Alternatives to GPT-5.4 mini

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.

Claude Opus 5 is Anthropic’s new state-of-the-art Opus model for coding, knowledge work, automation, science, and everyday AI assistance. The model is designed to provide near-frontier intelligence at a lower cost than Claude Fable 5 while keeping the same base pricing as Opus 4.8. Claude Opus 5 performs strongly on software engineering benchmarks, business task automation, computer use, novel problem solving, visual generation, and scientific research tasks. Users can adjust effort settings to trade off intelligence, speed, and token usage depending on the task. The model is also better at verifying its work, iterating carefully, building test harnesses, debugging root causes, and solving multi-step engineering problems. Anthropic highlights improvements in life sciences, including structural biology, organic chemistry, bioinformatics, and protein-related tasks. Claude Opus 5 includes alignment and safety safeguards designed to support beneficial work while blocking higher-risk cybersecurity and biology misuse. It is available through Claude.ai, Claude Max, Claude Pro, Claude Code, Claude Cowork, and the Claude API under the model name claude-opus-5. By combining stronger reasoning, coding ability, scientific capability, configurable effort, Fast mode, and enterprise-ready deployment options, Claude Opus 5 gives users a powerful model for demanding daily work.

Gemini 3.7 Flash represents Google's most advanced model for coding and agents, exhibiting significant enhancements in software engineering, knowledge-intensive tasks, web design, and intricate business processes. It excels in debugging and resolving issues, showcasing greater accuracy in first-pass code creation and a refined ability to generate code that is ready for production. When applied to web development, this model produces more functional designs and fully-featured applications with fewer prompts, maintaining strong adherence to design principles derived from screenshots, images, or comprehensive design systems. In fields with high knowledge requirements, such as finance, law, and biosciences, it enhances reasoning capabilities, precision, and comprehension of complex documents. Additionally, Gemini 3.7 Flash demonstrates superior performance in automating real-world workflows and executing multimodal tasks, catering to a range of applications from interactive web experiences and data storytelling to robotics and the creation of dynamically generated 3D content. Overall, its versatility makes it a powerful tool for a variety of professional domains.

Gemini 3.5 Pro is Google’s expected flagship Pro model for the Gemini 3.5 generation, built for users who need advanced intelligence across reasoning, coding, multimodal analysis, and agentic execution. The model is positioned as a higher-capability option for complex work that requires stronger planning, deeper instruction following, and more reliable handling of multi-step tasks. It is expected to serve demanding use cases such as software engineering, research synthesis, data analysis, enterprise automation, AI agents, and advanced productivity workflows. Gemini 3.5 Pro will likely expand on the Gemini 3 model family’s focus on state-of-the-art reasoning, tool use, and multimodal understanding. Unlike Flash models, which prioritize speed and cost efficiency, Gemini 3.5 Pro is expected to prioritize maximum capability for more difficult and high-value tasks. Developers may use it to build coding assistants, autonomous agents, technical copilots, business analysis tools, and applications that need to process complex context. Its anticipated strengths include long-horizon task execution, advanced code generation, structured problem solving, and improved performance on workflows that require careful reasoning. Gemini 3.5 Pro is not yet broadly documented as a generally available model, so businesses should treat it as an upcoming release rather than a fully launched product. Once available, it is expected to become a strong option for teams that want Google’s most capable Gemini 3.5 model for serious AI application development.

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.

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.

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

Gemini 3.6 Flash is Google’s workhorse Flash model for developers and enterprises building production AI agents at scale. The model is designed to deliver higher quality than Gemini 3.5 Flash while improving token efficiency, latency, and overall task cost. Google says Gemini 3.6 Flash uses 17% fewer output tokens than 3.5 Flash on the Artificial Analysis Index and can show even larger efficiency gains on certain software engineering benchmarks. It is priced lower than 3.5 Flash at $1.50 per 1 million input tokens and $7.50 per 1 million output tokens. Gemini 3.6 Flash improves performance in coding, ML research, computer use, knowledge work, document parsing, chart analysis, report drafting, and data-heavy workflows. The model also supports built-in computer use through the Gemini API and Gemini Enterprise, making it more useful for agentic systems that need to operate across digital environments. Google highlights customer use cases involving financial transcript analysis, code migrations, visual workflows, and interactive design tools. The model includes enhanced Frontier Safety safeguards for CBRN and cyber offense misuse while aiming to reduce unnecessary refusals for beneficial uses. By combining efficiency, stronger reasoning, multimodal ability, computer use, and enterprise availability, Gemini 3.6 Flash gives teams a practical model for scaling AI agents in production.

