OpenCode Integrations

GPT-6.1 Terra is a multimodal AI model developed by OpenAI for visual reasoning, precise computer interaction, natural language generation, and high-volume professional automation. The model is designed to combine detailed image understanding with the ability to execute multistep tasks across software applications and graphical user interfaces. Its visual reasoning capabilities support analyzing technical drawings, interpreting complex images, identifying interface elements, and extracting structured information from visual materials. Computer-use functionality enables AI agents to complete online forms, enter invoice details into accounting systems, manipulate desktop applications, and assist with video editing workflows. GPT-6.1 Terra supports text and image inputs, text generation, a 1,050,000-token context window, and a maximum output length of 128,000 tokens. Developers can configure reasoning effort using none, low, medium, high, xhigh, and max settings to manage performance, response time, and computational requirements. Its software engineering capabilities include code generation, debugging, terminal-based problem-solving, and automated development tasks involving external tools. The OpenAI Responses API provides access to function calling, structured outputs, web search, file search, code interpretation, hosted shell execution, computer use, and Model Context Protocol integrations. In OpenAI's published evaluations, GPT-6.1 Terra demonstrated improvements over GPT-5.6 Terra in visual reasoning, desktop application automation, coding performance, and factual accuracy. The model includes safeguards for cybersecurity risks, prompt injection resistance, and adherence to user instructions, with safety protections aligned with OpenAI's GPT-6 model family.

GPT-6.1 Sol is OpenAI's upgraded Sol model for developers and professionals who need advanced reasoning and agentic capabilities without the higher cost of GPT-6 Astra. It is designed for coding, professional knowledge work, computer use, scientific research, factual question answering, and multi-step business workflows. OpenAI describes GPT-6.1 Sol as approaching GPT-6 Astra's intelligence across several important workloads while charging one-fifth of Astra's standard input and output token prices. On DeepSWE v1.1, which evaluates long-horizon software engineering in real codebases, GPT-6.1 Sol matches GPT-6 Astra at approximately one-fifth of the cost and exceeds GPT-6 Sol's best score by 6.4 percentage points. Its professional-work capabilities include understanding complex PDFs containing tables, charts, diagrams, and fine-print details across fields such as finance, healthcare, and legal work. On AutomationBench, GPT-6.1 Sol improves on GPT-6 Sol by 4.8 percentage points at the same reasoning setting and scores 2.2 points above Opus 5.5 at medium reasoning effort. Computer-use performance also advances significantly, with GPT-6.1 Sol outperforming GPT-6 Sol by seven percentage points on the OSWorld 2.0 offline set at maximum reasoning effort and coming within 2.1 points of GPT-6 Astra. For scientific research, the model can work with code and terminal tools on workflows involving data analysis, simulations, model fitting, and theorem proving, more than doubling GPT-6 Sol's Terminal-Bench Science 0.1 score at maximum effort. OpenAI also reports improved factual accuracy, including a reduction in the factual-error rate from 11.4% with GPT-6 Sol to 7.7% with GPT-6.1 Sol at low reasoning effort on its deliberately difficult factuality evaluation.

Claude Duet is a proactive AI assistant developed by Anthropic to automate personal productivity tasks, coordinate professional responsibilities, and complete multistep assignments across connected applications. The platform is powered by Claude Opus 5.5 and operates through a dedicated cloud computer that enables autonomous interaction with external services. Unlike traditional conversational assistants that primarily respond to user prompts, Claude Duet can proactively identify important developments and deliver personalized updates when attention or decisions are required. Its morning briefing functionality reviews relevant emails, calendar events, ongoing projects, and other connected information to summarize priorities and outstanding tasks. Users can delegate work through text or voice messages, including scheduling meetings, researching topics, preparing financial reports, and managing administrative workflows. The assistant can generate documents, spreadsheets, presentations, dashboards, and other deliverables using information retrieved from authorized applications. Claude Duet supports more than 6,000 connectors and plugins, including integrations with Gmail, Google Calendar, Slack, HubSpot, Salesforce, Shopify, QuickBooks, Notion, and Microsoft 365. Its persistent memory incorporates existing Claude conversations, saved context, skills, and connected tools to maintain continuity across tasks and interactions. The platform can continue working while users are away, monitor assigned activities, identify emerging issues, and provide notifications when tasks are completed or require intervention. Security and governance features include approval requirements for sensitive activities, isolated cloud execution, activity visibility, and the ability to review or redirect automated operations.

Claude Sonnet 5.5 is Anthropic’s faster, lower-cost Claude 5.5 model for well-scoped professional work, software development, agentic workflows, and content creation. Anthropic positions it below Claude Opus 5.5 for the most complex open-ended work, but says it can perform comparably on some benchmarks when run at higher effort settings. The model delivers a particularly large improvement in coding, including a 70.6% result on Terminal-Bench 4.0, 46.2% on FrontierCode at Max effort, and 55.5% on CursorBench 4.0. It also scores 1844 on GDPval-AA, 1811 on AA-Briefcase, 64.5% on Humanity’s Last Exam with tools, and 80.1% on OSWorld 2.1 partial. Anthropic reports that Sonnet 5.5 is better at long-horizon work, image understanding, collaboration, and codebase comprehension than Sonnet 5. The company also highlights design-oriented improvements, including the ability to produce polished documents, spreadsheets, slides, and user interfaces with less editing. Sonnet 5.5 generates output more than 30% faster than its predecessor and is Anthropic’s fastest Sonnet model to date. Although its listed token prices are unchanged from Sonnet 5, Anthropic says greater token efficiency can reduce per-task cost by up to 30%. Pricing is $2 per million input tokens, $10 per million output tokens, $2.50 per million cache-write tokens, and $0.20 per million cache-read tokens. The model includes cyber safeguards, biology safeguards, reasoning-extraction protections, and preserved-thinking controls, with some higher-risk cyber requests falling back to Sonnet 5. Claude Sonnet 5.5 is available with zero data retention across Claude products, the Claude Platform, AWS, Google Cloud, and Microsoft Azure under the model name claude-sonnet-5-5.

GPT-6 Astra is OpenAI’s advanced frontier model for computer use, coding, browsing, scientific research, cybersecurity, professional knowledge work, and long-running agentic tasks. It is designed to combine high-level reasoning with the ability to directly operate software and tools rather than only generating text responses. Astra can navigate websites, complete forms, update business systems, organize calendars, conduct research, analyze data, generate plots, test applications, and troubleshoot problems that appear on screen. For professional users, the model can create documents, spreadsheets, presentations, analyses, websites, and other artifacts while following existing templates, formatting requirements, and organizational styles. Its software engineering capabilities include codebase analysis, implementation, debugging, verification, browser testing, system configuration, and other terminal-based development workflows. In Codex, Astra can preserve notes across context windows and search earlier requirements, test results, messages, and tool outputs during lengthy development sessions. The model also combines scientific reasoning with computer use so researchers can work with specialized applications, inspect data, explore results, and assist with computational research processes. OpenAI reports substantial advances in Astra’s cybersecurity capabilities, while the production model applies safeguards to restrict higher-risk activities such as advanced exploit creation. Alignment improvements focus on interpreting user intent, respecting authorization boundaries, avoiding attempts to circumvent system restrictions, and communicating more accurately about what the model can and cannot do. GPT-6 Astra supports enterprise-oriented deployment features including eligible Zero Data Retention API configurations and is available through ChatGPT, the OpenAI API, Amazon Web Services, and Amazon Bedrock.

