Top Gemma 4 Alternatives

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.

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.

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.

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.

Grok 4.6 is a frontier AI model from xAI focused on long-running agents, ambitious interactive work, visual projects, coding, research, and knowledge work. The model builds on Grok 4.5 and is designed to stay engaged across complex tasks that unfold over many steps. Users can apply Grok 4.6 to research unfamiliar domains, analyze information, work across codebases, generate applications, create work artifacts, and refine projects through iterative feedback. Its training included a longer supplemental run with curated model-generated data for reasoning and advanced technical concepts, high-quality engineering data, and an improved optimizer and training recipe. xAI also regenerated supervised fine-tuning trajectories across reasoning efforts, agent harnesses, STEM, software engineering, and knowledge work, then filtered problematic traces with model-based checks. Grok 4.6 was trained on agentic reinforcement learning tasks across knowledge work, general coding, kernel optimization, web development, computer-aided design, and related technical environments. The model is positioned as especially useful for turning broad product ideas into working first versions because it can structure an application, implement core interactions, and improve the result over several rounds. It also produces stronger first passes on visual and interactive projects than Grok 4.5, making it useful when teams need a substantial starting point for iteration. xAI reports that Grok 4.6 performs strongly across benchmarks such as Artificial Analysis Intelligence Index, GDPVal-AA, DeepSWE, CursorBench, FrontierCode, APEX-Agents, Terminal-Bench, APEX-SWE, AA-Briefcase, and Harvey LAB. Grok 4.6 is available in Cursor, Grok Build, the xAI API, OpenRouter, Vercel, Cloudflare, and other partner environments, with a fast variant also available at higher pricing.

Grok 4.7 is a frontier artificial intelligence model from SpaceXAI built for demanding coding, knowledge work, and long-running agent workflows. The model uses a larger base architecture than Grok 4.6 and was trained with an extended reinforcement learning process focused on more difficult and longer-duration tasks. Its training emphasizes problems that may require hours of work, making it suitable for workflows that involve planning, execution, verification, and repeated tool use. Grok 4.7 improves self-checking behavior and long-context management so it can maintain task state more effectively across complex operations. The model also natively understands the Grok Bot harness, which improves conversational performance and general knowledge capabilities. Its use cases include software engineering, terminal tasks, document and presentation creation, legal analysis, electrical engineering, clinical reasoning, and other professional knowledge work. SpaceXAI reports benchmark gains over Grok 4.6 across coding, terminal, engineering, legal, and multi-hour office-task evaluations. Grok 4.7 includes a newly developed safeguard stack designed to strengthen jailbreak resistance and improve handling of risky cybersecurity, biological, and other dual-use requests. The company states that the model is designed to maintain strong utility for legitimate cybersecurity and research tasks while refusing more dangerous requests. Grok 4.7 is available through Grok Build, Cursor, the Grok API, coding harnesses, model routers, and supported cloud platforms, with a faster serving option also available. Pricing starts at $2 per million input tokens and $6 per million output tokens, positioning the model for developers and organizations running high-volume coding and professional AI workloads.

GLM-5.2 is a powerful AI foundation model created to help developers and organizations handle advanced reasoning, coding, automation, and agent-based workflows. It is designed for complex system engineering tasks where an AI model needs to understand goals, follow multi-step instructions, and support technical execution. The model can be used for software development, code analysis, documentation support, research assistance, workflow automation, and intelligent application development. GLM-5.2 is especially valuable for long-context tasks because it can work with large amounts of information across extended prompts, files, or conversations. This makes it useful for reviewing large codebases, summarizing technical materials, generating structured outputs, and supporting detailed problem-solving. Its mixture-of-experts architecture helps deliver strong performance while using active model resources more efficiently. Development teams can use GLM-5.2 to improve productivity by reducing repetitive work and accelerating technical decision-making. Businesses can also use it to power AI assistants, internal automation tools, research platforms, and customer-facing intelligent systems. The model’s focus on agentic capabilities allows it to support workflows that require planning, reasoning, and task completion rather than basic response generation. GLM-5.2 can help organizations build smarter products while giving technical teams a more capable AI partner for demanding projects. It is a strong option for companies that want scalable AI support across engineering, research, automation, and digital transformation initiatives.

