Alternatives to Nemotron 3.5 Bolt

Claude Sonnet 5.5 is a general-purpose AI model from Anthropic built for everyday professional tasks, agentic coding, knowledge work, and fast iteration. It is the second model in the Claude 5.5 family and is intended to complement Claude Opus 5.5 by offering lower-cost performance on more clearly defined workloads. Anthropic reports that Sonnet 5.5 generates output more than 30% faster than Sonnet 5 and usually completes tasks with fewer tokens. Pricing remains $2 per million input tokens and $10 per million output tokens, with cache reads priced at $0.20 per million tokens. On Terminal-Bench 4.0, the model scores 70.6%, compared with 10.3% for Sonnet 5, while also posting sizable improvements on FrontierCode, CursorBench, and other coding benchmarks. Its knowledge-work results are also substantially stronger, including a GDPval-AA score of 1844 and an AA-Briefcase score of 1811, both close to Claude Opus 5.5. Anthropic says early testers found the model faster, more collaborative, more concise, and particularly strong at design-oriented work such as polished interfaces and presentation decks. The model supports adjustable effort levels so users can trade off speed and cost against more extended reasoning and checking. Claude Sonnet 5.5 is available through Claude apps, Claude Code, the Claude Platform, and major cloud providers including AWS, Google Cloud, and Microsoft Azure.

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 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.

GPT-5.6 Sol is OpenAI’s flagship model in the GPT-5.6 series, built for high-end reasoning, coding, scientific analysis, cybersecurity, and agentic automation. The model is designed to handle complex tasks that require planning, iteration, tool coordination, long-horizon reasoning, and careful execution across multiple steps. GPT-5.6 Sol introduces max reasoning effort, giving the model more time to reason deeply through difficult problems. It also introduces ultra mode, which uses subagents to accelerate complex work and extend capability beyond a single-agent workflow. For coding, GPT-5.6 Sol is positioned for command-line workflows, software engineering tasks, debugging, testing, and multi-step tool use. In biology and quantitative research workflows, the model is designed to support genomics analysis and other long-context scientific tasks while using tokens more efficiently than prior models. For cybersecurity, GPT-5.6 Sol supports legitimate defensive work such as vulnerability research, code review, patch development, security education, and defensive testing. The model includes a layered safeguard stack with trained refusals, real-time cyber and biology misuse classifiers, account-level monitoring, differentiated access, human-in-the-loop review, and ongoing red-team testing. GPT-5.6 Sol helps trusted users and organizations access more powerful AI for technical work while maintaining stronger controls around misuse, sensitive requests, and high-risk activity.

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.

Claude Opus 5.5 is a frontier AI model from Anthropic built for coding, research, business analysis, computer use, and complex agentic workflows. The model is particularly suited to long-running software engineering tasks such as codebase migrations, audits, debugging, optimization, and large-scale refactoring. Anthropic also positions Opus 5.5 for professional knowledge work including financial analysis, legal workflows, research reports, spreadsheets, presentations, and business automation. Compared with Claude Opus 5, the model is designed to use fewer tokens, generate responses more quickly, and reduce typical workload costs. Its communication style has also been refined to make outputs clearer, easier to scan, and more consistent with user-defined writing requirements. Opus 5.5 includes stronger defenses against prompt injection and is less likely to take irreversible or out-of-bounds actions during autonomous tasks. Enterprise safety features include action classification, an auditable open-source sandbox, code review capabilities, preserved thinking protections, and configurable safeguards for sensitive domains. The model supports zero data retention and is available with additional verification programs for vetted life sciences and cybersecurity organizations. Claude Opus 5.5 is accessible through Anthropic’s applications and API as well as AWS, Google Cloud, and Microsoft Azure.

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.

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.

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.

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.

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.

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.

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.

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.