Claude Fable 5.1 is an advanced general-purpose AI model from Anthropic focused on coding, scientific research, knowledge work, business processes, and long-horizon agentic reasoning. It is the generally available counterpart to Claude Mythos 5.1, which uses the same underlying model but is offered with different safeguards for vetted cybersecurity and life sciences users. Compared with Claude Fable 5, Fable 5.1 shows stronger performance across agentic coding, research, computer use, multidisciplinary reasoning, business workflow automation, and other complex benchmarks. The model is designed to remain effective during long-running tasks that involve planning, tool use, repeated verification, code modification, research, and multi-step decision making. In software engineering scenarios, it can investigate difficult bugs, trace problems across large codebases, perform code review, and work through complex implementation tasks with less supervision. Anthropic also positions Fable 5.1 as a stronger research model, with demonstrated capabilities in scientific analysis, computational modeling, and other technically demanding workflows. Improvements to cache-read pricing reduce the cost of reusing previously processed context, making the model more economical for workflows that involve long conversations, large codebases, or repeated tool calls. Fable 5.1 introduces updated enterprise privacy and security options, including Enterprise Frontier Safeguards and zero-data-retention access for eligible customers during the rollout period. Its cybersecurity protections are designed to permit more benign defensive security work, including vulnerability discovery, while continuing to restrict higher-risk activities such as exploit development and certain penetration-testing tasks. The model is available through Claude.ai, Claude Code, Claude Cowork, the Claude API, Amazon Web Services, Google Cloud, and Microsoft Azure under the claude-fable-5-1 model identifier for API users.

Claude Mythos 5.1 signifies the latest evolution in the Mythos series of models developed by Anthropic, specifically designed for advanced applications across fields such as cybersecurity, biology, scientific research, programming, and extensive knowledge-intensive tasks. Although it is built on the same core architecture as Claude Fable 5.1, it stands out due to its distinct safety protocols: while Fable 5.1 is broadly available, Mythos 5.1 is restricted to select trusted access initiatives that incorporate specialized safeguards for cybersecurity and life sciences. This model sets a new standard for performance in autonomous coding and exhibits unmatched cyber capabilities compared to all previous Anthropic models. In the scientific research domain, Mythos 5.1 adeptly manages specialized tools and complex workflows related to molecular design, computational biology, and other technical disciplines. During Anthropic's evaluation, it successfully designed high-affinity protein binders for various targets, achieving its highest hit rate to date. Furthermore, it excelled in optimizing seven distinct open-source deep learning models that focus on protein and genomics. By advancing the limits of what can be accomplished, Mythos 5.1 is poised to play a pivotal role in shaping future research and development projects, ultimately influencing a wide array of scientific inquiries and technological innovations. Its capabilities suggest a transformative impact on how complex biological and computational problems are approached in the coming years.

GPT-5.6 Sol is a next-generation OpenAI model previewed as the flagship option in the GPT-5.6 family. The series includes Sol for the strongest capability, Terra for balanced everyday work, and Luna for faster, lower-cost use cases. GPT-5.6 Sol is built for demanding work across coding, agentic automation, biology, cybersecurity, research, and enterprise knowledge workflows. The model introduces a new max reasoning effort that allows it to spend more time reasoning through difficult problems. It also adds ultra mode, which coordinates subagents to help accelerate complex tasks that benefit from parallel or multi-agent execution. In coding workflows, GPT-5.6 Sol is designed for command-line tasks that require planning, iteration, testing, tool coordination, and long-horizon software engineering judgment. In biology workflows, it is positioned for genomics and quantitative-biology analysis where efficient reasoning over complex scientific tasks matters. In cybersecurity, GPT-5.6 Sol supports legitimate defensive work such as vulnerability discovery, patch development, debugging, security education, code review, and authorized testing. OpenAI describes GPT-5.6 Sol as more capable at helping users find and fix vulnerabilities than reliably carrying out end-to-end attacks under tested conditions. The model’s release is paired with a layered safeguard system that includes model-level refusals, real-time misuse classifiers, paused generation for higher-risk cases, account-level review, automated red-teaming, third-party testing, differentiated access, and enterprise safety controls. GPT-5.6 Sol helps developers, researchers, enterprises, and cyber defenders use frontier AI for advanced technical work while supporting safer deployment, stronger oversight, and phased access.

Claude Opus 5.5 is Anthropic’s high-capability AI model for advanced coding, research, business work, computer use, and extended agentic tasks. It is designed to operate effectively on large and complex workloads that require planning, sustained context, tool use, verification, and multiple execution steps. In software engineering, Opus 5.5 can be used for codebase-wide migrations, debugging, audits, optimization, code review, and other long-running development projects. The model also supports knowledge-intensive work such as financial analysis, legal research, spreadsheet creation, executive presentations, data collection, and professional reporting. Anthropic reports that Opus 5.5 improves both task efficiency and serving efficiency compared with Opus 5, including lower token usage, faster output, and reduced cost on typical workloads. Its writing and communication behavior has been updated to prioritize important information, reduce unclear phrasing, and better follow requested style constraints. Opus 5.5 also includes stronger safeguards for autonomous and tool-using scenarios, including action screening, improved prompt-injection resistance, sandbox support, and vulnerability detection during code review. Anthropic applies additional safeguards to cybersecurity, biology, and model-distillation use cases, with expanded access programs available to verified organizations in certain sensitive fields. The model supports zero data retention and includes watermarking measures intended to support compliance requirements such as the EU AI Act. Developers can access Opus 5.5 through the Claude Platform using the claude-opus-5-5 model, while Claude Code and other Anthropic products can use it for interactive and agentic work. Claude Opus 5.5 is also available through Amazon Web Services, Google Cloud, and Microsoft Azure for organizations that prefer to deploy through major cloud platforms.

