Top DeepSeek R1 Alternatives

EXAONE Deep is a suite of sophisticated language models developed by LG AI Research, featuring configurations of 2.4 billion, 7.8 billion, and 32 billion parameters. These models are particularly adept at tackling a range of reasoning tasks, excelling in domains like mathematics and programming evaluations. Notably, the 2.4B variant stands out among its peers of comparable size, while the 7.8B model surpasses both open-weight counterparts and the proprietary model OpenAI o1-mini. Additionally, the 32B variant competes strongly with leading open-weight models in the industry. The accompanying repository not only provides comprehensive documentation, including performance metrics and quick-start guides for utilizing EXAONE Deep models with the Transformers library, but also offers in-depth explanations of quantized EXAONE Deep weights structured in AWQ and GGUF formats. Users will also find instructions on how to operate these models locally using tools like llama.cpp and Ollama, thereby broadening their understanding of the EXAONE Deep models' potential and ensuring easier access to their powerful capabilities. This resource aims to empower users by facilitating a deeper engagement with the advanced functionalities of the models.

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.

ERNIE 4.5 Turbo by Baidu is a powerful AI model that excels in multimodal processing, offering capabilities that span text, images, audio, and video. With advanced logical reasoning, the model is designed for use in a wide range of industries, including enterprise applications, education, and creative industries. The model’s ability to reduce hallucinations and improve the accuracy of results makes it an ideal solution for businesses looking to enhance automation and streamline processes. Additionally, ERNIE 4.5 Turbo will be available as open-source by June 2025, making it more accessible for developers to integrate into their own applications and projects.

ERNIE 4.5 is an advanced conversational AI system developed by Baidu, employing the latest natural language processing (NLP) techniques to enable highly sophisticated and human-like dialogues. This platform is a key element of Baidu's ERNIE (Enhanced Representation through Knowledge Integration) series, featuring multimodal capabilities that support text, images, and voice interactions. The enhancements in ERNIE 4.5 significantly boost the AI models' ability to interpret complex contexts, resulting in more accurate and nuanced responses. This versatility makes the platform suitable for a diverse array of uses, such as customer support, virtual assistance, content creation, and corporate automation. In addition, the blend of different communication modes allows users to interact with the AI in whichever way they find most comfortable, greatly improving the overall user experience. Such advancements position ERNIE 4.5 as a leading choice for organizations seeking innovative AI solutions.

The ERNIE X1 Turbo by Baidu is a powerful AI model that excels in complex tasks like logical reasoning, text generation, and creative problem-solving. It is designed to process multimodal data, including text and images, making it ideal for a wide range of applications. What sets ERNIE X1 Turbo apart from its competitors is its remarkable performance at an accessible price—just 25% of the cost of the leading models in the market. With its real-time data-driven insights, ERNIE X1 Turbo is perfect for developers, enterprises, and researchers looking to incorporate advanced AI solutions into their workflows without high financial barriers.

ERNIE X1 is an advanced conversational AI model developed by Baidu as part of its ERNIE (Enhanced Representation through Knowledge Integration) series. This version outperforms its predecessors by significantly improving its ability to understand and generate human-like responses. By employing cutting-edge machine learning techniques, ERNIE X1 skillfully handles complex questions and broadens its functions to encompass not only text processing but also image generation and multimodal interactions. Its diverse applications in natural language processing are evident in areas such as chatbots, virtual assistants, and business automation, which contribute to remarkable improvements in accuracy, contextual understanding, and the overall quality of responses. The adaptability of ERNIE X1 positions it as a crucial asset across numerous sectors, showcasing the ongoing advancements in artificial intelligence technology. Consequently, its integration into various platforms exemplifies the transformative impact AI can have on both individual and organizational levels.

DeepCoder, a fully open-source initiative for code reasoning and generation, has been created through a collaboration between the Agentica Project and Together AI. Built on the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has been fine-tuned using distributed reinforcement learning techniques, achieving an impressive accuracy of 60.6% on LiveCodeBench, which represents an 8% improvement compared to its predecessor. This remarkable performance positions it competitively alongside proprietary models such as o3-mini (2025-01-031 Low) and o1, all while operating with a streamlined 14 billion parameters. The training process was intensive, lasting 2.5 weeks on a fleet of 32 H100 GPUs and utilizing a meticulously curated dataset comprising around 24,000 coding challenges obtained from reliable sources such as TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each coding challenge was required to include a valid solution paired with at least five unit tests to ensure robustness during the reinforcement learning phase. Additionally, DeepCoder employs innovative methods like iterative context lengthening and overlong filtering to effectively handle long-range contextual dependencies, allowing it to tackle complex coding tasks with proficiency. This distinctive approach not only enhances DeepCoder's accuracy and reliability in code generation but also positions it as a significant player in the landscape of code generation models. As a result, developers can rely on its capabilities for diverse programming challenges.