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 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.

GLM-5.3 is Z.ai’s frontier coding model built to improve complex software engineering, long-horizon agent work, and advanced technical reasoning through scaled post-training. The model uses the same base model as GLM-5.2, with performance gains coming from additional post-training environments, more diverse tasks, and expanded compute on the existing training stack. Z.ai’s stack includes IndexShare for efficient long-context processing, SAO for reinforcement learning on long-horizon tasks, and slime for large-scale asynchronous post-training. GLM-5.3 is designed to perform better on work that resembles real engineering tasks rather than short coding exercises. Its training environments include production-style workflows where the model must diagnose bottlenecks, inspect documentation, use codebases, run experiments, implement changes, and produce measurable improvements. The model improves coding performance across public and private benchmarks, including Terminal Bench 3.0, DeepSWE, Agents’ Last Exam, and Z.ai Code Bench. GLM-5.3 also improves token efficiency, producing stronger agentic coding results than GLM-5.2 while using fewer output tokens in Z.ai’s internal evaluations. The model supports three reasoning effort levels, low, high, and max, and no longer supports disabling thinking. Z.ai recommends max reasoning effort for coding tasks, while applications using disabled thinking must migrate to enabled thinking before switching to GLM-5.3. The release also reports emergent cyber capabilities, including stronger vulnerability discovery and exploitation-chain reasoning, with open-weight release planned after safety evaluation and hardening. By combining scaled post-training, long-context infrastructure, long-horizon reinforcement learning, coding-agent workflows, benchmark improvements, reasoning controls, and ZCode integration, GLM-5.3 helps developers and researchers work on demanding coding and agentic tasks.

Grok 4.5 is an advanced AI model from SpaceXAI built for coding, agentic tasks, engineering workflows, and knowledge work. It is presented as SpaceXAI’s strongest model to date and is designed to perform well on real-world software engineering tasks rather than only short benchmark prompts. The model was trained on datasets spanning coding, science, engineering, and math, with heavy investment in data filtering, deduplication, quality scoring, and domain-focused selection. Its reinforcement learning process focuses on multi-step software engineering, technical problem solving, automated grading, model-based evaluation, and long-running agentic rollouts. Grok 4.5 can work on challenging development tasks across languages and environments, including Rust, C/C++, terminal workflows, debugging, bug fixing, and end-to-end app generation. The model is also capable of building polished applications from a single prompt, such as interactive simulations, modern interfaces, and functional web experiences. In addition to coding, Grok 4.5 supports knowledge work inside Grok Build, including Excel model creation, web research, multi-sheet formulas, PowerPoint slide design, native diagram creation, and Word document drafting. It is designed for speed and efficiency, with fast serving, strong token efficiency, and pricing based on input and output token usage. Developers can access Grok 4.5 through the SpaceXAI API console, Cursor, and Grok Build, making it usable across coding tools, productivity environments, and custom applications. The model is positioned for teams that need intelligent technical execution at a lower cost and with fewer steps than some competing frontier models. By combining engineering-focused training, agentic reasoning, fast inference, office productivity skills, and broad developer access, Grok 4.5 gives users a capable model for building, automating, debugging, researching, and shipping complex work.

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.