GPT-6 Luna is OpenAI’s efficiency-focused GPT-6 model for developers and users who need capable reasoning, coding, computer use, and agentic workflows at very low inference cost. It is positioned below GPT-6 Sol and GPT-6 Astra in the model family while bringing many of the GPT-6 generation’s improvements to applications that prioritize scale and affordability. The model supports configurable reasoning effort so developers can allocate additional computation to complex tasks while keeping simpler interactions fast and economical. GPT-6 Luna can power business automation across applications used for sales, marketing, finance, operations, customer support, and human resources. Its coding capabilities support work on real software repositories, including multi-step engineering tasks that require analysis, modification, testing, and iteration. Luna can also operate in computer-use environments, allowing agents to navigate graphical interfaces and complete extended workflows across software applications. OpenAI reports that GPT-6 Luna substantially improves factual reliability compared with GPT-5.6 Luna and can approach the capabilities of more expensive models on some tasks when used at higher reasoning levels. The model also benefits from GPT-6’s improved collaboration style, with clearer technical communication, less unnecessary jargon, and fewer low-value details. Enhanced prompt caching allows applications to reuse previously processed context at a discount while preserving cache reuse when reasoning effort or available tools change. These efficiency improvements make Luna suitable for high-volume agents, coding assistants, automated workflows, customer-facing applications, and other systems where per-request cost is important. GPT-6 Luna is available through the OpenAI API as gpt-6-luna, as well as through ChatGPT Work, Codex, and supported ChatGPT desktop experiences.

GPT-6 Sol is an advanced OpenAI model positioned between the cost-efficient GPT-6 Luna and the higher-capability GPT-6 Astra for demanding professional and agentic workloads. The model is designed for coding, knowledge work, business automation, computer use, research, and other tasks that require sustained reasoning across multiple steps. It inherits advances from the GPT-6 generation while emphasizing a balance of intelligence, speed, and operating cost for applications that need to run at scale. GPT-6 Sol supports multiple reasoning-effort levels so applications can spend more computation on difficult tasks and reduce effort for straightforward requests. In software development, it can handle complex real-codebase tasks, generate merge-ready changes, debug software, work through terminal workflows, and operate as part of coding agents. Its professional-work capabilities support multi-application processes spanning functions such as finance, operations, sales, marketing, customer support, and human resources. Computer-use abilities allow agents powered by GPT-6 Sol to interact with graphical interfaces and complete long-horizon workflows involving everyday and professional software. OpenAI has also improved the model’s factual reliability, communication style, and alignment compared with GPT-5.6 Sol, including lower rates of misleading claims in challenging coding evaluations. GPT-6 prompt caching provides higher cache-hit rates, supports changing reasoning effort or available tools without invalidating earlier cached context, and offers substantial discounts for cached input tokens. Developers can monitor caching behavior, configure prompt-cache breakpoints, and incorporate Sol into persistent agents that repeatedly reuse large amounts of context. GPT-6 Sol is accessible through ChatGPT Work, Codex, and the OpenAI API under the gpt-6-sol model identifier.

Kimi K3.1 Pro is a multimodal large language model developed by Moonshot AI to support advanced reasoning, autonomous AI agents, software development, 3D creation, and complex knowledge work. The model is based on a mixture-of-experts architecture containing approximately 2.8 trillion total parameters and 104 billion active parameters. Its one-million-token context window enables the processing of extensive documents, source code repositories, technical materials, and long-running task histories. Kimi K3.1 Pro builds on the Kimi K3 foundation with additional reinforcement learning and post-training optimization across programming, analytical reasoning, and agentic tasks. Its software engineering capabilities include code generation, debugging, terminal operations, automated testing, application development, and multistep problem-solving across existing repositories. The model can develop interactive websites, animated interfaces, browser-based 3D environments, and game projects using tools and frameworks such as Three.js, Unity, Unreal Engine, and Godot. Its multimodal reasoning capabilities allow it to interpret images, sketches, and visual references when generating technical designs or structured outputs. Three-dimensional modeling features include CAD script generation, parametric geometry creation, asset modification, and integration with tools such as Blender. Financial reasoning capabilities support complex spreadsheet models, financial statement analysis, investment research, cross-document verification, and evidence-based reporting. Kimi K3.1 Pro also supports tool-assisted workflows in which AI agents plan tasks, execute actions, evaluate results, and refine outputs across multiple steps. The model is available through Kimi's web and mobile applications, Kimi Work, Kimi Code, and the Kimi API, with a Highspeed variant providing faster inference and an open-weight release planned for November 4, 2026.

Muse Wonder is a frontier artificial intelligence model developed by Meta for advanced reasoning, autonomous agent workflows, personal assistance, and professional applications. Released in October 2026, the model belongs to the Muse Wonder family alongside Muse Wonder Full, a larger variant designed for computationally demanding tasks. The model incorporates Active Memory, a capability that preserves important contextual information across conversations and retrieves relevant details to support personalized assistance. Its Active Focus functionality enables sustained execution of complex objectives by exploring alternative solutions, adapting to new information, and working through obstacles. Muse Wonder can request clarification when instructions are incomplete and seek user approval before performing consequential actions. The model supports professional tasks involving legal analysis, financial information, health-related questions, research, and business process automation. Its agentic capabilities allow it to interact with connected applications, navigate browser-based workflows, and use external tools to complete multistep assignments. For software development, Muse Wonder can generate code, build applications, analyze existing projects, modify source files, and perform iterative testing and debugging. Meta trained the Muse Wonder series on diverse long-running tasks to improve performance on software engineering projects and unfamiliar problem-solving scenarios. The model has been evaluated using benchmarks such as MemoryBench, FocusBench, AutomationBench-AA, SWE-Marathon, and DeepSWE, with additional safety evaluations addressing privacy protection, adversarial instructions, and misuse prevention. Muse Wonder is available through the Meta Model API and Muse Code, while Muse Wonder Full is available to Muse Code subscribers, with broader integration into Meta's Muse assistant planned.

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.

MiMo-V2.6-Pro is Xiaomi MiMo’s flagship open-source omnimodal model for software engineering, agentic automation, multimodal reasoning, visual design, research, and creative production. The model was developed through large-scale reinforcement learning on heterogeneous tasks spanning coding, general agents, visual workflows, and cybersecurity. Xiaomi trained MiMo-V2.6-Pro across roughly 750,000 trajectories using large asynchronous batches, long-context training, multi-task environments, and expanded grader compute. The resulting model is designed to plan, execute, verify, and refine complex work across multiple tools and interaction environments. In software development, MiMo-V2.6-Pro supports long-horizon coding, terminal work, automation, debugging, and other agent-driven engineering tasks. Its multimodal capabilities allow it to generate interactive 3D worlds, create Blender assets from text or reference images, and control simulated robotic systems using continuous visual feedback. The model can also build frontend interfaces, design slide decks, work with Figma and media-generation tools, and automate portions of video production from concept through editing and narration. Creative capabilities extend to music composition, including generating arrangements, musical scores, and MIDI output. For research, MiMo-V2.6-Pro has been demonstrated performing literature searches, generating scientific hypotheses, running computational tools, screening materials, and assisting with formal mathematical proofs. Xiaomi has open-sourced the model family together with its technical report, reinforcement learning environments, and training code to support reproducibility and further research. MiMo-V2.6-Pro is available through Xiaomi MiMo’s desktop and developer products, OpenRouter, Hugging Face, and an API, with an UltraSpeed version offered for workflows that require much faster generation.