GLM-5.3 is Z.ai’s advanced coding and agentic reasoning model built through scaled post-training on top of the GLM-5.2 base model. The release focuses on frontier coding, long-horizon software engineering, agent tasks, cyber evaluation, and reinforcement learning at scale. GLM-5.3 improves significantly over GLM-5.2 on complex coding benchmarks, real-world engineering environments, Terminal Bench 3.0, DeepSWE, Agents’ Last Exam, and Z.ai’s internal Code Bench. The model is trained on environments that resemble real professional work, including tasks involving codebases, infrastructure, documentation, compute clusters, experiments, bottleneck diagnosis, implementation, testing, and measurable optimization. Z.ai’s post-training stack includes IndexShare for efficient long-context processing, SAO for reinforcement learning on long-horizon tasks, and slime for large-scale asynchronous training. GLM-5.3 supports three thinking effort levels, including low, high, and max, with max recommended for coding tasks. The model also demonstrates emergent cyber capabilities across vulnerability discovery and exploitation benchmarks, prompting continued safety evaluation and hardening before weights are released. GLM-5.3 can be used through the GLM Coding Plan, ZCode, Claude Code, OpenCode, and other coding agent workflows. By combining stronger coding performance, long-horizon task execution, post-training scale, cyber evaluation, reasoning effort controls, and coding-agent integrations, GLM-5.3 supports advanced developer and research workflows.

Claude Mythos 5 is a frontier AI model from Anthropic created for highly trusted users working on advanced cybersecurity, infrastructure protection, and scientific research. It is based on the same core model as Claude Fable 5, but certain safeguards are lifted for approved partners operating under restricted access programs. The model offers exceptional performance across software engineering, cybersecurity analysis, autonomous development workflows, scientific reasoning, visual understanding, and long-context tasks. In cybersecurity, Claude Mythos 5 is positioned for cyberdefenders and critical infrastructure providers who need advanced AI support for securing complex systems. In life sciences, the model has demonstrated strong capabilities in drug design, protein research, molecular biology, and genomics. Claude Mythos 5 can perform long-running research and technical workflows with minimal high-level human input. Anthropic designed the model for controlled deployment because its advanced capabilities could create misuse risks if broadly available without safeguards. Access is initially limited to Project Glasswing partners, with broader trusted access programs planned for cybersecurity and select biology researchers. Claude Mythos 5 helps approved organizations apply powerful AI to high-impact technical and scientific challenges while operating within a stricter governance model.

GPT-5.6 Luna is OpenAI’s fast, cost-efficient model in the GPT-5.6 lineup. The GPT-5.6 family includes Sol for flagship performance, Terra for balanced everyday work, and Luna for strong capability at the lowest listed price. Luna is designed for users who need scalable AI support for routine tasks, coding assistance, workflow automation, analysis, and production API use cases where speed and cost matter. According to the pasted preview text, Luna is priced below both Sol and Terra, making it the most affordable GPT-5.6 option for high-volume workloads. The model is included in GPT-5.6 benchmark previews across Terminal-Bench 2.1, GeneBench v1, ExploitBench, and ExploitGym, showing that it is part of the same technical family used for coding, biology, and cybersecurity evaluations. Luna benefits from safeguards developed across the GPT-5.6 series, including model-level refusal training, real-time cyber and biology misuse classifiers, account-level signals, differentiated access, monitoring, enforcement, and ongoing testing. These controls are designed to preserve legitimate use cases such as debugging, code review, defensive testing, security education, and productivity automation while constraining prohibited misuse. GPT-5.6 Luna is planned for broader access through ChatGPT, Codex, and the API after the limited preview period. GPT-5.6 Luna helps developers and organizations run useful AI workflows with a practical balance of affordability, responsiveness, and safety.

Kimi K3 is a large-scale AI model from Moonshot AI designed for advanced reasoning, software engineering, visual understanding, agentic workflows, and knowledge work. The model is built with 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention design created to support long-context intelligence. It also includes Attention Residuals and a native 1 million token context window, giving developers room to work with large files, repositories, documentation sets, transcripts, and enterprise knowledge bases. Kimi K3 always runs with thinking mode enabled and currently supports maximum reasoning effort by default. Developers can access the model through Moonshot’s OpenAI-compatible API using Python, cURL, and the OpenAI SDK. The API supports standard chat completions, streaming output, structured JSON Schema responses, partial continuation from a prefix, custom tool calling, required tool choice, and dynamic tool loading. Kimi K3 also supports vision inputs, including local images encoded as base64 and video files uploaded through the file API. Automatic context caching helps repeated long-prefix workflows become more efficient without requiring manual cache IDs or extra cache parameters. By combining long context, visual understanding, tool use, structured output, and advanced reasoning, Kimi K3 is built for developers creating sophisticated AI agents, coding systems, research tools, and enterprise applications.