ERNIE X1.1 represents a significant advancement in Baidu’s line of reasoning models, offering major gains in accuracy and reliability. It improves factual accuracy by 34.8%, instruction following by 12.5%, and agentic capabilities by 9.6% compared to ERNIE X1. These enhancements place it above DeepSeek R1-0528 in benchmark evaluations and on par with leading frontier models such as GPT-5 and Gemini 2.5 Pro. The model leverages the foundation of ERNIE 4.5 while adding extensive mid-training and post-training optimizations, including reinforcement learning to refine reasoning depth. With a focus on reducing hallucinations, it produces more trustworthy outputs and follows user instructions with higher fidelity. Its improved agentic functions mean it can handle more complex, action-driven workflows like planning, chained reasoning, and task execution. Developers and businesses can integrate ERNIE X1.1 into their systems through ERNIE Bot, the Wenxiaoyan app, or the Qianfan MaaS platform’s API. This makes it adaptable for enterprise use cases such as customer support automation, knowledge management, and intelligent assistants. The model’s transparency and output reliability position it as a competitive alternative in the global AI landscape. By combining accuracy, usability, and advanced reasoning, ERNIE X1.1 establishes itself as a trusted solution for high-stakes applications.

DeepScaleR is an advanced language model featuring 1.5 billion parameters, developed from DeepSeek-R1-Distilled-Qwen-1.5B through a unique blend of distributed reinforcement learning and a novel technique that gradually increases its context window from 8,000 to 24,000 tokens throughout training. The model was constructed using around 40,000 carefully curated mathematical problems taken from prestigious competition datasets, such as AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. With an impressive accuracy rate of 43.1% on the AIME 2024 exam, DeepScaleR exhibits a remarkable improvement of approximately 14.3 percentage points over its base version, surpassing even the significantly larger proprietary O1-Preview model. Furthermore, its outstanding performance on various mathematical benchmarks, including MATH-500, AMC 2023, Minerva Math, and OlympiadBench, illustrates that smaller, finely-tuned models enhanced by reinforcement learning can compete with or exceed the performance of larger counterparts in complex reasoning challenges. This breakthrough highlights the promising potential of streamlined modeling techniques in advancing mathematical problem-solving capabilities, encouraging further exploration in the field. Moreover, it opens doors for developing more efficient models that can tackle increasingly challenging problems with great efficacy.

DeepSWE represents a groundbreaking advancement in open-source coding agents, harnessing the Qwen3-32B foundation model trained exclusively through reinforcement learning (RL) without the aid of supervised fine-tuning or proprietary model distillation. Developed using rLLM, which is Agentica's open-source RL framework tailored for language-driven agents, DeepSWE functions effectively within a simulated development environment provided by the R2E-Gym framework. This setup equips it with a range of tools, such as a file editor, search functions, shell execution, and submission capabilities, allowing the agent to adeptly navigate extensive codebases, modify multiple files, compile code, execute tests, and iteratively generate patches or fulfill intricate engineering tasks. In addition to mere code generation, DeepSWE exhibits sophisticated emergent behaviors; when confronted with bugs or feature requests, it engages in critical reasoning regarding edge cases, searches for existing tests in the codebase, proposes patches, creates additional tests to avert regressions, and adapts its cognitive strategies based on the specific challenges presented. This remarkable adaptability and efficiency position DeepSWE as a formidable asset in the software development landscape, empowering developers to tackle complex projects with greater ease and confidence. Its ability to learn from each interaction further enhances its performance, ensuring continuous improvement over time.