Claude Opus 5 is Anthropic’s advanced Opus model designed for high-value coding, knowledge work, problem-solving, automation, scientific research, and everyday AI workflows. The model is positioned as a thoughtful and proactive system that approaches the frontier intelligence of Claude Fable 5 at half the price. Anthropic says Claude Opus 5 delivers greatly improved performance for the same cost as Opus 4.8, with base pricing of $5 per million input tokens and $25 per million output tokens. The model supports effort settings that allow customers to optimize for deeper intelligence or conserve tokens for faster and cheaper results. Claude Opus 5 performs especially well on software engineering evaluations, including tasks that require debugging, code generation, root-cause analysis, test creation, and multi-step implementation. It also shows strong results on knowledge work, business automation, computer use, novel problem solving, and research-heavy tasks. Anthropic highlights that Opus 5 is better at checking its own work, iterating until it succeeds, and building supporting tools when a task requires it. The model improves on Opus 4.8 across life sciences evaluations, including structural biology, organic chemistry, bioinformatics, molecular structure inference, and protein function tasks. Claude Opus 5 includes alignment and safety protections that aim to allow beneficial cybersecurity and biology use cases while restricting riskier exploit generation, penetration testing, and certain autonomous misuse scenarios. It is available on Claude Max as the default model, on Claude Pro as the strongest model, and through the Claude API as claude-opus-5, with a Fast mode that runs around 2.5 times the default speed.

Claude Fable 5 is a frontier AI model developed by Anthropic to deliver advanced reasoning, coding, research, and multimodal capabilities for enterprise and professional users. As a Mythos-class model adapted for broad availability, it combines high-level intelligence with safety-focused deployment controls. The model excels at software engineering tasks, including large-scale code analysis, migrations, debugging, architecture review, and autonomous project execution. Claude Fable 5 also demonstrates strong performance in knowledge work, helping users analyze documents, evaluate financial information, interpret charts and tables, conduct research, and generate actionable insights. Its vision capabilities enable sophisticated image understanding, visual reasoning, and screenshot-based analysis. The model supports long-context workflows and persistent memory utilization, allowing it to work effectively on extended tasks involving millions of tokens of information. Anthropic has implemented a layered safety framework that includes specialized classifiers for cybersecurity, biology, chemistry, and model distillation-related requests. When these areas are detected, requests may be handled by a different model with stricter operational controls. Claude Fable 5 is available through the Claude API and Anthropic’s product ecosystem, providing developers and enterprises with access to advanced AI-powered assistance. The model is designed to enhance productivity, accelerate research, improve software development workflows, and support complex analytical tasks. By combining powerful reasoning, multimodal intelligence, and enterprise-focused safeguards, Claude Fable 5 enables organizations to scale AI adoption responsibly and effectively.

GPT-5.6 Luna is the lowest-cost model in OpenAI’s GPT-5.6 family, built for fast and affordable AI assistance across everyday and technical workflows. The GPT-5.6 lineup includes Sol as the flagship model, Terra as the balanced model for everyday work, and Luna as the efficient model for users who need strong capability at lower cost. Luna is intended for developers, businesses, and teams that need scalable AI for coding help, workflow automation, research support, analysis, customer-facing applications, and high-volume API usage. In the pasted preview text, Luna is presented as part of the same GPT-5.6 release process and benchmark set as Sol and Terra. It appears in evaluations for command-line coding workflows, long-horizon biology tasks, ExploitBench, and ExploitGym, indicating that it is designed to handle more than simple chat use cases. The model is priced at a lower per-token rate than Sol and Terra, making it more suitable for applications where cost efficiency is a major priority. GPT-5.6 Luna also supports the new GPT-5.6 prompt caching approach, including explicit cache breakpoints, a 30-minute minimum cache life, cache writes billed above the uncached input rate, and discounted cached-input reads. Like the rest of the GPT-5.6 family, Luna is developed with layered safeguards matched to model capability. These safeguards include trained refusals for prohibited cyber assistance, real-time misuse classifiers, paused generation for higher-risk cases, account-level review, monitoring, enforcement, automated red-teaming, and third-party human expert red-teaming. Luna is expected to support legitimate defensive and technical workflows such as code review, debugging, patch development, security education, and defensive testing while making prohibited misuse more difficult and detectable. GPT-5.6 Luna helps organizations deploy GPT-5.6-class AI where speed, affordability, scalability, and safe production use are the most important requirements.

Kimi K3 is Moonshot AI’s most advanced model, designed for high-end reasoning, software engineering, multimodal understanding, knowledge work, and agentic AI applications. The model has 2.8 trillion parameters and is built on Kimi Delta Attention, a hybrid linear attention mechanism created for long-context performance. It also uses Attention Residuals and supports a native context window of up to 1 million tokens. This makes Kimi K3 suitable for tasks involving large codebases, long research materials, enterprise documentation, multi-file analysis, legal documents, technical manuals, and complex workflows. Kimi K3 always has thinking mode enabled, with reasoning effort configured through the reasoning_effort field and maximum effort currently supported as the default. Developers can use the model through an OpenAI-compatible API, making it easier to integrate with existing SDKs, clients, and application infrastructure. The model supports streaming responses with separate reasoning and final-answer deltas, allowing applications to display reasoning progress and final content differently. Kimi K3 also supports strict structured output with JSON Schema, partial mode for continuing from a prefix, custom tool calling, required tool use, and dynamic tool loading through system messages. Its vision capabilities support image and video inputs through base64 or uploaded files, enabling analysis of visual content alongside text. Automatic context caching helps workflows that reuse long prefixes, such as large knowledge bases or persistent system context, without requiring developers to manage cache IDs manually. By combining frontier-scale parameters, long-context processing, visual input, structured outputs, tool orchestration, and developer-friendly API compatibility, Kimi K3 gives teams a strong foundation for advanced AI agents, coding assistants, research systems, enterprise automation, and multimodal applications.

GPT-5.6 Terra is a balanced model in OpenAI’s GPT-5.6 series, designed to provide strong performance for everyday work while keeping costs lower than the flagship Sol tier. The GPT-5.6 family includes Sol for the highest capability, Terra for balanced work, and Luna for fast and affordable use cases. Terra is positioned as a practical option for developers, businesses, and enterprise teams that need capable reasoning, coding, automation, research support, and defensive security assistance without always using the most expensive model. According to the pasted preview text, Terra offers competitive performance to GPT-5.5 while being 2x cheaper. It appears in GPT-5.6 benchmark previews for Terminal-Bench 2.1, GeneBench v1, ExploitBench, and ExploitGym, showing that the model is intended for technical and long-horizon tasks as well as general work. Terra can support coding workflows that require planning, iteration, command-line reasoning, and tool coordination. It can also support legitimate cybersecurity workflows such as code review, vulnerability research, patch development, debugging, security education, and defensive testing. The model is developed with layered safeguards matched to its capabilities, including trained refusals, real-time checks, misuse classifiers, monitoring, enforcement, and account-level review. OpenAI also describes automated red-teaming and third-party human expert red-teaming as part of the broader GPT-5.6 safety process. Terra is priced below Sol in the pasted API pricing structure, with lower input and output costs per 1 million tokens. GPT-5.6 Terra helps organizations use a capable GPT-5.6 model for production workflows where performance, cost efficiency, and safety controls all matter.