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.

Gemini 3.5 Flash is Google’s high-performance multimodal AI model built to deliver frontier-level intelligence, fast execution speeds, and advanced agentic capabilities for coding, automation, and enterprise workflows. As the first release in the Gemini 3.5 series, the model is designed to help developers, businesses, and users execute complex long-horizon tasks through AI-powered reasoning, workflow orchestration, and intelligent automation. Gemini 3.5 Flash combines powerful coding performance, multimodal understanding, and real-time responsiveness while outperforming earlier Gemini models and competing frontier AI systems across several coding and reasoning benchmarks. The model is optimized for agentic workflows, allowing it to plan, execute, and manage multi-step tasks such as software development, infrastructure management, document preparation, and business process automation through the updated Antigravity harness. Gemini 3.5 Flash can also deploy collaborative subagents that work together under supervision to complete demanding workflows more efficiently and at lower operational cost. Beyond coding and automation, the platform generates richer graphics, dynamic web interfaces, interactive animations, and advanced multimodal experiences that support developers and enterprise users building AI-driven applications. Google has integrated Gemini 3.5 Flash across the Gemini app, AI Mode in Google Search, Google AI Studio, Android Studio, Gemini Enterprise Agent Platform, and enterprise AI services to expand access to advanced AI capabilities globally. The model also powers Gemini Spark, Google’s new personal AI agent designed to operate continuously and assist users with digital life management and automated task execution.

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

Muse Spark 1.1 is Meta’s upgraded multimodal reasoning model designed to support advanced agentic workflows, coding tasks, computer use, and complex tool orchestration. Developed by Meta Superintelligence Labs, it builds on Muse Spark with major gains in planning, tool use, long-context reasoning, multimodal perception, and real-world task execution. The model can work across external apps and services, native tools, MCP servers, custom skills, browsers, scripts, images, video, PDFs, and audio inputs. Muse Spark 1.1 can act as a main agent by gathering context, creating a plan, and delegating work to parallel subagents, or operate as a subagent that follows instructions and escalates when needed. Its 1 million token context window allows it to retain earlier actions, retrieve information from long workflows, and compact context while preserving critical details. The model is also trained for computer-use tasks, deciding when to automate with scripts and when to interact directly with an interface. In coding workflows, Muse Spark 1.1 can diagnose bugs, implement features, migrate large codebases, generate web applications, take screenshots, identify UI issues, and validate fixes. Its multimodal strengths include visual-to-code generation, detailed image and video captioning, grounded perception, and workflows where seeing, reasoning, and acting happen together. Available through the Meta Model API public preview and in Thinking mode inside Meta AI, Muse Spark 1.1 gives developers and users a more capable foundation for building agents, automations, coding assistants, and multimodal productivity tools.

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.

Muse Spark 1.2 is Meta’s newest coding-focused model, released alongside Muse Code as part of Meta’s AI developer platform. The model improves on Muse Spark 1.1 with stronger code generation, complex debugging, codebase understanding, and full developer workflow performance. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan changes, write code, validate results, and coordinate persistent background subagents. The model was co-trained with Muse Code so it performs well inside the agentic coding runtime and tool environment. Its training included scaled coding compute, broader training environment diversity, rejection-sampled harness trajectories, recipe optimizations, and Muse Code toolset integration. Muse Spark 1.2 is designed for long-horizon coding tasks such as whole-repository generation, large end-to-end projects, auto-research, and extended optimization work. It uses planning to sequence work, goal conditioning to stay aligned with the user’s objective, and context compaction to preserve useful knowledge over long sessions. The model also benefits from a self-improvement loop where Muse Spark 1.1 generated challenging coding environments and instruction-following templates for training. By combining coding specialization, agentic workflow support, long-horizon training, subagent compatibility, and Meta Model API availability, Muse Spark 1.2 helps developers build, debug, and optimize software more effectively.