DeepSeek-Coder-V2 represents an innovative open-source model specifically designed to excel in programming and mathematical reasoning challenges. With its advanced Mixture-of-Experts (MoE) architecture, it features an impressive total of 236 billion parameters, activating 21 billion per token, which greatly enhances its processing efficiency and overall effectiveness. The model has been trained on an extensive dataset containing 6 trillion tokens, significantly boosting its capabilities in both coding generation and solving mathematical problems. Supporting more than 300 programming languages, DeepSeek-Coder-V2 has emerged as a leader in performance across various benchmarks, consistently surpassing other models in the field. It is available in multiple variants, including DeepSeek-Coder-V2-Instruct, tailored for tasks based on instructions, and DeepSeek-Coder-V2-Base, which serves well for general text generation purposes. Moreover, lightweight options like DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct are specifically designed for environments that demand reduced computational resources. This range of offerings allows developers to choose the model that best fits their unique requirements, ultimately establishing DeepSeek-Coder-V2 as a highly adaptable tool in the ever-evolving programming ecosystem. As technology advances, its role in streamlining coding processes is likely to become even more significant.

DeepSeek Coder represents a groundbreaking advancement in the field of data analysis and programming. Utilizing cutting-edge machine learning and natural language processing, it empowers users to seamlessly integrate data querying, analysis, and visualization into their everyday workflows. With an intuitive interface designed for both novices and experienced developers, it simplifies the processes of writing, testing, and optimizing code. Notable features include real-time syntax checking, intelligent code suggestions, and comprehensive debugging tools, all of which significantly boost coding efficiency. Additionally, DeepSeek Coder excels at interpreting complex data sets, allowing users to derive meaningful insights and create sophisticated data-driven applications with ease. Its robust capabilities and user-friendly design make DeepSeek Coder an indispensable tool for anyone involved in projects that rely on data. As such, it stands out as a key resource in the ever-evolving landscape of technology and analytics.

DeepSeek-V3 is a remarkable leap forward in the realm of artificial intelligence, meticulously crafted to demonstrate exceptional prowess in understanding natural language, complex reasoning, and effective decision-making. By leveraging cutting-edge neural network architectures, this model assimilates extensive datasets along with sophisticated algorithms to tackle challenging issues in numerous domains such as research, development, business analytics, and automation. With a strong emphasis on scalability and operational efficiency, DeepSeek-V3 provides developers and organizations with groundbreaking tools that can greatly accelerate advancements and yield transformative outcomes. Additionally, its adaptability ensures that it can be applied in a multitude of contexts, thereby enhancing its significance across various sectors. This innovative approach not only streamlines processes but also opens new avenues for exploration and growth in artificial intelligence applications.

DeepSeek R2 is the much-anticipated successor to the original DeepSeek R1, an AI reasoning model that garnered significant attention upon its launch in January 2025 by the Chinese startup DeepSeek. This latest iteration enhances the impressive groundwork laid by R1, which transformed the AI domain by delivering cost-effective capabilities that rival top-tier models such as OpenAI's o1. R2 is poised to deliver a notable enhancement in performance, promising rapid processing and reasoning skills that closely mimic human capabilities, especially in demanding fields like intricate coding and higher-level mathematics. By leveraging DeepSeek's advanced Mixture-of-Experts framework alongside refined training methodologies, R2 aims to exceed the benchmarks set by its predecessor while maintaining a low computational footprint. Furthermore, there is a strong expectation that this model will expand its reasoning prowess to include additional languages beyond English, potentially enhancing its applicability on a global scale. The excitement surrounding R2 underscores the continuous advancement of AI technology and its potential to impact a variety of sectors significantly, paving the way for innovations that could redefine how we interact with machines.

Cohere has introduced Command A, a cutting-edge AI model designed to maximize efficiency while utilizing minimal computational power. This innovative model not only rivals but also exceeds the performance of other top contenders like GPT-4 and DeepSeek-V3 in numerous enterprise tasks that necessitate advanced agentic abilities, all while significantly reducing computing costs. Tailored for scenarios that require quick and effective AI responses, Command A empowers organizations to tackle intricate tasks across various sectors without sacrificing performance or resource efficiency. Its sophisticated architecture enables companies to effectively leverage AI capabilities, optimizing workflows and enhancing overall productivity in the process. As businesses increasingly seek to integrate AI into their operations, Command A stands out as a transformative solution that meets the demands of modern enterprises.