Qwen3.8-Max is a frontier AI model from the Qwen family designed for advanced coding, coworking, research, multimodal reasoning, and long-horizon autonomous tasks. It is described as Qwen’s most capable model to date and the first Qwen-Max-class model with open weights announced for release. The model scales to 2.4 trillion parameters with 95 billion active parameters and is accessible through QwenCloud. Qwen3.8-Max is built to answer difficult questions and complete complex deliverables from start to finish. Its coding capabilities include autonomous project creation, self-testing, issue dispatch, CI validation, pull request workflows, and long-running feedback loops. The model is also designed for real-world work across legal review, UI/UX design, restaurant operations, structural engineering, rehabilitation visualization, sports analytics, and quantitative research. Its multimodal capabilities support images, documents, videos, interface reconstruction, visual production, application recreation, and visual feedback loops. QwenCloud supports industry-standard API protocols, including OpenAI-compatible chat completions and responses APIs as well as an Anthropic-compatible interface. By combining large-scale reasoning, agentic coding, multimodal intelligence, API access, long-context workflows, and open-weight availability, Qwen3.8-Max gives teams a powerful foundation for building advanced AI systems.

GPT-5.6 Terra is OpenAI’s balanced GPT-5.6 model for users who need strong performance across everyday work, development tasks, enterprise workflows, and technical analysis. The model is part of the GPT-5.6 family alongside Sol and Luna, with Terra positioned as the middle tier for capable, cost-efficient use. Terra is described as having competitive performance to GPT-5.5 while being 2x cheaper, making it useful for teams that want advanced capability without always using the flagship model. It supports coding workflows, agentic tasks, cybersecurity-related defensive work, biology workflows, knowledge work, and tool-assisted automation. In benchmark previews, Terra appears alongside Sol and Luna in evaluations for coding, biology, ExploitBench, and ExploitGym. The model benefits from the GPT-5.6 safeguard stack, which includes model-level refusals for prohibited cyber assistance, real-time cyber and biology misuse classifiers, and account-level risk review. These safeguards are designed to preserve access to legitimate work such as code review, debugging, vulnerability research, patch development, security education, and defensive testing. GPT-5.6 Terra is planned for availability through the API, Codex, and broader OpenAI products after the limited preview period. GPT-5.6 Terra helps teams get a balanced model for high-quality AI work when they need strong reasoning and automation at a lower cost than Sol.

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.

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.

Cursor has introduced Composer 2.5, a next-generation AI coding assistant built to deliver stronger reasoning, better collaboration, and improved reliability during software development tasks. The upgraded model performs better on long-running coding workflows and can manage complicated instructions with greater consistency than earlier Composer versions. Cursor expanded the training process by scaling compute resources, generating more advanced reinforcement learning environments, and refining behavioral traits that improve the developer experience. One of the key innovations in Composer 2.5 is its targeted textual feedback system, which helps the model learn from localized mistakes inside long coding trajectories instead of relying only on broad reward signals. This training method allows the AI to improve coding style, communication quality, and tool usage accuracy in a more focused way. The company also increased the amount of synthetic coding data by 25 times compared to Composer 2, giving the model exposure to more difficult and realistic programming tasks. During development, the system demonstrated sophisticated reasoning abilities by uncovering hidden implementation details and reverse-engineering deleted functionality inside synthetic environments. Composer 2.5 additionally uses advanced distributed training methods such as Sharded Muon and dual mesh HSDP to optimize large-scale model training performance. Available directly inside Cursor, the model comes in both standard and fast variants with different pricing tiers designed for developers, teams, and enterprise-scale engineering workflows.

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.