Anthropic has introduced Claude Haiku 4.5, its newest small language model aimed at achieving near-frontier capabilities at a significantly reduced cost. This model mirrors the coding and reasoning abilities of the company's mid-tier Sonnet 4, yet operates at approximately one-third of the expense while delivering over double the processing speed. According to benchmarks highlighted by Anthropic, Haiku 4.5 either matches or surpasses the performance of Sonnet 4 in critical areas such as code generation and intricate "computer use" workflows. The model is specifically optimized for scenarios requiring real-time, low-latency performance, making it ideal for applications like chat assistants, customer support, and pair-programming. Available through the Claude API under the designation “claude-haiku-4-5,” Haiku 4.5 is designed for large-scale implementations where cost-effectiveness, responsiveness, and advanced intelligence are essential. Now accessible on Claude Code and various applications, this model's efficiency allows users to achieve greater productivity within their usage confines while still enjoying top-tier performance. Moreover, its launch marks a significant step forward in providing businesses with affordable yet high-quality AI solutions.

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

Claude Sonnet 4.6 represents a comprehensive upgrade to Anthropic’s Sonnet model line, delivering expanded capabilities across coding, reasoning, computer interaction, and professional knowledge tasks. With a beta 1M token context window, the model can process massive datasets such as full repositories, extended legal agreements, or multi-document research projects in a single request. Developers report improved reliability, better instruction adherence, and fewer hallucinations, making long working sessions smoother and more predictable. Early users preferred Sonnet 4.6 over its predecessor in the majority of tests and often selected it over Opus 4.5 for practical coding work. The model’s computer-use skills have advanced significantly, enabling it to navigate spreadsheets, complete web forms, and manage multi-tab workflows with near human-level competence in many cases. Benchmark evaluations show consistent performance gains across reasoning, coding, and long-horizon planning tasks. In competitive simulations like Vending-Bench Arena, Sonnet 4.6 demonstrated strategic capacity-building and profit optimization over time. On the developer platform, it supports adaptive and extended thinking modes, context compaction, and improved tool integration for greater efficiency. Claude’s API tools now automatically execute filtering and code-processing steps to enhance search and token optimization. Sonnet 4.6 is available across Claude.ai, Cowork, Claude Code, the API, and major cloud providers at the same starting price as Sonnet 4.5.

Claude Opus 4.6 is a state-of-the-art AI model from Anthropic, designed to deliver advanced reasoning, coding, and enterprise-level performance. It improves significantly on previous versions with better planning, debugging, and code review capabilities. The model can sustain long-running, agentic workflows and operate effectively across large codebases. One of its key features is a 1 million token context window in beta, allowing it to handle extensive documents and complex tasks. Claude Opus 4.6 excels in knowledge work, including financial analysis, research, and document creation. It also performs strongly on industry benchmarks, leading in areas like agentic coding and multidisciplinary reasoning. The model includes adaptive thinking, enabling it to adjust its reasoning depth based on task complexity. Developers can control performance using adjustable effort levels for speed, cost, and accuracy. It integrates with productivity tools such as Excel and PowerPoint for enhanced workflow automation. Overall, Claude Opus 4.6 provides a powerful and reliable AI solution for professional and enterprise use cases.

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

Leanstral is an open-source AI code agent created by Mistral AI to support formal software verification and mathematical proof development using Lean 4. The system is designed to generate code while simultaneously validating its correctness through formal proof mechanisms. Unlike many AI coding assistants that rely on general-purpose language models, Leanstral is specifically optimized for proof engineering tasks within structured repositories. The model operates using a sparse architecture with efficient active parameters, allowing it to deliver strong performance without requiring extremely large computational resources. Leanstral integrates closely with the Lean proof assistant, which acts as a strict verifier for mathematical reasoning and software specifications. Developers and researchers can use the model to build verified implementations, reducing the need for time-consuming manual debugging and validation. The project is released under the Apache 2.0 open-source license, ensuring accessibility and flexibility for customization. Leanstral also supports integration with model communication protocols, enabling compatibility with development tools and extensions. Benchmarks show that the system can compete with larger closed-source coding agents while maintaining significantly lower operational costs. By combining automated reasoning, code generation, and formal proof verification, Leanstral introduces a new approach to building trustworthy AI-assisted software systems.