DeepSeek V3.1 emerges as a groundbreaking open-weight large language model, featuring an astounding 685-billion parameters and an extensive 128,000-token context window that enables it to process lengthy documents similar to 400-page novels in a single run. This model encompasses integrated capabilities for conversation, reasoning, and code generation within a unified hybrid framework that effectively blends these varied functionalities. Additionally, V3.1 supports multiple tensor formats, allowing developers to optimize performance across different hardware configurations. Initial benchmark tests indicate impressive outcomes, with a notable score of 71.6% on the Aider coding benchmark, placing it on par with or even outperforming competitors like Claude Opus 4, all while maintaining a significantly lower cost. Launched under an open-source license on Hugging Face with minimal promotion, DeepSeek V3.1 aims to transform the availability of advanced AI solutions, potentially challenging the traditional landscape dominated by proprietary models. The model's innovative features and affordability are likely to attract a diverse array of developers eager to implement state-of-the-art AI technologies in their applications, thus fostering a new wave of creativity and efficiency in the tech industry.

Tencent's Hunyuan-TurboS is an advanced AI model designed to provide quick responses and superior functionality across various domains, encompassing knowledge retrieval, mathematical problem-solving, and creative tasks. In contrast to its predecessors that operated on a "slow thinking" paradigm, this revolutionary system significantly enhances response times, doubling the rate of word generation while reducing initial response delay by 44%. Featuring a sophisticated architecture, Hunyuan-TurboS not only boosts operational efficiency but also lowers costs associated with deployment. The model adeptly combines rapid thinking—instinctive, quick responses—with slower, analytical reasoning, facilitating accurate and prompt resolutions across diverse scenarios. Its exceptional performance is evident in numerous benchmarks, placing it in direct competition with leading AI models like GPT-4 and DeepSeek V3, thus representing a noteworthy evolution in AI technology. Consequently, Hunyuan-TurboS is set to transform the landscape of artificial intelligence applications, establishing new standards for what such systems can achieve. This evolution is likely to inspire future innovations in AI development and application.

Tencent has introduced the Hunyuan T1, a sophisticated AI model now available to users through the Tencent Yuanbao platform. This model excels in understanding multiple dimensions and potential logical relationships, making it well-suited for addressing complex problems. Users can also explore a variety of AI models on the platform, such as DeepSeek-R1 and Tencent Hunyuan Turbo. Excitement is growing for the upcoming official release of the Tencent Hunyuan T1 model, which promises to offer external API access along with enhanced services. Built on the robust foundation of Tencent's Hunyuan large language model, Yuanbao is particularly noted for its capabilities in Chinese language understanding, logical reasoning, and efficient task execution. It improves user interaction by offering AI-driven search functionalities, document summaries, and writing assistance, thereby facilitating thorough document analysis and stimulating prompt-based conversations. This diverse range of features is likely to appeal to many users searching for cutting-edge solutions, enhancing the overall user engagement on the platform. As the demand for innovative AI tools continues to rise, Yuanbao aims to position itself as a leading resource in the field.

Manus is a versatile general AI agent that seamlessly bridges the gap between concepts and actions, enabling it to perform a wide array of tasks in various professional and personal contexts. From managing data analysis and organizing travel plans to creating educational materials and offering stock market evaluations, Manus assists users in reaching their objectives while allowing them to focus on other significant responsibilities. Its functions include conducting detailed research, designing captivating presentations, and analyzing market trends, all designed to boost productivity and optimize efficiency. Additionally, Manus generates accurate, actionable insights, positioning itself as an essential tool for both professionals and everyday individuals who seek to simplify their workflows and gain deeper insights into their tasks. By fusing cutting-edge technology with an intuitive user interface, Manus serves as an invaluable ally in navigating the intricacies of contemporary life. Ultimately, its comprehensive capabilities make it a reliable partner for anyone looking to enhance their daily operations and decision-making processes.

