List of the Top 13 AI Coding Models for Nebius Token Factory in 2026

Mistral AI is recognized as a pioneering startup in the field of artificial intelligence, with a particular emphasis on open-source generative technologies. The company offers a wide range of customizable, enterprise-grade AI solutions that can be deployed across multiple environments, including on-premises, cloud, edge, and individual devices. Notable among their offerings are "Le Chat," a multilingual AI assistant designed to enhance productivity in both personal and business contexts, and "La Plateforme," a resource for developers that streamlines the creation and implementation of AI-powered applications. Mistral AI's unwavering dedication to transparency and innovative practices has enabled it to carve out a significant niche as an independent AI laboratory, where it plays an active role in the evolution of open-source AI while also influencing relevant policy conversations. By championing the development of an open AI ecosystem, Mistral AI not only contributes to technological advancements but also positions itself as a leading voice within the industry, shaping the future of artificial intelligence. This commitment to fostering collaboration and openness within the AI community further solidifies its reputation as a forward-thinking organization.

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.

Qwen is an advanced AI assistant and development platform powered by Alibaba Cloud’s cutting-edge Qwen model family, offering powerful multimodal reasoning and creativity tools for users at all skill levels. It provides a free and accessible interface through Qwen Chat, where anyone can generate images, analyze content, perform deep multi-step research, and build fully coded web pages simply by describing what they want. Using its VLo model, Qwen transforms ideas into detailed visuals and supports editing, style transfer, and complex multi-element image creation. Deep Research acts like an automated research partner, gathering information online, synthesizing insights, and generating structured reports in minutes. The Web Dev feature empowers users to create modern, ready-to-deploy websites with clean code using only natural language instructions. Qwen’s enhanced “Thinking” capabilities provide stronger logic, structured problem-solving, and real-time internet-aware analysis. Its Search tool retrieves precise results with contextual understanding, while multimodal intelligence enables Qwen to process images, audio, video, and text together for deeper comprehension. For developers, the Qwen API offers OpenAI-compatible endpoints, allowing seamless integration of Qwen’s reasoning, generation, and multimodal abilities into any application or product. This makes Qwen not only an AI assistant but also a versatile platform for builders and engineers. Across web, desktop, and mobile environments, Qwen delivers a unified, high-performance AI experience.

DeepSeek-R1 represents a state-of-the-art open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible through web, app, and API platforms, it demonstrates exceptional skills in intricate tasks such as mathematics and programming, achieving notable success on exams like the American Invitational Mathematics Examination (AIME) and MATH. This model employs a mixture of experts (MoE) architecture, featuring an astonishing 671 billion parameters, of which 37 billion are activated for every token, enabling both efficient and accurate reasoning capabilities. As part of DeepSeek's commitment to advancing artificial general intelligence (AGI), this model highlights the significance of open-source innovation in the realm of AI. Additionally, its sophisticated features have the potential to transform our methodologies in tackling complex challenges across a variety of fields, paving the way for novel solutions and advancements. The influence of DeepSeek-R1 may lead to a new era in how we understand and utilize AI for problem-solving.

We are excited to unveil Mistral NeMo, our latest and most sophisticated small model, boasting an impressive 12 billion parameters and a vast context length of 128,000 tokens, all available under the Apache 2.0 license. In collaboration with NVIDIA, Mistral NeMo stands out in its category for its exceptional reasoning capabilities, extensive world knowledge, and coding skills. Its architecture adheres to established industry standards, ensuring it is user-friendly and serves as a smooth transition for those currently using Mistral 7B. To encourage adoption by researchers and businesses alike, we are providing both pre-trained base models and instruction-tuned checkpoints, all under the Apache license. A remarkable feature of Mistral NeMo is its quantization awareness, which enables FP8 inference while maintaining high performance levels. Additionally, the model is well-suited for a range of global applications, showcasing its ability in function calling and offering a significant context window. When benchmarked against Mistral 7B, Mistral NeMo demonstrates a marked improvement in comprehending and executing intricate instructions, highlighting its advanced reasoning abilities and capacity to handle complex multi-turn dialogues. Furthermore, its design not only enhances its performance but also positions it as a formidable option for multi-lingual tasks, ensuring it meets the diverse needs of various use cases while paving the way for future innovations.