GLM-5.3-Flash is a multimodal foundation model from Z.ai built for high-efficiency reasoning, coding, agents, and visual understanding. The model contains 320 billion parameters in total but activates only 18 billion parameters during inference, helping reduce compute requirements. Its architecture combines linear attention with sparse attention so it can efficiently handle both local dependencies and relevant information spread across very long contexts. Z.ai also introduced IndexPool to reduce the memory and latency overhead associated with long-context retrieval at context lengths reaching one million tokens. The model was pretrained on a 30-trillion-token multimodal dataset that incorporates both textual and visual information. GLM-5.3-Flash is designed for software engineering tasks, autonomous workflows, frontend development, computer use, document analysis, and other professional workloads that benefit from visual reasoning. Its visual coding capabilities allow it to inspect rendered interfaces, identify layout or interaction problems, and use those observations to revise its work. Benchmark results published by Z.ai show that it improves substantially over GLM-5.2 on multiple coding and agentic tests while remaining competitive with more expensive frontier models. GLM-5.3-Flash can be accessed through Z.ai services and is also available as downloadable model weights for deployment through supported open inference frameworks.

DeepSeek-V4-Pro is an advanced Mixture-of-Experts language model built for high-performance reasoning, coding, and large-scale AI applications. With 1.6 trillion total parameters and 49 billion activated parameters, it delivers strong capabilities while maintaining computational efficiency. The model supports a massive context window of up to one million tokens, making it ideal for handling long documents and complex workflows. Its hybrid attention architecture improves efficiency by reducing computational overhead while maintaining accuracy. Trained on more than 32 trillion tokens, DeepSeek-V4-Pro demonstrates strong performance across knowledge, reasoning, and coding benchmarks. It includes advanced training techniques such as improved optimization and enhanced signal propagation for better stability. The model offers multiple reasoning modes, allowing users to choose between faster responses or deeper analytical thinking. It is designed to support agentic workflows and complex multi-step problem solving. As an open-source model, it provides flexibility for developers and organizations to customize and deploy at scale. Overall, DeepSeek-V4-Pro delivers a balance of performance, efficiency, and scalability for demanding AI applications.

Nemotron 3 Nano is a small yet powerful large language model from NVIDIA's Nemotron 3 series, specifically crafted for effective agentic reasoning, interactive dialogue, and programming assignments. Its innovative Mixture-of-Experts Mamba-Transformer framework selectively activates a limited set of parameters for each token, ensuring rapid inference times without sacrificing accuracy or reasoning capabilities. With roughly 31.6 billion parameters in total, including about 3.2 billion active ones (or 3.6 billion when factoring in embeddings), it surpasses the performance of the previous Nemotron 2 Nano model while requiring less computational effort for each forward pass. The model is equipped to manage long-context processing of up to one million tokens, which allows it to efficiently process extensive documents, complex workflows, and detailed reasoning sequences in a single cycle. Moreover, it is engineered for high-throughput, real-time performance, making it particularly adept at handling multi-turn dialogues, invoking tools, and executing agent-based workflows that involve intricate planning and reasoning tasks. This versatility positions Nemotron 3 Nano as a leading choice for applications requiring advanced cognitive capabilities.

MiniMax M3 is a frontier open-weight AI model built for coding, agentic work, multimodal understanding, and ultra-long-context tasks. The model supports up to a 1 million token context window, allowing it to work across large codebases, long documents, logs, project histories, and complex task environments. MiniMax M3 introduces MiniMax Sparse Attention, a sparse attention architecture designed to make long-context processing more efficient. The model is natively multimodal, with training that supports deeper semantic fusion across text, image, and video inputs. It is designed to support software engineering tasks, repository analysis, terminal-style work, browser-style retrieval, tool use, and autonomous workflows. MiniMax M3 has a mixture-of-experts architecture with hundreds of billions of total parameters and a smaller activated parameter count for more efficient inference. Developers can use it for AI coding assistants, workflow automation, research agents, document analysis, visual reasoning, and enterprise AI systems. Its long-context capability makes it especially useful when tasks require many files, references, instructions, or interaction histories to stay available at once. MiniMax M3 helps teams build more capable AI agents that can understand larger problems, work across multiple modalities, and execute complex tasks with stronger context awareness.

