applications, and agentic AI environments that support content creators and enterprise workflows.

Kimi K2.6 is an advanced agentic AI model created by Moonshot AI, aiming to enhance practical implementation, programming, and complex reasoning compared to its predecessors, K2 and K2.5. This model is based on a Mixture-of-Experts framework and the multimodal, agent-centric principles of the Kimi series, merging language comprehension, coding capabilities, and tool utilization into one cohesive system that can plan and execute intricate workflows. It features enhanced reasoning skills and significantly better agent planning, enabling it to deconstruct tasks, synchronize various tools, and tackle multi-file or multi-step challenges with increased precision and effectiveness. Additionally, it provides robust tool-calling capabilities with a high degree of reliability, facilitating seamless integration with external platforms like web searches or APIs, and incorporates built-in validation systems to guarantee the accuracy of execution formats. Notably, Kimi K2.6 represents a significant leap forward in the realm of AI, setting new standards for the complexity and reliability of automated tasks.

Kimi K2.5 is a powerful multimodal AI model built to handle complex reasoning, coding, and visual understanding at scale. It supports both text and image or video inputs, enabling developers to build applications that go beyond traditional language-only models. As Kimi’s most advanced model to date, it delivers open-source state-of-the-art performance across agent tasks, software development, and general intelligence benchmarks. The model supports an ultra-long 256K context window, making it ideal for large codebases, long documents, and multi-turn conversations. Kimi K2.5 includes a long-thinking mode that excels at logical reasoning, mathematics, and structured problem solving. It integrates seamlessly with existing workflows through full compatibility with the OpenAI SDK and API format. Developers can use Kimi K2.5 for chat, tool calling, file-based Q&A, and multimodal analysis. Built-in support for streaming, partial mode, and web search expands its flexibility. With predictable pricing and enterprise-ready capabilities, Kimi K2.5 is designed for scalable AI development.

Grok is a powerful AI chatbot developed by xAI, designed to deliver real-time, intelligent, and conversational assistance. It is uniquely integrated with the X platform, enabling access to live data and trending topics for more relevant responses. Grok is built to handle a wide range of tasks, including answering questions, generating content, and assisting with research. The platform combines advanced reasoning capabilities with a conversational tone, often incorporating humor and personality. It uses large-scale language models to understand context and provide accurate, meaningful answers. Grok is particularly useful for staying updated on current events and social trends. Its real-time data access sets it apart from traditional AI assistants that rely on static knowledge. The platform is designed for both casual users and professionals seeking quick insights. It continuously evolves with updates and improvements from xAI. Overall, Grok delivers a modern AI experience focused on relevance, engagement, and real-time intelligence.