Claude Mythos 5 is Anthropic’s restricted-access Mythos-class AI model built for trusted organizations that require the highest level of Claude capability. The model shares the same underlying architecture as Claude Fable 5, but is offered with certain safeguards removed for approved use cases and vetted users. Claude Mythos 5 is designed for advanced cybersecurity, software engineering, scientific discovery, long-context reasoning, and autonomous research workflows. It is initially deployed through Project Glasswing for cyberdefenders and critical infrastructure providers. The model is intended to help security teams analyze complex systems, support defensive cybersecurity work, and protect important software environments. Claude Mythos 5 also demonstrates major potential in life sciences, where it can assist with protein design, binding-site selection, bioinformatics workflows, and research hypothesis generation. Anthropic reports that the model can carry out extended technical tasks, recover from failures, and operate with a high degree of autonomy. Its capabilities in genomics include assembling large-scale single-cell datasets and designing custom machine learning approaches for biological research. Because these capabilities may be dual-use, Anthropic limits access through trusted programs and applies a 30-day retention policy for Mythos-class traffic. The model is priced at $10 per million input tokens and $50 per million output tokens. Claude Mythos 5 helps vetted organizations apply frontier AI to critical defense, infrastructure, and scientific problems while maintaining controlled access and oversight.

Claude Code is an advanced AI coding assistant created to deeply understand and work within real software projects. Unlike traditional coding tools that focus on syntax or snippets, it comprehends entire repositories, dependencies, and architecture. Developers can interact with Claude Code directly from their terminal, IDE, Slack workspace, or the web interface. By using natural language prompts, users can ask Claude to explain unfamiliar code, refactor components, or implement new features. The tool performs agentic searches across the codebase to gather context automatically, removing the need to manually select files. This makes it especially valuable when joining new projects or working in large, complex repositories. Claude Code can also run CLI commands, tests, and scripts as part of its workflow. It integrates with version control platforms to help manage issues, commits, and pull requests. Teams benefit from faster iteration cycles and reduced context switching. Claude Code supports multiple powerful Claude models depending on the plan selected. Usage scales from short sprints to large, ongoing development efforts. Overall, it acts as a collaborative coding partner that enhances productivity without disrupting established workflows.

Claude Haiku 5.5 is an Anthropic AI model built for high-volume and latency-sensitive AI workloads that require a balance of speed, cost, context capacity, and reasoning. Its primary use cases include classification, routing, information extraction, and subagent tasks within larger AI systems. Claude Haiku 5.5 introduces adaptive thinking, which allows the model to decide when reasoning is needed and how much thinking to apply before producing a response. Adaptive thinking is enabled by default, while the effort parameter gives developers a way to trade response quality against latency and cost. Developers can disable thinking at high effort or below, but Anthropic recommends using effort as the primary mechanism for controlling the model's reasoning behavior. The model provides a 1 million token context window, representing a substantial increase from the 200,000-token context window available with Claude Haiku 4.5. It can generate as many as 128,000 output tokens compared with the previous model's 64,000-token maximum, although thinking tokens are included within the configured output allowance. Browser use is available through the Claude API and Google Cloud, enabling developers to incorporate the model into workflows that require interaction with browser-based environments. Claude Haiku 5.5 uses the newer tokenizer found in Claude 4.7 and later models, resulting in approximately 30% more tokens for the same text compared with Claude Haiku 4.5, depending on the content. The model also changes several API behaviors, including replacing manual extended thinking with adaptive thinking, rejecting non-default sampling parameters and assistant-message prefills, and requiring updated computer-use tooling for supported environments.

Claude Opus 4.8 is Anthropic’s latest frontier AI model engineered to deliver advanced coding intelligence, reasoning capabilities, autonomous workflows, and enterprise-grade collaboration for developers, technical teams, and organizations building AI-powered systems. As the successor to Claude Opus 4.7, the model introduces improvements across software engineering, agentic execution, practical knowledge work, benchmark performance, and alignment behavior while retaining the same standard pricing structure. Claude Opus 4.8 is specifically optimized for complex coding tasks, large-scale workflow orchestration, long-running automation processes, and advanced reasoning scenarios where reliability, transparency, and contextual judgment are critical. One of the model’s defining advancements is its improved honesty and uncertainty awareness, making it significantly less likely to produce unsupported conclusions or overlook defects in generated code, reasoning chains, and operational outputs. Anthropic’s alignment assessments also report stronger prosocial behavior, lower rates of deceptive or unsafe actions, and improved adherence to user intent compared to earlier Opus releases. The release introduces configurable effort controls that allow users to determine how much computational reasoning the model applies to a task, enabling flexible tradeoffs between speed, token consumption, and response depth depending on workflow complexity. Claude Opus 4.8 also powers new “dynamic workflows” functionality in Claude Code, where the model can coordinate hundreds of parallel AI subagents during a single session to execute large-scale software engineering operations such as repository-wide migrations, testing workflows, and multi-step automation tasks. Anthropic further expanded the platform with lower-cost fast mode processing, enabling the model to operate at significantly higher speeds while remaining more affordable than previous high-performance configurations.

Kimi K2.7 Code is an open-source agentic coding model from Moonshot AI designed for developers, engineering teams, and AI coding workflows that require long-context understanding and multi-step execution. It is built for real-world software engineering tasks, including code generation, code review, debugging, repository navigation, tool use, and long-horizon development work. The model is described by Moonshot AI as a coding-focused agentic model with stronger performance on complex coding tasks than earlier Kimi K2 releases. Kimi K2.7 Code supports a 256K context window, allowing it to process large codebases, technical requirements, logs, documentation, and multi-file development context in a single workflow. It is available through Kimi Code, which provides developer-oriented tools for using the model in coding tasks. The model can also be accessed through Moonshot’s API platform, where Kimi K2.7 Code and Kimi K2.7 Code Highspeed are offered alongside earlier Kimi models. For developers who want more control, Kimi K2.7 Code is listed on Hugging Face with deployment support for inference engines such as vLLM, SGLang, and KTransformers. It uses OpenAI- and Anthropic-compatible API options, helping teams connect it to existing applications, coding tools, and agent systems more easily. Third-party model listings describe it as using a 1T-parameter mixture-of-experts architecture with 32B active parameters, native INT4 quantization, and reduced thinking-token usage compared with Kimi K2.6. The model is designed to improve efficiency by using fewer reasoning tokens while still supporting demanding programming workflows. Kimi K2.7 Code is a strong fit for developers who want an open, long-context, tool-friendly AI model for software engineering automation and AI-assisted development.