GLM-5-Turbo represents a rapid iteration of Z.ai’s GLM-5 model, engineered to offer both efficient and stable performance specifically tailored for agent-driven scenarios, all while preserving robust reasoning and programming abilities. This model is fine-tuned to handle high-throughput demands, especially in complex long-chain agent tasks that necessitate a series of sequential steps, tools, and decisions executed reliably and with minimal latency. With its support for sophisticated agentic workflows, GLM-5-Turbo enhances multi-step planning, tool utilization, and task execution, delivering superior responsiveness compared to larger flagship models in the lineup. Drawing from the foundational strengths of the GLM-5 family, it maintains strong capabilities in reasoning, coding, and processing extensive contexts, but prioritizes the optimization of essential aspects like speed, efficiency, and stability within production settings. Furthermore, it is crafted to seamlessly integrate with agent frameworks such as OpenClaw, allowing it to proficiently coordinate actions, manage inputs, and carry out tasks effectively. This ensures that users benefit from a responsive and reliable tool that can adapt to various operational demands and complexities.

GLM-5 is a next-generation open-source foundation model from Z.ai designed to push the boundaries of agentic engineering and complex task execution. Compared to earlier versions, it significantly expands parameter count and training data, while introducing DeepSeek Sparse Attention to optimize inference efficiency. The model leverages a novel asynchronous reinforcement learning framework called slime, which enhances training throughput and enables more effective post-training alignment. GLM-5 delivers leading performance among open-source models in reasoning, coding, and general agent benchmarks, with strong results on SWE-bench, BrowseComp, and Vending Bench 2. Its ability to manage long-horizon simulations highlights advanced planning, resource allocation, and operational decision-making skills. Beyond benchmark performance, GLM-5 supports real-world productivity by generating fully formatted documents such as .docx, .pdf, and .xlsx files. It integrates with coding agents like Claude Code and OpenClaw, enabling cross-application automation and collaborative agent workflows. Developers can access GLM-5 via Z.ai’s API, deploy it locally with frameworks like vLLM or SGLang, or use it through an interactive GUI environment. The model is released under the MIT License, encouraging broad experimentation and adoption. Overall, GLM-5 represents a major step toward practical, work-oriented AI systems that move beyond chat into full task execution.

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

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

DeepSeek-V3.2 is a highly optimized large language model engineered to balance top-tier reasoning performance with significant computational efficiency. It builds on DeepSeek's innovations by introducing DeepSeek Sparse Attention (DSA), a custom attention algorithm that reduces complexity and excels in long-context environments. The model is trained using a sophisticated reinforcement learning approach that scales post-training compute, enabling it to perform on par with GPT-5 and match the reasoning skill of Gemini-3.0-Pro. Its Speciale variant overachieves in demanding reasoning benchmarks and does not include tool-calling capabilities, making it ideal for deep problem-solving tasks. DeepSeek-V3.2 is also trained using an agentic synthesis pipeline that creates high-quality, multi-step interactive data to improve decision-making, compliance, and tool-integration skills. It introduces a new chat template design featuring explicit thinking sections, improved tool-calling syntax, and a dedicated developer role used strictly for search-agent workflows. Users can encode messages using provided Python utilities that convert OpenAI-style chat messages into the expected DeepSeek format. Fully open-source under the MIT license, DeepSeek-V3.2 is a flexible, cutting-edge model for researchers, developers, and enterprise AI teams.

Holo3 is an advanced multimodal AI solution created by H Company, designed to control computers and perform functions within graphical user interfaces (GUIs) across various platforms, including web, desktop, and mobile. In contrast to conventional language models that primarily focus on text generation, Holo3 operates as a "computer-use" model; it analyzes system screenshots, interprets the visual elements, and executes specific actions like clicking, typing, and scrolling sequentially to accomplish actual tasks. Utilizing a Mixture-of-Experts architecture, this model adeptly manages intricate, multi-step processes while minimizing computational expenses by engaging only a fraction of its parameters for each task. Holo3 is built for effective real-world application and seamlessly integrates into business ecosystems through an agent-based platform, enabling organizations to configure, launch, and oversee automated workflows comprehensively. This innovative approach not only streamlines operations but also enhances productivity by allowing users to focus on higher-level decision-making.