Kimi K2 showcases a groundbreaking series of open-source large language models that employ a mixture-of-experts (MoE) architecture, featuring an impressive total of 1 trillion parameters, with 32 billion parameters activated specifically for enhanced task performance. With the Muon optimizer at its core, this model has been trained on an extensive dataset exceeding 15.5 trillion tokens, and its capabilities are further amplified by MuonClip’s attention-logit clamping mechanism, enabling outstanding performance in advanced knowledge comprehension, logical reasoning, mathematics, programming, and various agentic tasks. Moonshot AI offers two unique configurations: Kimi-K2-Base, which is tailored for research-level fine-tuning, and Kimi-K2-Instruct, designed for immediate use in chat and tool interactions, thus allowing for both customized development and the smooth integration of agentic functionalities. Comparative evaluations reveal that Kimi K2 outperforms many leading open-source models and competes strongly against top proprietary systems, particularly in coding tasks and complex analysis. Additionally, it features an impressive context length of 128 K tokens, compatibility with tool-calling APIs, and support for widely used inference engines, making it a flexible solution for a range of applications. The innovative architecture and features of Kimi K2 not only position it as a notable achievement in artificial intelligence language processing but also as a transformative tool that could redefine the landscape of how language models are utilized in various domains. This advancement indicates a promising future for AI applications, suggesting that Kimi K2 may lead the way in setting new standards for performance and versatility in the industry.

Kimi K2 Thinking is an advanced open-source reasoning model developed by Moonshot AI, specifically designed for complex, multi-step workflows where it adeptly merges chain-of-thought reasoning with the use of tools across various sequential tasks. It utilizes a state-of-the-art mixture-of-experts architecture, encompassing an impressive total of 1 trillion parameters, though only approximately 32 billion parameters are engaged during each inference, which boosts efficiency while retaining substantial capability. The model supports a context window of up to 256,000 tokens, enabling it to handle extraordinarily lengthy inputs and reasoning sequences without losing coherence. Furthermore, it incorporates native INT4 quantization, which dramatically reduces inference latency and memory usage while maintaining high performance. Tailored for agentic workflows, Kimi K2 Thinking can autonomously trigger external tools, managing sequential logic steps that typically involve around 200-300 tool calls in a single chain while ensuring consistent reasoning throughout the entire process. Its strong architecture positions it as an optimal solution for intricate reasoning challenges that demand both depth and efficiency, making it a valuable asset in various applications. Overall, Kimi K2 Thinking stands out for its ability to integrate complex reasoning and tool use seamlessly.

Kimi K2.5 is an advanced multimodal AI model engineered for high-performance reasoning, coding, and visual intelligence tasks. It natively supports both text and visual inputs, allowing applications to analyze images and videos alongside natural language prompts. The model achieves open-source state-of-the-art results across agent workflows, software engineering, and general-purpose intelligence tasks. With a massive 256K token context window, Kimi K2.5 can process large documents, extended conversations, and complex codebases in a single request. Its long-thinking capabilities enable multi-step reasoning, tool usage, and precise problem solving for advanced use cases. Kimi K2.5 integrates smoothly with existing systems thanks to full compatibility with the OpenAI API and SDKs. Developers can leverage features like streaming responses, partial mode, JSON output, and file-based Q&A. The platform supports image and video understanding with clear best practices for resolution, formats, and token usage. Flexible deployment options allow developers to choose between thinking and non-thinking modes based on performance needs. Transparent pricing and detailed token estimation tools help teams manage costs effectively. Kimi K2.5 is designed for building intelligent agents, developer tools, and multimodal applications at scale. Overall, it represents a major step forward in practical, production-ready multimodal AI.

Kimi K2.6 is a cutting-edge agentic AI model developed by Moonshot AI, designed to improve practical application, programming efficiency, and complex reasoning abilities beyond its forerunners, K2 and K2.5. Utilizing a Mixture-of-Experts framework, this model embodies the multimodal, agent-centric principles of the Kimi series, seamlessly combining language understanding, coding skills, and tool application into a unified system capable of planning and executing sophisticated workflows. It boasts advanced reasoning capabilities and superior agent planning, allowing it to break down tasks, coordinate multiple tools, and address challenges involving numerous files or steps with heightened accuracy and efficiency. Furthermore, it excels in tool-calling functions, ensuring a reliable connection with external platforms like web searches or APIs, while incorporating built-in validation systems to confirm the correctness of execution formats. Significantly, Kimi K2.6 marks a transformative advancement in the AI landscape, establishing new benchmarks for the intricacy and dependability of automated processes, and paving the way for future innovations in the field.