DeepSeek-V4-Flash is an optimized Mixture-of-Experts language model built for efficient large-scale AI workloads and fast inference. With 284 billion total parameters and 13 billion activated parameters, it delivers strong performance while maintaining lower computational demands compared to larger models. The model supports a massive context length of up to one million tokens, making it suitable for handling long-form content and multi-step workflows. Its hybrid attention mechanism improves efficiency by minimizing resource consumption while preserving accuracy. Trained on a dataset exceeding 32 trillion tokens, DeepSeek-V4-Flash performs well across reasoning, coding, and knowledge benchmarks. It offers flexible reasoning modes, enabling users to switch between quick responses and more detailed analytical outputs. The architecture is designed to support agentic workflows and scalable deployment environments. As an open-source model, it provides flexibility for customization and integration. Overall, DeepSeek-V4-Flash is a cost-effective and high-performance solution for modern AI applications.

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.

BEAM is an innovative accounting-centric platform designed to compute a wide range of interest and fee structures. It simplifies the allocation and management of costs related to account collections by utilizing tailored parameters for each client or portfolio. This encompasses expenses such as court fees, servicing charges, repossession costs, and additional financial elements. With its unique emphasis on accounting, BEAM allows for extensive customization to align with the specific requirements of your recovery strategy. Our team collaborates closely with yours to maximize the advantages our system offers for your operations. The implementation strategy we provide is tailored to fit the distinct needs of your organization. Each client using Beam Software receives a Dedicated Support Representative, who facilitates the onboarding or transition process to our platform. Furthermore, BEAM ensures the protection of sensitive information by offering the option to host your database either on our secure Microsoft Azure cloud or within your own private Azure cloud environment, giving you peace of mind regarding data security. This adaptability not only enhances operational efficiency but also aligns with modern compliance standards.

DeepSeek-V4.1-Flash is a highly efficient and adaptable AI model tailored for complex tasks in coding, creativity, agentic functions, and spatial reasoning. Building on its predecessor, DeepSeek-V4-Flash, this version prioritizes rapid output generation while ensuring robust performance on intricate challenges, achieving over 400 tokens per second with peak performance reaching approximately 427 tokens per second in tests. The model is equipped to handle sophisticated programming tasks, craft immersive 3D environments, develop voxel-based creations, and analyze spatially intricate scenes and simulations. Noteworthy demonstrations showcase its versatility through Minecraft-inspired worlds, traditional Chinese gardens, racing tracks, dungeon exploration, exploded camera perspectives, and other scenarios that require a blend of coding skills and spatial comprehension. Its advanced capabilities position it as an ideal choice for quick prototyping, game design, 3D modeling, architecture, academic research, and various other technical or artistic endeavors where speed in iteration is crucial. Additionally, the model's innovative features allow it to adapt to diverse project requirements, further enhancing its usefulness across multiple domains.

EchoInk is a digital learning content authoring platform designed to simplify how organizations create, collaborate on, publish, and manage interactive training and course materials. The platform lets instructional designers, subject matter experts, educators, and business teams build mobile-ready learning content without needing advanced authoring experience. Users can create courses, modules, curricula, and digital resources using drag-and-drop patterns, bespoke templates, interactive widgets, videos, and consistent design components. EchoInk supports real-time collaboration, allowing teams to manage comments, edits, requests, and review cycles across departments or regions. Its automated review capabilities help flag content issues such as design errors, structural problems, missing files, and other quality concerns before publication. The platform integrates with existing LMS and LXP systems so organizations can deliver dynamic digital content where learners already access training. EchoInk also supports data export into business intelligence tools and learning record stores for reporting on learner behaviors and outcomes. Its responsive design previews help teams ensure content looks good and works well across laptops, tablets, phones, and BYOD environments. As part of the broader Echosystem from Echo360, EchoInk helps education, corporate, and government organizations deliver modern learning content that is easier to update, scale, and measure.

Claude Fable 5.5 is an anticipated but currently unannounced model in Anthropic's Claude family, and Anthropic has not confirmed that a model with this name will be released. As of September 30, 2026, Claude Fable 5.1 remains the latest officially documented Fable model. Fable represents Anthropic's highest-end model tier for demanding reasoning and long-horizon agentic work, while the newer Opus 5.5 and Sonnet 5.5 occupy lower-cost positions in the Claude lineup. Anthropic's current documentation gives Fable 5.1 a 1-million-token context window and maximum output length of 128,000 tokens. It supports text and image inputs with text output and uses adaptive thinking that remains active throughout model operation. Fable 5.1 defaults to high reasoning effort and is listed as having a June 2026 reliable knowledge cutoff and training-data cutoff. API pricing is $10 per million input tokens and $50 per million output tokens, while prompt-cache reads cost $0.25 per million tokens and Batch API processing receives a 50% input and output discount. Anthropic's official documentation currently provides model identifiers for Fable 5.1 across the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS. No equivalent model identifier, specifications, benchmark results, pricing, availability information, or release schedule has been published for Claude Fable 5.5.