Gemini 3.1 Pro represents the next evolution of Google’s Gemini model family, delivering enhanced reasoning and core intelligence for demanding tasks. Designed for situations where nuanced thinking is required, it significantly improves performance across logic-heavy and unfamiliar problem domains. Its verified 77.1% score on ARC-AGI-2 highlights its ability to solve entirely new reasoning patterns, marking a major leap over Gemini 3 Pro. Beyond benchmarks, the model translates advanced reasoning into practical use cases such as visual explanations, structured data synthesis, and creative generation. One standout capability includes generating lightweight, scalable animated SVG graphics directly from text prompts, suitable for production-ready web use. Gemini 3.1 Pro is available in preview for developers through the Gemini API, Google AI Studio, Gemini CLI, Antigravity, and Android Studio. Enterprises can access it through Gemini Enterprise Agent Platform and Gemini Enterprise environments. Consumers benefit through the Gemini app and NotebookLM, with higher usage limits for Google AI Pro and Ultra subscribers. The release aims to validate improvements while expanding into more ambitious agentic workflows before general availability. Gemini 3.1 Pro positions itself as a smarter, more capable foundation for complex, real-world problem solving across industries.

Gemini 3.1 Flash-Lite represents Google’s newest addition to the Gemini 3 family, built specifically for speed and affordability at scale. Engineered for developers managing high-frequency workloads, the model balances performance and cost efficiency without sacrificing quality. It is competitively priced at $0.25 per million input tokens and $1.50 per million output tokens, making it accessible for large production deployments. Compared to Gemini 2.5 Flash, it delivers substantially faster responses, including a 2.5x improvement in time to first token and a 45% boost in output speed. Benchmark evaluations show strong results, with an Elo score of 1432 and leading scores in reasoning and multimodal understanding tests. The model rivals or surpasses similarly tiered competitors while even outperforming some previous-generation Gemini models. A key feature is its adjustable reasoning control, enabling developers to fine-tune how much computational “thinking” is applied to each request. This flexibility makes it ideal for both lightweight tasks like translation and more complex use cases such as dashboard generation or simulation design. Early enterprise adopters have praised its ability to follow instructions accurately while handling complex inputs efficiently. Gemini 3.1 Flash-Lite is currently rolling out in preview within Google AI Studio and Vertex AI for enterprise customers.

GPT-5.3-Codex-Spark is OpenAI’s first model purpose-built for real-time coding within the Codex ecosystem. Engineered for ultra-low latency, it can generate more than 1000 tokens per second when running on Cerebras’ Wafer Scale Engine hardware. Unlike larger frontier models designed for long-running autonomous tasks, Codex-Spark specializes in rapid iteration, targeted edits, and immediate feedback loops. Developers can interrupt, redirect, and refine outputs interactively, making it ideal for collaborative coding sessions. The model features a 128k context window and is currently text-only during its research preview phase. End-to-end latency improvements—including WebSocket streaming and inference stack optimizations—reduce time-to-first-token by 50% and overall roundtrip overhead by up to 80%. Codex-Spark performs strongly on benchmarks such as SWE-Bench Pro and Terminal-Bench 2.0 while completing tasks significantly faster than its larger counterpart. It is available to ChatGPT Pro users in the Codex app, CLI, and VS Code extension with separate rate limits during preview. The model maintains OpenAI’s standard safety training and evaluation protocols. Codex-Spark represents the beginning of a dual-mode Codex future that blends real-time interaction with long-horizon reasoning capabilities.

Gemini 3.5 Flash-Lite stands out as the quickest model within Google's Gemini 3.5 lineup, specifically engineered for tasks requiring low latency and for enhancing developer workflows that demand high throughput, including agentic search, document processing, coding, and extensive data analysis. It boasts an impressive output capacity of 350 tokens per second and marks a significant enhancement over earlier Flash-Lite iterations in terms of both quality and agentic capabilities. Developers have the flexibility to adjust the model's thinking level to suit the demands of the task at hand: minimal or low thinking allows for rapid processing of large volumes, while elevated thinking levels accommodate more intricate, multi-step workflows involving subagents. Furthermore, the model is equipped with built-in computational skills, enabling it to interact effectively with various digital environments across compatible platforms. Additionally, Gemini 3.5 Flash-Lite excels in coding, comprehending long contexts, and executing real-world tasks, consistently outperforming its predecessor, Gemini 3.1 Flash-Lite, in critical assessments and even exceeding the performance of Gemini 3 Flash on multiple benchmarks related to agentic functions and software engineering. This impressive performance highlights its potential to transform how developers approach complex workflows and data-intensive tasks.