Muse Spark 1.2 is a coding-focused AI model from Meta designed to support advanced software engineering tasks through Muse Code and the Meta Model API. The model builds on Muse Spark 1.1 with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan repository changes, write code, validate outputs, and work across large codebases. Muse Code uses persistent async background agents that stay active throughout a session to reduce redundant information gathering and support difficult multi-step work. The runtime uses a local event log where model calls, tool runs, approvals, and edits are appended, making sessions replay-exact and restart-safe. Muse Spark 1.2 was co-trained with Muse Code so the model can take advantage of its toolset, harness workflows, goals, compaction, and subagent architecture. Meta significantly scaled training compute on coding tasks and expanded training environment diversity to improve the model’s engineering capabilities. The model was also trained on long-horizon coding tasks, including whole-repository generation, large end-to-end projects, auto-research, and extended iterative work. Its training approach uses planning, goal conditioning, context compaction, rejection-sampled harness trajectories, and self-improvement data generated with Muse Spark 1.1. Meta also tested Muse Spark 1.2 on long-running GPU kernel optimization workflows where the model wrote, compiled, profiled, and improved Triton kernels over many tool calls. By combining coding-focused training, agentic runtime integration, persistent subagents, long-horizon reasoning, replay-safe execution, and API availability, Muse Spark 1.2 helps developers and AI agents complete complex software engineering work with less intervention.

Fugu Cyber represents a sophisticated orchestration framework crafted explicitly for modern cyber defense, operating seamlessly as a single unit through one API endpoint while skillfully coordinating various specialized agents to address complex security issues. This cutting-edge model operates independently of any single provider and is designed for two primary defensive functions: evaluating intricate codebases to uncover real vulnerabilities and translating raw cyber threat intelligence into practical detection rules. Its performance metrics on CyberGym, which assesses vulnerability analysis and validation, showcased an impressive success rate of 86.9%, while on CTI-REALM, which measures the creation of detection rules from threat intelligence reports, it garnered a score of 72.1%. Such outcomes position Fugu Cyber among the elite models dedicated to advancements in cybersecurity. Rather than being a standalone solution, Fugu Cyber acts as the cognitive core within comprehensive security frameworks, bolstering overall defensive capabilities against emerging cyber threats. This strategic integration fosters a more cohesive approach to cyber defense, empowering organizations to respond with greater efficiency to potential incursions. Additionally, it allows for continuous improvement in threat detection and response strategies, making it an invaluable asset in the ever-evolving cybersecurity landscape.

Laguna S 2.1 represents a state-of-the-art open weight coding model that focuses on the completion of long-term projects and demonstrates exceptional reasoning abilities. With a Mixture-of-Experts architecture comprising 118 billion parameters, it engages 8 billion parameters per token and supports a context window of up to one million tokens in both cognitive and non-cognitive modes. The model’s optimized active size enables it to execute complex tasks on local systems while remaining competitive with much larger models across a variety of benchmarks, such as terminal usage, software development, codebase question answering, and tool application. Built for durability, Laguna S 2.1 is adept at addressing demanding challenges with an emphasis on thorough verification and a willingness to backtrack when necessary, rather than hastily claiming victory. In real-world scenarios, it has successfully engineered a browser rendering engine from the ground up, improved an agent harness for faster execution and lower memory requirements, and conducted comprehensive mathematical investigations using the tools available in its environment, showcasing its adaptability and proficiency. This remarkable array of capabilities positions Laguna S 2.1 as an invaluable asset for developers in search of cutting-edge solutions, making it a top choice in the ever-evolving landscape of coding models.

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.

GPT-5.5 represents a new class of AI built to transform how work is done across digital environments. It combines advanced reasoning, tool usage, and task execution capabilities to manage complex, multi-step workflows with minimal human intervention. The model performs strongly in software engineering, data analysis, business operations, and scientific research, where it can plan tasks, gather information, test solutions, and refine outputs iteratively. It supports generating documents, building applications, analyzing large datasets, and navigating software systems as part of a unified workflow. A key capability is its integration with workspace agents—customizable AI agents that can be created once and deployed across teams to automate entire processes. These agents can run continuously, interact with tools like CRM systems, messaging platforms, and document editors, and keep workflows moving without constant supervision. Organizations can define permissions, approval checkpoints, and monitoring to maintain full control over automation. GPT-5.5 also improves collaboration by standardizing workflows and scaling best practices across teams. With enterprise-grade security and governance, it is designed for safe deployment in complex environments. Its ability to persist through ambiguity and long-running tasks makes it highly effective for execution-heavy work. By reducing manual intervention and increasing speed, GPT-5.5 enables teams to focus on higher-value activities and operate at a significantly higher level of productivity.

Visual Studio Code (VS Code) is an advanced, open-source AI-integrated development environment (IDE) built for productivity, creativity, and collaboration. Designed by Microsoft, it unites code editing, debugging, version control, and AI automation in a single cross-platform workspace. Developers can code in nearly any language—including JavaScript, TypeScript, Python, C++, C#, Java, PHP, and more—using built-in tools or specialized extensions. Its AI features, such as Agent Mode and Next Edit Suggestions, transform VS Code into an autonomous assistant capable of reading entire codebases, suggesting edits, and resolving compile or test issues automatically. With MCP (Model Context Protocol) and multi-model support, developers can use AI models like Claude Sonnet, GPT-4, or Gemini—or connect their own keys via OpenRouter or Azure OpenAI. Deep GitHub integration, remote development, and cloud portability make collaboration effortless across global teams. VS Code’s extensive marketplace offers tens of thousands of extensions—from Docker to Jupyter and Stripe—empowering developers to build and deploy in any environment. Custom profiles, themes, and settings sync allow for a personalized workspace across devices. Its integrated terminal, local history, and debugging tools ensure smooth workflows from prototype to production. Whether building enterprise software, web apps, or AI agents, VS Code redefines what a modern coding experience should feel like: intelligent, fast, and universally accessible.

Devin Desktop is an AI-powered integrated development environment that enables developers to manage fleets of coding agents while maintaining complete control over the software development lifecycle. Built as the evolution of Windsurf, the platform combines advanced AI agents, a fully featured IDE, and collaborative workflow management into a single development experience. Developers can assign coding tasks to local or cloud-based agents, allowing autonomous execution of research, implementation, testing, debugging, optimization, and documentation activities. The platform's Agent Command Center provides centralized visibility into ongoing agent work, making it easier to coordinate multiple development efforts simultaneously. Features such as Spaces enable shared context and Git worktrees across agents, while Fast Context rapidly surfaces relevant code, files, and dependencies to accelerate development. Devin Desktop includes Supercomplete, which predicts developer intent beyond simple code completion, helping users work faster and remain focused. The platform supports multiple AI models and agent frameworks through the Agent Client Protocol, providing flexibility across different coding workflows and use cases. Extensive integrations with development, collaboration, monitoring, and project management tools allow organizations to connect AI-assisted development with their existing technology stack. Built-in code review, debugging, and traceability features ensure developers can inspect, validate, and refine every AI-generated change before deployment. The platform is designed for organizations that want to scale AI-assisted software engineering while maintaining visibility, governance, and code quality standards. Devin Desktop helps developers and engineering teams accelerate software delivery by combining autonomous AI execution with professional development tools and human oversight.