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

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

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

SWE-2 is Cognition’s coding model for software engineering agents, developed to improve the balance between capability, reasoning cost, and execution efficiency. The model is post-trained from Kimi K3, a multi-trillion-parameter model that had already received extensive reinforcement learning for agentic coding. Cognition further trained SWE-2 with a reinforcement learning algorithm that optimizes several reasoning-effort levels during a single training run. These effort levels let users trade off speed and cost against deeper planning, codebase exploration, and verification for more difficult assignments. SWE-2 is designed to reduce the over-exploration seen in earlier models by identifying relevant files and implementation paths more quickly. Its software engineering abilities include repository analysis, code writing and editing, debugging, testing, build and lint workflows, terminal tasks, and verification of completed work. The model places additional emphasis on writing end-to-end tests, catching edge cases and regressions, and gathering evidence instead of simply accepting assumptions in a prompt. Cognition’s training approach also uses cost penalties tied to the model’s performance frontier, length-weighted reward baselines, speculative decoding improvements, low-precision inference techniques, and expanded reinforcement learning data. Training data includes more diverse repositories, additional instruction-following requirements, and iterative verifier improvements designed to reduce reward hacking and false validation. SWE-2 is benchmarked against models such as GPT-6 Astra, GPT-5.6 Sol, Fable 5.1, Grok 4.6, and Kimi K3, with Cognition positioning it around strong coding performance at substantially lower cost. SWE-2 is intended for use across Cognition’s Devin ecosystem, including Desktop and CLI, with rollout to Devin Web and Fusion.

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.

DeepSeek-V4-Pro is a next-generation Mixture-of-Experts language model designed to deliver high performance across reasoning, coding, and long-context AI tasks. It features a massive architecture with 1.6 trillion total parameters and 49 billion activated parameters, enabling efficient computation while maintaining strong capabilities. The model supports an industry-leading context window of up to one million tokens, allowing it to process extremely large datasets, documents, and workflows. Its hybrid attention mechanism combines advanced techniques to optimize long-context efficiency and reduce computational requirements. DeepSeek-V4-Pro is trained on over 32 trillion tokens, enhancing its knowledge base and reasoning abilities. It incorporates advanced optimization methods to improve training stability and convergence. The model supports multiple reasoning modes, including fast responses and deep analytical thinking for complex problem solving. It performs strongly across benchmarks in coding, mathematics, and knowledge-based tasks. The architecture is designed for agentic workflows, enabling it to handle multi-step tasks and tool-based interactions. As an open-source model, it offers flexibility for customization and deployment across various environments. It also supports efficient memory usage and reduced inference costs compared to previous versions. The model’s capabilities make it suitable for both research and enterprise applications. Overall, DeepSeek-V4-Pro represents a significant advancement in scalable, high-performance AI with long-context intelligence.

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

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.

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

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

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.

The Nemotron 3 Nano, a compact yet robust language model from NVIDIA's Nemotron 3 lineup, is specifically designed to excel in agentic reasoning, engaging dialogue, and programming tasks. Its cutting-edge Mixture-of-Experts Mamba-Transformer architecture selectively activates a specific subset of parameters for each token, allowing for quick inference times while maintaining high accuracy and reasoning skills. With an impressive total of around 31.6 billion parameters, including about 3.2 billion active ones (or 3.6 billion when including embeddings), this model outperforms its predecessor, the Nemotron 2 Nano, while demanding less computational power for every forward pass. It boasts the capability to handle long-context processing of up to one million tokens, enabling it to efficiently analyze lengthy documents, navigate complex workflows, and carry out detailed reasoning tasks in one go. Additionally, it is designed for high-throughput, real-time performance, making it particularly skilled in managing multi-turn dialogues, executing tool invocations, and handling agent-driven workflows that require sophisticated planning and reasoning. This adaptability renders the Nemotron 3 Nano a top-tier option for a wide range of applications that necessitate advanced cognitive functions and seamless interaction. Its ability to integrate these features sets a new standard in the landscape of language models.

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

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.

Aya Expanse is transforming the realm of multilingual AI by acting as a research model proficient in 101 languages, leveraging advanced instruction tuning and cross-lingual transfer techniques. Its innovative methodology combines a meticulously curated open source dataset with streamlined pretraining procedures, thereby achieving outstanding performance for languages with varying resource levels. This groundbreaking development not only boosts effectiveness but also reduces infrastructure costs by as much as 30%, setting a new benchmark for scalable and inclusive language modeling within the sector. Consequently, Aya Expanse is ready to play a pivotal role in shaping the future landscape of AI language processing, potentially revolutionizing how technology interacts with diverse linguistic communities.