GPT-5.4 Pro is a high-performance AI model introduced by OpenAI for users who require maximum capability when solving complex problems. It builds on earlier GPT models by integrating advanced reasoning, coding, and workflow automation into a single system. The model is designed to assist professionals with demanding tasks such as data analysis, financial modeling, document generation, and software development. GPT-5.4 Pro can interact directly with computers and applications, allowing AI agents to perform multi-step workflows across different tools and environments. Its extended context window supports up to one million tokens, enabling it to analyze large amounts of information while maintaining accuracy. The model also improves deep web research and long-form reasoning tasks. Developers benefit from improved tool usage and search capabilities that help agents select and operate external tools efficiently. GPT-5.4 Pro delivers stronger coding performance and faster iteration cycles for developers working on complex software projects. It also reduces token usage compared with earlier models, improving cost efficiency and speed. Overall, GPT-5.4 Pro is designed to support advanced professional workflows and AI-powered automation at scale.

GPT-5.3 Instant represents a significant refinement of ChatGPT’s core conversational model, prioritizing smoother, more natural interactions. This update directly addresses user feedback about tone, unnecessary refusals, and overly defensive disclaimers. The model now provides more direct answers when safe to do so, minimizing conversational friction and reducing dead ends. It also demonstrates improved judgment when handling sensitive topics, offering balanced responses without moralizing preambles. When using web information, GPT-5.3 Instant better synthesizes search results with its internal knowledge, delivering concise and relevant insights instead of link-heavy summaries. Internal evaluations show meaningful reductions in hallucination rates, particularly in high-stakes domains such as medicine, law, and finance. The model is designed to feel consistent and familiar while offering noticeable capability upgrades. Writing performance has been enhanced, enabling richer storytelling and more expressive prose without sacrificing clarity. These improvements aim to make ChatGPT feel less mechanical and more intuitively helpful in everyday use. GPT-5.3 Instant is available across ChatGPT and through the API, with older versions remaining temporarily accessible before retirement.

The Nemotron 3 Nano stands out as the tiniest model within NVIDIA's Nemotron 3 lineup, specifically designed for agentic AI tasks that require robust reasoning and conversational skills while maintaining cost-effective inference. This hybrid Mamba-Transformer Mixture-of-Experts model boasts 3.2 billion active parameters, 3.6 billion when including embeddings, and a total of 31.6 billion parameters. NVIDIA asserts that this model offers greater accuracy compared to its predecessor, the Nemotron 2 Nano, all while utilizing less than half of the parameters during each forward pass, thus enhancing efficiency without compromising on performance. It is also claimed to surpass the accuracy of both GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507 across various widely-used benchmarks. With an 8K input and 16K output setting utilizing a single H200, the model achieves an inference throughput that is 3.3 times greater than that of Qwen3-30B-A3B and 2.2 times that of GPT-OSS-20B. Additionally, the Nemotron 3 Nano is capable of handling context lengths of up to 1 million tokens, further establishing its superiority over GPT-OSS-20B and Qwen3-30B-A3B-Instruct-2507. This remarkable combination of features positions it as a leading choice for advanced AI applications that demand both precision and efficiency.

GPT-5.4 nano is a compact and cost-efficient AI model designed for handling lightweight, high-frequency tasks at scale. It is optimized for operations such as classification, data extraction, ranking, and simple coding assistance. The model delivers fast response times, making it suitable for applications where low latency is critical. Compared to earlier nano models, GPT-5.4 nano offers improved performance while maintaining minimal computational cost. It supports key features such as tool usage and structured output generation, allowing it to integrate easily into automated systems. The model is often used as a subagent within larger AI workflows, handling repetitive or supporting tasks efficiently. This approach allows more complex models to focus on higher-level reasoning and decision-making. GPT-5.4 nano is particularly useful in environments that require processing large volumes of requests quickly. Its efficiency makes it ideal for cost-sensitive applications and scalable deployments. Overall, it provides a reliable and fast solution for simple AI-driven tasks.

MiniMax M2.5 is a next-generation foundation model built to power complex, economically valuable tasks with speed and cost efficiency. Trained using large-scale reinforcement learning across hundreds of thousands of real-world task environments, it excels in coding, tool use, search, and professional office workflows. In programming benchmarks such as SWE-Bench Verified and Multi-SWE-Bench, M2.5 reaches state-of-the-art levels while demonstrating improved multilingual coding performance. The model exhibits architect-level reasoning, planning system structure and feature decomposition before writing code. With throughput speeds of up to 100 tokens per second, it completes complex evaluations significantly faster than earlier versions. Reinforcement learning optimizations enable more precise search rounds and fewer reasoning steps, improving overall efficiency. M2.5 is available in two variants—standard and Lightning—offering identical capabilities with different speed configurations. Pricing is designed to be dramatically lower than competing frontier models, reducing cost barriers for large-scale agent deployment. Integrated into MiniMax Agent, the model supports advanced office skills including Word formatting, Excel financial modeling, and PowerPoint editing. By combining high performance, efficiency, and affordability, MiniMax M2.5 aims to make agent-powered productivity accessible at scale.