Qwen3, the latest large language model from the Qwen family, introduces a new level of flexibility and power for developers and researchers. With models ranging from the high-performance Qwen3-235B-A22B to the smaller Qwen3-4B, Qwen3 is engineered to excel across a variety of tasks, including coding, math, and natural language processing. The unique hybrid thinking modes allow users to switch between deep reasoning for complex tasks and fast, efficient responses for simpler ones. Additionally, Qwen3 supports 119 languages, making it ideal for global applications. The model has been trained on an unprecedented 36 trillion tokens and leverages cutting-edge reinforcement learning techniques to continually improve its capabilities. Available on multiple platforms, including Hugging Face and ModelScope, Qwen3 is an essential tool for those seeking advanced AI-powered solutions for their projects.

Qwen3-Coder is a multifaceted coding model available in different sizes, prominently showcasing the 480B-parameter Mixture-of-Experts variant with 35B active parameters, which adeptly manages 256K-token contexts that can be scaled up to 1 million tokens. It demonstrates remarkable performance comparable to Claude Sonnet 4, having been pre-trained on a staggering 7.5 trillion tokens, with 70% of that data comprising code, and it employs synthetic data fine-tuned through Qwen2.5-Coder to bolster both coding proficiency and overall effectiveness. Additionally, the model utilizes advanced post-training techniques that incorporate substantial, execution-guided reinforcement learning, enabling it to generate a wide array of test cases across 20,000 parallel environments, thus excelling in multi-turn software engineering tasks like SWE-Bench Verified without requiring test-time scaling. Beyond the model itself, the open-source Qwen Code CLI, inspired by Gemini Code, equips users to implement Qwen3-Coder within dynamic workflows by utilizing customized prompts and function calling protocols while ensuring seamless integration with Node.js, OpenAI SDKs, and environment variables. This robust ecosystem not only aids developers in enhancing their coding projects efficiently but also fosters innovation by providing tools that adapt to various programming needs. Ultimately, Qwen3-Coder stands out as a powerful resource for developers seeking to improve their software development processes.

Devstral Small 2 is a condensed, 24 billion-parameter variant of Mistral AI's groundbreaking coding-focused models, made available under the adaptable Apache 2.0 license to support both local use and API access. Alongside its more extensive sibling, Devstral 2, it offers "agentic coding" capabilities tailored for low-computational environments, featuring a substantial 256K-token context window that enables it to understand and alter entire codebases with ease. With a performance score nearing 68.0% on the widely recognized SWE-Bench Verified code-generation benchmark, Devstral Small 2 distinguishes itself within the realm of open-weight models that are much larger. Its compact structure and efficient design allow it to function effectively on a single GPU or even in CPU-only setups, making it an excellent option for developers, small teams, or hobbyists who may lack access to extensive data-center facilities. Moreover, despite being smaller, Devstral Small 2 retains critical functionalities found in its larger counterparts, such as the capability to reason through multiple files and adeptly manage dependencies, ensuring that users enjoy substantial coding support. This combination of efficiency and high performance positions it as an indispensable asset for the coding community. Additionally, its user-friendly approach ensures that both novice and experienced programmers can leverage its capabilities without significant barriers.

The NVIDIA Llama Nemotron family includes a range of advanced language models optimized for intricate reasoning tasks and a diverse set of agentic AI functions. These models excel in fields such as sophisticated scientific analysis, complex mathematics, programming, adhering to detailed instructions, and executing tool interactions. Engineered with flexibility in mind, they can be deployed across various environments, from data centers to personal computers, and they incorporate a feature that allows users to toggle reasoning capabilities, which reduces inference costs during simpler tasks. The Llama Nemotron series is tailored to address distinct deployment needs, building on the foundation of Llama models while benefiting from NVIDIA's advanced post-training methodologies. This results in a significant accuracy enhancement of up to 20% over the original models and enables inference speeds that can reach five times faster than other leading open reasoning alternatives. Such impressive efficiency not only allows for tackling more complex reasoning challenges but also enhances decision-making processes and substantially decreases operational costs for enterprises. Furthermore, the Llama Nemotron models stand as a pivotal leap forward in AI technology, making them ideal for organizations eager to incorporate state-of-the-art reasoning capabilities into their operations and strategies.