Bonsai 27B emerges as the newest flagship in the Bonsai series, representing the first-ever 27B-class model crafted for mobile device functionality. Leveraging the foundation of Qwen3.6 27B, this model significantly enhances local device capabilities with sophisticated multi-step reasoning, structured tool interactions, vision tasks, and agentic loops that ensure coherence across numerous operations. The Bonsai 27B is offered in two unique versions, with the Ternary Bonsai 27B utilizing ternary weights alongside FP16 group-wise scaling to achieve an effective weight of 1.71 bits, while maintaining a 5.9 GB footprint ideal for high-performance laptops. Alternatively, the 1-bit Bonsai 27B adopts binary weights with the same group-wise scaling approach, resulting in an effective weight of 1.125 bits and a reduced size of 3.9 GB, which aligns well with the memory limitations of devices such as the iPhone 17 Pro. Both variants operate smoothly throughout the entire language network, encompassing embeddings, attention mechanisms, MLPs, and the language model head, without the need for higher-precision solutions. Additionally, they include a compact 4-bit vision tower, which empowers on-device workflows to accurately analyze screenshots, documents, and camera inputs, thereby improving user interaction and productivity. This groundbreaking methodology illustrates Bonsai 27B's dedication to advancing the frontiers of mobile AI technology and enhancing the user experience across diverse applications.

DeepSeek-V3.2 represents one of the most advanced open-source LLMs available, delivering exceptional reasoning accuracy, long-context performance, and agent-oriented design. The model introduces DeepSeek Sparse Attention (DSA), a breakthrough attention mechanism that maintains high-quality output while significantly lowering compute requirements—particularly valuable for long-input workloads. DeepSeek-V3.2 was trained with a large-scale reinforcement learning framework capable of scaling post-training compute to the level required to rival frontier proprietary systems. Its Speciale variant surpasses GPT-5 on reasoning benchmarks and achieves performance comparable to Gemini-3.0-Pro, including gold-medal scores in the IMO and IOI 2025 competitions. The model also features a fully redesigned agentic training pipeline that synthesizes tool-use tasks and multi-step reasoning data at scale. A new chat template architecture introduces explicit thinking blocks, robust tool-interaction formatting, and a specialized developer role designed exclusively for search-powered agents. To support developers, the repository includes encoding utilities that translate OpenAI-style prompts into DeepSeek-formatted input strings and parse model output safely. DeepSeek-V3.2 supports inference using safetensors and fp8/bf16 precision, with recommendations for ideal sampling settings when deployed locally. The model is released under the MIT license, ensuring maximal openness for commercial and research applications. Together, these innovations make DeepSeek-V3.2 a powerful choice for building next-generation reasoning applications, agentic systems, research assistants, and AI infrastructures.

Command A+ stands out as Cohere's most sophisticated and swift language model thus far, designed as a powerful open-source resource for complex reasoning, engaging with various multimodal and multilingual tasks, and facilitating seamless private deployments. Its innovative sparse mixture-of-experts architecture features an impressive total of 218 billion parameters, with 25 billion actively in use, which optimizes high-performance workflows while reducing computational strain. By integrating capabilities from the entire Command series into one versatile solution, it adeptly handles text, images, reasoning, and tool usage, offering a vast 128K input context and a maximum output of 64K, all while supporting 48 different languages. The model has been carefully fine-tuned to boost reasoning skills, enhance agentic workflows, facilitate retrieval-augmented generation (RAG), and process complex multimodal documents, in addition to being compatible with vLLM and Transformers technology. In comparison to earlier models in the Command A series, this iteration significantly elevates enterprise performance across a wide range of fields, including multimodal understanding, data retrieval, extended tasks, advanced reasoning, programming, translation, and comprehensive document analysis. These advancements highlight the model's capacity to revolutionize how businesses tackle intricate language and data processing challenges, ultimately paving the way for more efficient solutions in various applications. As organizations increasingly rely on sophisticated AI tools, Command A+ represents a pivotal step forward in meeting those demands.