Mistral Small 4 is a next-generation open-source AI model created by Mistral AI to deliver powerful reasoning, coding, and multimodal capabilities within a single unified architecture. The model merges features from several specialized systems, including Magistral for advanced reasoning, Pixtral for multimodal processing, and Devstral for agentic software development tasks. It supports both text and image inputs, enabling applications such as conversational AI, document analysis, and visual data interpretation. The model is built using a mixture-of-experts design with 128 experts, allowing efficient scaling while maintaining strong performance across diverse tasks. Users can adjust the model’s reasoning behavior through a configurable parameter that toggles between lightweight responses and deeper analytical processing. Mistral Small 4 also provides a large context window that enables it to handle long conversations, detailed documents, and complex reasoning chains. Compared with earlier versions, the model offers improved performance, reduced latency, and higher throughput for real-time applications. Developers can integrate it with popular machine learning frameworks such as Transformers, vLLM, and llama.cpp. The model’s open-source Apache 2.0 license allows organizations to fine-tune and customize it for specialized use cases. By combining efficiency, flexibility, and multimodal intelligence, Mistral Small 4 provides a versatile foundation for building advanced AI-powered applications.

OrcaRouter serves as a routing system for AI models that are compatible with OpenAI, efficiently directing prompts to the appropriate models from a wide array, including OpenAI, Anthropic, Gemini, DeepSeek, Qwen, Kimi, and over 200 other leading and open-source models. Its design aims to maintain the high quality of responses while minimizing costs associated with AI inference by evaluating each prompt and directing complex reasoning tasks to premium models while assigning simpler tasks to more economical open-source options. The routing process is meticulously quality-graded, avoiding arbitrary swaps for cheaper models, and every request clearly indicates the difficulty rating, chosen model, provider, and associated costs, ensuring that routes remain transparent, accountable, and reproducible. Developers can easily switch models by updating the API base URL, while previously established SDKs, model names, and streaming functionalities remain operational. Additionally, OrcaRouter features seamless automatic failover capabilities, allowing for traffic rerouting without interruption should a provider experience downtime, thus preventing disruptions for users. It also offers comprehensive API key management that incorporates spending limits, model allowlists, rate restrictions, and budget compliance, among other functionalities, ensuring robust control over resource usage. This combination of features makes OrcaRouter an indispensable tool for optimizing AI model utilization in various applications.

MiniMax M2.7 is a powerful AI model built to drive real-world productivity across coding, search, and office-based workflows. It is trained using reinforcement learning across a wide range of real-world environments, enabling it to execute complex, multi-step tasks with precision and efficiency. The model demonstrates strong problem-solving capabilities by breaking down challenges into structured steps before generating solutions across multiple programming languages. It delivers high-speed performance with rapid token output, ensuring faster completion of demanding tasks. With optimized reasoning, it reduces token usage and execution time, making it more efficient than previous models. M2.7 also achieves state-of-the-art results in software engineering benchmarks, significantly improving response times for technical issues. Its advanced agentic capabilities allow it to work seamlessly with tools and support complex workflows with high skill accuracy. The model is designed to handle professional tasks, including multi-turn interactions and high-quality document editing. It also provides strong support for office productivity, enabling efficient handling of structured data and business tasks. With competitive pricing, it delivers high performance while remaining cost-effective. Overall, it combines speed, intelligence, and versatility to meet the needs of modern professionals and teams.