Muse Spark 1.3 represents an advanced AI model that has enhanced capabilities for both agentic and coding tasks, making it more intelligent and practical for use in everyday applications. It excels in maintaining focus on extended tasks through active collaboration with users while efficiently managing various workflows within a single, continuous thread. When faced with an open-ended goal, the model adeptly utilizes tools to create context from disorganized or contradictory information, rectify any gaps in its strategy, track its learning progress, and ultimately generate a final product. In situations where prompts lack clarity, it is proactive in seeking clarification, asking for assistance when it encounters obstacles, and confirming its actions before proceeding with significant decisions. The model demonstrates a high level of reliability in following intricate, long-form instructions, ensuring that detailed requirements are maintained throughout complex, multi-step tasks without losing critical constraints or deviating from the desired workflow. With its enhanced multitasking capabilities, it effectively aligns incoming prompts with the appropriate tasks, even in cases where users interject or shift the focus of previous requests, allowing for a seamless user experience. This makes Muse Spark 1.3 a versatile tool for a wide range of applications.

Muse Glimmer is an open-weights model featuring 30 billion parameters, developed by Meta Superintelligence Labs, and is fine-tuned for continuous local agent operations. Its compact design allows it to function on a standard Mac or PC equipped with a single consumer GPU, making it ideal for various tasks such as local agent management, function calling, programming, and LLM-as-a-judge evaluations without reliance on cloud services or internet connectivity. This innovative model integrates advanced capabilities such as long-horizon execution, accurate tool invocation, multimodal comprehension, extended memory for context, and effective instruction following. It is proficient in accomplishing end-to-end tasks as an agent, maintains the ability to engage in multi-step reasoning over lengthy processes, can recover gracefully from failed or unanticipated tool engagements, and interprets interleaved text and images using a specialized perception encoder designed for analyzing screenshots, graphs, and document files. Furthermore, Muse Glimmer is compatible with OpenClaw and other orchestration frameworks, allowing for adjustable reasoning efforts, and has been developed with a diverse dataset encompassing over 100 languages. The model's versatility ensures that it can adapt to various applications, thus enhancing its utility in different domains.

Gemma 4 is an advanced AI model developed by Google as part of its Gemini architecture, designed to deliver strong performance while remaining accessible to developers. The model is optimized to run on a single GPU or TPU, allowing more organizations and researchers to experiment with powerful AI technology. Gemma 4 improves natural language understanding and generation, making it suitable for applications such as chatbots, text analysis, and automated content creation. Its architecture enables the model to process complex language patterns while maintaining efficient computational performance. Developers can integrate Gemma 4 into various AI projects that require intelligent text processing or conversational capabilities. The model is designed with scalability in mind, allowing it to support both research experiments and production systems. By offering high-performance AI in a more accessible format, Gemma 4 lowers the barrier for developing sophisticated AI solutions. Its flexibility makes it useful for industries ranging from technology and education to business automation. Researchers can also use the model to explore new AI techniques and improve language processing systems. Overall, Gemma 4 represents a step forward in making powerful AI models easier to deploy and use.

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.

Gemini 3.8 Flash Cyber represents Google's most advanced cybersecurity model, offering top-tier performance in identifying vulnerabilities and automating patching processes with remarkable speed for rapid iteration. Tailored for trusted defenders, it is accessible via the Fairwind Program. On CyberGym, a recognized industry benchmark for detecting vulnerabilities, this model showcases exceptional autonomous vulnerability discovery, outperforming both Gemini 3.5 Flash Cyber and larger frontier models. Furthermore, Google assessed its effectiveness on an internal benchmark that spans complex codebases across 20 programming languages, achieving a success rate of over 70% in identifying various vulnerabilities. Unlike many models that focus on offensive strategies, Gemini 3.8 Flash Cyber emphasizes the importance of fixing vulnerabilities, providing defenders with advanced tools that enhance their ability to stay ahead of cyber attackers. This focus on proactive defense represents a crucial shift in the cybersecurity landscape, prioritizing the safeguarding of systems over mere exploitation capabilities.