GitHub remains the foremost platform for developers around the world, celebrated for its robust security, impressive scalability, and strong community engagement. By becoming part of the vast network of millions of developers and organizations, you can play a role in creating the software that propels society forward. Engage and collaborate with some of the most innovative communities while taking advantage of our exceptional tools, support, and services. If you are managing multiple contributors, consider utilizing our complimentary GitHub Team for Open Source feature. Furthermore, GitHub Sponsors is designed to help finance your initiatives and projects effectively. We are excited to bring back The Pack, a program that offers students and educators free access to top-notch developer tools throughout the academic year and beyond. In addition, if you are affiliated with a recognized nonprofit, association, or a 501(c)(3) organization, we provide a discounted Organization account to help further your mission. Through these initiatives, GitHub continues to empower a diverse range of users in their software development endeavors, fostering a more inclusive tech community. With ongoing support and resources, GitHub is dedicated to enhancing the development experience for everyone involved.

GitLab serves as a comprehensive DevOps platform that provides an all-in-one CI/CD toolchain, simplifying the workflow for teams. With a singular interface, unified conversations, and a consistent permission model, GitLab transforms collaboration among Security, Development, and Operations teams within a single application. This integration leads to significant reductions in development time and costs, minimizes application vulnerabilities, and accelerates software delivery processes. Furthermore, it enhances developer productivity by facilitating source code management that promotes collaboration, sharing, and coordination among the entire software development team. To expedite software delivery, GitLab enables efficient tracking and merging of branches, auditing of changes, and supports concurrent work efforts. Teams can review code, engage in discussions, share knowledge, and pinpoint defects, even in distributed settings, through asynchronous review processes. Additionally, the platform automates and tracks code reviews, generating reports that enhance transparency and continuous improvement in the development cycle. By offering these robust features, GitLab not only streamlines operations but also fosters a culture of collaboration and efficiency within development teams.

GitHub Copilot is an AI-powered developer platform designed to enhance productivity across the entire software development workflow. It works directly within IDEs, terminals, and GitHub to assist with coding, debugging, and collaboration. Copilot offers intelligent code completion, explanations, refactoring, and real-time suggestions. Developers can leverage agent-based capabilities to let Copilot autonomously handle tasks like writing code, creating pull requests, and responding to feedback. The platform supports multiple industry-leading AI models, giving teams flexibility in performance and cost optimization. Copilot CLI brings AI assistance to the command line for complex, context-aware workflows. Teams can customize Copilot with organizational knowledge to ensure consistency and shared best practices. Enterprise-grade controls allow administrators to manage access, monitor usage, and enforce governance. Secure MCP integrations help organizations control how external tools connect to Copilot. Copilot scales easily from individual developers to large enterprises. It integrates seamlessly with existing GitHub workflows and tools. GitHub Copilot ultimately helps teams build better software faster with AI as a collaborative partner.

AnyAPI is a unified AI API platform built to simplify and accelerate AI adoption. It provides seamless access to hundreds of top-tier AI models through a single integration layer. Developers can use models from OpenAI, Anthropic, Google, xAI, and Mistral without changing their code structure. AnyAPI reduces complexity by standardizing requests across providers. The platform is designed for speed, offering low latency and high availability for production workloads. Developers can experiment, compare, and deploy models using an integrated AI playground. Long-context capabilities support up to hundreds of thousands of tokens for document-heavy use cases. Intelligent model switching improves response quality and performance automatically. Enterprise features include access control, usage monitoring, and overage alerts. AnyAPI works with modern development stacks and scales with growing applications. Built-in documentation and tutorials help teams onboard quickly. AnyAPI empowers startups and enterprises to build AI-powered products faster and with confidence.

Claude is a powerful AI assistant designed by Anthropic to support problem-solving, creativity, and productivity across a wide range of use cases. It helps users write, edit, analyze, and code by combining conversational AI with advanced reasoning capabilities. Claude allows users to work on documents, software, graphics, and structured data directly within the chat experience. Through features like Artifacts, users can collaborate with Claude to iteratively build and refine projects. The platform supports file uploads, image understanding, and data visualization to enhance how information is processed and presented. Claude also integrates web search results into conversations to provide timely and relevant context. Available on web, iOS, and Android, Claude fits seamlessly into modern workflows. Multiple subscription tiers offer flexibility, from free access to high-usage professional and enterprise plans. Advanced models give users greater depth, speed, and reasoning power for complex tasks. Claude is built with enterprise-grade security and privacy controls to protect sensitive information. Anthropic prioritizes transparency and responsible scaling in Claude’s development. As a result, Claude is positioned as a trusted AI assistant for both everyday tasks and mission-critical work.

Cursor is an AI coding agent platform built to help developers turn ideas into working software. The platform lets users hand off engineering tasks to AI agents while staying focused on decisions, review, and product direction. Cursor agents can explore files, search codebases, write code, run tests, process screen recordings, create demos, and summarize completed work. Cloud agents can run autonomously and in parallel, allowing teams to work on multiple tasks across repositories at the same time. Cursor also supports always-on automations that run on schedules or triggers to build, maintain, and fix software. The platform works across the editor, terminal, Slack, GitHub, CLI, cloud agents, and code review workflows. Developers can use Cursor for feature development, bug fixing, refactoring, CI investigation, deployment work, repository search, billing fixes, infrastructure tasks, and UI polish. Cursor gives teams access to frontier models from providers such as OpenAI, Anthropic, Gemini, SpaceXAI, and Cursor. Its autonomy slider supports lightweight targeted edits as well as more independent agentic development. Enterprise capabilities support secure adoption across large engineering organizations, with SOC 2 certification and tools for teams that need scale. By combining autonomous coding agents, parallel execution, multi-model support, editor integration, terminal workflows, Slack collaboration, GitHub review, automations, and enterprise security, Cursor helps teams build enduring software more quickly.

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.

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.

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.

Claude Opus 4.7 represents a major step forward in AI model development, focusing on advanced reasoning, coding, and enterprise-level task execution. It improves significantly over Opus 4.6 by delivering stronger performance on complex and high-effort software engineering challenges. The model is particularly effective at managing long-running processes, maintaining consistency, and producing reliable outputs over time. Its enhanced instruction-following capabilities ensure that it interprets prompts more literally and executes tasks with greater precision. Opus 4.7 also features advanced self-checking mechanisms, enabling it to validate its own responses before completion. A major highlight is its improved multimodal support, allowing it to process high-resolution images and extract fine visual details. This capability is especially useful for tasks like analyzing technical screenshots, interpreting diagrams, and supporting computer-based workflows. The model produces high-quality professional outputs, including refined documents, presentations, and UI designs that meet business standards. It also demonstrates strong performance across industries such as finance, legal services, and data analysis. Enhanced memory capabilities allow it to retain important context across sessions, making it more efficient for ongoing projects. Opus 4.7 includes safety and alignment improvements, with systems in place to detect and block potentially harmful or restricted use cases. It introduces new controls for balancing reasoning depth and response speed, giving users flexibility based on task complexity. Widely accessible through APIs and major cloud platforms, Opus 4.7 is designed to support scalable, high-performance AI applications for modern enterprises.