SubQ 1.1 Small is the second iteration of Subquadratic’s long-context AI model, built to help enterprises solve problems that require reasoning across entire artifacts rather than isolated chunks. The model is designed for use cases involving large code repositories, document libraries, legal agreements, financial reports, contracts, and other complex information sets. Its Subquadratic Sparse Attention architecture reduces the compute burden of traditional dense attention, making it more practical to process multi-million-token contexts. SubQ 1.1 Small achieves near-perfect performance on needle-in-a-haystack retrieval tests up to 12M tokens, despite being trained primarily at 1M tokens. It also performs strongly on RULER, GPQA Diamond, LiveCodeBench, and AutomationBench Finance, showing a balance between long-context retrieval and general reasoning ability. At 1M tokens, the model uses 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2 on a single attention layer. This efficiency makes long-context training and inference more scalable for enterprise AI applications. SubQ 1.1 Small is especially valuable for teams that need to analyze relationships across full documents, trace logic across codebases, or connect information across extensive collections. The model is intended to help organizations reduce dependence on complex retrieval workarounds and reason more directly over large-scale data.

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

Seed2.0 Mini represents the most compact version of ByteDance's Seed2.0 line of versatile multimodal agent models, crafted for efficient high-throughput inference and dense deployment, while still embodying the essential strengths found in its larger counterparts regarding multimodal understanding and instruction adherence. This Mini variant, alongside Pro and Lite siblings, is particularly fine-tuned for handling high-concurrency and batch generation tasks, proving itself ideal for scenarios where the ability to process numerous requests simultaneously is as crucial as its overall capability. In line with other models in the Seed2.0 family, it showcases notable improvements in visual reasoning and motion perception, excels at extracting structured information from intricate inputs such as text and images, and effectively carries out multi-step instructions. However, in exchange for enhanced inference speed and cost efficiency, it sacrifices some degree of raw reasoning power and output quality, ensuring that it remains a practical option for various applications. As a result, Seed2.0 Mini strikes a balance between performance and efficiency, appealing to developers seeking to optimize their systems for scalable solutions.

Seed2.0 Lite belongs to the Seed2.0 lineup from ByteDance, which encompasses versatile multimodal AI agent models engineered to tackle intricate, real-world challenges while maintaining a harmonious balance between efficiency and performance. This model boasts superior multimodal comprehension and instruction-following skills compared to its predecessors in the Seed series, allowing it to effectively interpret and analyze text, visual components, and structured data for use in production environments. Positioned as a mid-sized option within the family, Lite is fine-tuned to provide high-quality results with quick responsiveness at a reduced cost and faster inference times than the Pro version, while also enhancing the capabilities of earlier models. Consequently, it is well-suited for applications that demand consistent reasoning, extended context comprehension, and the execution of multimodal tasks without necessitating the utmost raw performance levels. Moreover, this accessibility makes Seed2.0 Lite an attractive choice for developers seeking efficiency alongside capabilities in their AI solutions.

The o1-mini from OpenAI is an innovative and budget-friendly AI model that specializes in improved reasoning capabilities, especially in STEM areas such as mathematics and programming. As a member of the o1 series, it aims to tackle intricate challenges by allocating more time to analyze and contemplate solutions. Although it is smaller in size and costs 80% less than its counterpart, the o1-preview, the o1-mini remains highly effective in both coding assignments and mathematical reasoning. This makes it an appealing choice for developers and businesses that seek efficient and reliable AI solutions. Furthermore, its affordability does not compromise its performance, allowing a wider range of users to benefit from advanced AI technologies.

GPT-4.1 mini is a streamlined version of GPT-4.1, offering the same core capabilities in coding, instruction adherence, and long-context comprehension, but with faster performance and lower costs. Ideal for developers seeking to integrate AI into real-time applications, GPT-4.1 mini maintains a 1 million token context window and is well-suited for tasks that demand low-latency responses. It is a cost-effective option for businesses that need powerful AI capabilities without the high overhead associated with larger models.

The o3-mini-high model developed by OpenAI enhances artificial intelligence reasoning capabilities by improving deep problem-solving skills in areas such as programming, mathematics, and intricate tasks. This model incorporates adaptive thinking time and allows users to select from various reasoning modes—low, medium, and high—to tailor performance to the difficulty of the task at hand. Impressively, it surpasses the o1 series by an impressive 200 Elo points on Codeforces, providing exceptional efficiency at a reduced cost while ensuring both speed and precision in its operations. As a notable member of the o3 family, this model not only expands the frontiers of AI problem-solving but also remains user-friendly, offering a complimentary tier alongside increased limits for Plus subscribers, thereby making advanced AI more widely accessible. Its innovative design positions it as a significant tool for users looking to tackle challenging problems with enhanced support and adaptability.