Gemini 3.8 Flash stands out as Google's most advanced model for Flash, offering substantial enhancements compared to version 3.7 in areas such as software engineering, agent-based tasks, and intricate multi-step reasoning within specialized fields. Designed for extended coding projects and autonomous agents, it adeptly addresses complex engineering challenges in a comprehensive manner, ensuring the reliability essential for critical enterprise autonomy in specialized knowledge areas. This model excels particularly in quantitative and professional disciplines that demand sophisticated analysis and reporting, as well as in multi-step reasoning tasks spanning STEM, humanities, and professional domains. The improvements it showcases arise from a fundamental design decision: Gemini 3.8 Flash intensifies its focus on challenging tasks by conducting additional reasoning steps and utilizing tools iteratively, thus optimizing its performance. When operating at higher effort levels, it may consume more tokens to achieve superior outcomes, while developers also have the option to adjust to lower effort levels for varied results. Overall, this flexibility allows for tailored use based on project needs and desired outcomes.

Grok 4.8 is an upcoming large language model from xAI designed to continue the company’s push toward more capable coding, reasoning, knowledge work, and autonomous AI agents. Elon Musk has stated that Grok 4.8 uses approximately 2.5 trillion parameters, making it larger than the 2.1-trillion-parameter Grok 4.7 model previously discussed. The model is also being trained on a new C++ software stack that xAI expects to use for its next generation of large-scale training runs. Initial model training is expected to finish before reinforcement learning and additional post-training work begin, meaning the final production model is not yet available. Based on the current Grok generation, Grok 4.8 is likely to emphasize software engineering, agentic tool use, professional knowledge work, image understanding, and complex multi-step reasoning. Grok 4.7 currently supports configurable reasoning levels and a 500,000-token context window, providing a baseline for the capabilities xAI is developing further. Grok 4.8 may also become an important model for products such as Grok Build and Grok Bot, where stronger reasoning and tool coordination can support longer autonomous workflows. xAI has not published official Grok 4.8 benchmarks, pricing, context limits, API details, or an exact release date. Grok 4.8 is expected to serve developers, engineering teams, researchers, enterprises, and AI agent builders seeking frontier-level performance across technical and professional tasks.

Hy4 preview represents a cutting-edge open source Mixture-of-Experts flagship model tailored for a variety of real-world productivity tasks, including software engineering, office activities, game development, and scientific exploration. This model boasts a staggering total of 770 billion parameters, with 49 billion activated per token, and features an impressive 1 million-token context window, allowing it to efficiently manage large codebases, vast document collections, and complex multi-step processes. The architecture consists of 78 layers that integrate Gated DeepSeek Sparse Attention alongside IndexCache for reusing sparse indices across layers, while also employing identity Hyper-Connections to enhance the flow of information between layers. Additionally, a dedicated Multi-Token Prediction layer facilitates speculative decoding, further enhancing its capabilities. Hy4 preview is crafted to comprehend, plan, debug, and validate intricate engineering projects, while also achieving notable improvements in the quality of front-end visuals and interaction design, thereby making it an invaluable asset for professionals across various domains.

The Nemotron-3 Super is an innovative member of NVIDIA's Nemotron 3 series of open models, specifically crafted to facilitate sophisticated agentic AI systems that can effectively reason, plan, and carry out multi-step workflows in intricate environments. This model features a unique hybrid Mamba-Transformer Mixture-of-Experts architecture that merges the streamlined efficiency of Mamba layers with the contextual depth provided by transformer attention mechanisms, which allows it to adeptly manage extended sequences and intricate reasoning tasks with impressive accuracy and throughput. By activating only a portion of its parameters for each token, this architecture significantly enhances computational efficiency while preserving robust reasoning capabilities, making it ideal for scalable inference under heavy workloads. The Nemotron-3 Super comprises approximately 120 billion parameters, with around 12 billion being active during inference, which substantially boosts its ability to handle multi-step reasoning and collaborative interactions among agents within extensive contexts. Such advancements make it a powerful tool for tackling diverse challenges in AI applications.