Muse Glimmer is a cutting-edge model boasting 30 billion parameters, crafted by Meta Superintelligence Labs, specifically optimized for seamless local agent functionality. Its streamlined architecture enables operation on standard Mac or PC systems with a single consumer GPU, making it suitable for a range of applications, including local agent management, programming tasks, function invocation, and evaluations within LLM-as-a-judge scenarios, all without needing cloud services or an internet connection. This groundbreaking model features sophisticated abilities like long-horizon execution, precise tool invocation, multimodal understanding, expanded memory for contextual awareness, and proficient instruction adherence. It excels in performing comprehensive tasks as an agent, adeptly navigates complex multi-step reasoning across extensive workflows, and can recover effectively from unexpected tool interactions. Additionally, it interprets interleaved text and images through a specialized perception encoder tailored for analyzing screenshots, graphs, and various document types. Beyond its primary functions, Muse Glimmer is designed to work harmoniously with OpenClaw and other orchestration frameworks, allowing for customizable reasoning capabilities and has been trained on a rich dataset that spans over 100 languages. The adaptability of this model not only enhances its effectiveness across different fields but also positions it as a significant asset in the evolving landscape of AI applications. Its innovative features and user-friendly deployment make it a versatile choice for professionals seeking to leverage AI for complex problem-solving.

Muse Spark 1.3 showcases a sophisticated AI model that significantly enhances its abilities for both agentic and programming tasks, thereby increasing its intelligence and practical utility for daily use. It is particularly adept at sustaining concentration on lengthy projects through active user collaboration, all while skillfully orchestrating multiple workflows within a cohesive thread. When confronted with an open-ended objective, the model efficiently harnesses tools to derive context from chaotic or conflicting data, addresses strategy gaps, monitors its learning trajectory, and ultimately produces a polished final outcome. In instances where prompts are vague, it takes the initiative to request clarification, seeks help when obstacles arise, and verifies its next steps before making critical decisions. The model exhibits exceptional dependability in adhering to complex, lengthy instructions, ensuring that intricate requirements are consistently honored throughout multifaceted tasks without overlooking essential constraints or deviating from the intended workflow. Furthermore, its advanced multitasking abilities allow it to effectively match incoming requests to the relevant tasks, even when users make interjections or alter the focus of prior inquiries, resulting in a fluid user experience. Consequently, Muse Spark 1.3 stands out as a highly adaptable tool suitable for diverse applications, making it a valuable asset across various fields.

OpenCheese AI functions as a unified AI gateway and API aimed at developers who want to connect a variety of applications and coding tools to different AI models through a single service interface. It ensures compatibility with OpenAI endpoints and provides a messaging interface that aligns with Anthropic, including features such as model selection, API key management, usage monitoring, and access to AI models from leading providers like OpenAI, Anthropic, Google, and DeepSeek. By allowing developers to configure supported coding clients and applications to channel requests through OpenCheese, the platform removes the hassle of managing distinct provider credentials for each connection. Additionally, it offers both credit-based and time-limited plans with included balances, where the availability of models, pricing structures, and free options can differ based on the selected model and plan. This service is particularly beneficial for developers working on evaluating or building workflows that involve text, code, and image generation with hosted AI models. Moreover, OpenCheese AI simplifies the integration process, enabling developers to prioritize innovation without getting bogged down by the complexities associated with juggling multiple AI service providers. Ultimately, this platform empowers developers to streamline their projects more effectively and efficiently.

The source code for Microsoft's Visual Studio Code (VSCode) is released as open source under the MIT license, but the version available for download operates under a different license that does not qualify as free and open-source software (FLOSS) and includes telemetry functionalities. In response to this, the VSCodium project was established, enabling users to bypass the complexities of building from source by providing ready-to-use binaries. This initiative employs unique build scripts that retrieve Microsoft's VSCode repository, perform the required build operations, and then upload the resulting binaries to GitHub releases, ensuring that they are licensed under the MIT license and come without telemetry features. For Mac OS X Mojave users, if you see the warning “App can’t be opened because Apple cannot check it for malicious software” when attempting to open VSCodium for the first time, you can simply right-click the application and choose Open, a step that is only necessary during your initial launch on that particular operating system. Furthermore, extensive documentation is accessible for those wishing to switch from Visual Studio Code, along with resources to help troubleshoot any issues that may come up while using VSCodium. This comprehensive support guarantees that users are equipped with all the necessary information for a seamless and enjoyable experience. Ultimately, this effort emphasizes the importance of user choice and transparency in software usage.

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 2.5 Pro is an advanced AI model specifically designed to address complex tasks, exhibiting exceptional abilities in reasoning and coding. It excels in multiple benchmarks, particularly in areas like mathematics, science, and programming, where it shows impressive effectiveness in tasks such as web app development and code transformation. This model, an evolution of the Gemini 2.5 framework, features a substantial context window of 1 million tokens, enabling it to handle large datasets from various sources, including text, images, and code libraries efficiently. Now available via Google AI Studio, Gemini 2.5 Pro is optimized for more sophisticated applications, providing expert users with enhanced tools for tackling intricate problems. Additionally, its development signifies a dedication to expanding the horizons of AI's capabilities in practical applications, ensuring it meets the demands of contemporary challenges. As AI continues to evolve, the introduction of such models represents a significant leap forward in harnessing technology for innovative solutions.

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.

Muse Wonder Full is a large-scale artificial intelligence model developed by Meta for advanced agentic reasoning, professional knowledge work, and complex software engineering tasks. Introduced in October 2026, it represents the larger variant of the Muse Wonder family and builds on Meta's research into scaling pretrained models and reinforcement learning. The model is designed to manage complex objectives involving multiple steps, changing requirements, external information, and sustained problem-solving. 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 long-running assignments, explore alternative strategies, recover from obstacles, and adapt to new information. The system can request clarification when instructions are incomplete and seek user confirmation before performing important actions. Muse Wonder Full supports professional applications involving legal documents, financial analysis, health-related information, and business process automation. Its coding capabilities include generating software, analyzing existing repositories, tracking implementation requirements, modifying source code, and performing iterative testing and debugging. Meta trained the model on diverse long-running software engineering tasks to support development projects requiring multiple rounds of implementation and revision. Evaluations include MemoryBench for contextual memory, FocusBench for sustained task execution, SWE-Marathon and DeepSWE for software engineering, and additional assessments covering professional agents and unfamiliar problem-solving tasks. Muse Wonder Full is available to Muse Code subscribers, with Meta also reporting safety evaluations addressing personal information handling, adversarial instructions, unauthorized code execution, and misuse prevention.