Mistral Large 3 is a frontier-scale open AI model built on a sophisticated Mixture-of-Experts framework that unlocks 41B active parameters per step while maintaining a massive 675B total parameter capacity. This architecture lets the model deliver exceptional reasoning, multilingual mastery, and multimodal understanding at a fraction of the compute cost typically associated with models of this scale. Trained entirely from scratch on 3,000 NVIDIA H200 GPUs, it reaches competitive alignment performance with leading closed models, while achieving best-in-class results among permissively licensed alternatives. Mistral Large 3 includes base and instruction editions, supports images natively, and will soon introduce a reasoning-optimized version capable of even deeper thought chains. Its inference stack has been carefully co-designed with NVIDIA, enabling efficient low-precision execution, optimized MoE kernels, speculative decoding, and smooth long-context handling on Blackwell NVL72 systems and enterprise-grade clusters. Through collaborations with vLLM and Red Hat, developers gain an easy path to run Large 3 on single-node 8×A100 or 8×H100 environments with strong throughput and stability. The model is available across Mistral AI Studio, Amazon Bedrock, Azure Foundry, Hugging Face, Fireworks, OpenRouter, Modal, and more, ensuring turnkey access for development teams. Enterprises can go further with Mistral’s custom-training program, tailoring the model to proprietary data, regulatory workflows, or industry-specific tasks. From agentic applications to multilingual customer automation, creative workflows, edge deployment, and advanced tool-use systems, Mistral Large 3 adapts to a wide range of production scenarios. With this release, Mistral positions the 3-series as a complete family—spanning lightweight edge models to frontier-scale MoE intelligence—while remaining fully open, customizable, and performance-optimized across the stack.

Claude Sonnet 3.5 from Anthropic is a highly efficient AI model that excels in key areas like graduate-level reasoning (GPQA), undergraduate knowledge (MMLU), and coding proficiency (HumanEval). It significantly outperforms previous models in grasping nuance, humor, and following complex instructions, while producing content with a conversational and relatable tone. With a performance speed twice that of Claude Opus 3, this model is optimized for complex tasks such as orchestrating workflows and providing context-sensitive customer support. Available for free on Claude.ai and the Claude iOS app, and offering higher rate limits for Claude Pro and Team plan users, it’s also accessible through the Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI, making it both affordable and scalable for developers and businesses alike.

Magistral marks the first language model family launched by Mistral AI, focusing on enhanced reasoning abilities and available in two distinct versions: Magistral Small, which is a 24 billion parameter model with open weights under the Apache 2.0 license and can be found on Hugging Face, and Magistral Medium, a more advanced version designed for enterprise use, accessible through Mistral's API, the Le Chat platform, and several leading cloud marketplaces. Tailored for specific sectors, this model excels at transparent, multilingual reasoning across a variety of tasks, including mathematics, physics, structured calculations, programmatic logic, decision trees, and rule-based systems, producing outputs that maintain a coherent thought process in the language preferred by the user, enabling easy tracking and validation of results. The launch of this model signifies a notable shift towards compact yet highly efficient AI reasoning capabilities that are easily interpretable. Presently, Magistral Medium is available in preview on platforms such as Le Chat, the API, SageMaker, WatsonX, Azure AI, and Google Cloud Marketplace. Its architecture is specifically designed for general-purpose tasks that require prolonged cognitive engagement and enhanced precision in comparison to conventional non-reasoning language models. The arrival of Magistral is a landmark achievement that showcases the ongoing evolution towards more sophisticated reasoning in artificial intelligence applications, setting new standards for performance and usability. As more organizations explore these capabilities, the potential impact of Magistral on various industries could be profound.

Claude Opus 4.1 marks a significant iterative improvement over its earlier version, Claude Opus 4, with a focus on enhancing capabilities in coding, agentic reasoning, and data analysis while keeping deployment straightforward. This latest iteration achieves a remarkable coding accuracy of 74.5 percent on the SWE-bench Verified, alongside improved research depth and detailed tracking for agentic search operations. Additionally, GitHub has noted substantial progress in multi-file code refactoring, while Rakuten Group highlights its proficiency in pinpointing precise corrections in large codebases without introducing errors. Independent evaluations show that the performance of junior developers has seen an increase of about one standard deviation relative to Opus 4, indicating meaningful advancements that align with the trajectory of past Claude releases. Opus 4.1 is currently accessible to paid subscribers of Claude, seamlessly integrated into Claude Code, and available through the Anthropic API (model ID claude-opus-4-1-20250805), as well as through services like Amazon Bedrock and Google Cloud Vertex AI. Moreover, it can be effortlessly incorporated into existing workflows, needing only the selection of the updated model, which significantly enhances the user experience and boosts productivity. Such enhancements suggest a commitment to continuous improvement in user-centric design and operational efficiency.

Claude Sonnet 3.7, created by Anthropic, is an innovative AI model that brings a unique approach to problem-solving by balancing rapid responses with deep reflective reasoning. This hybrid capability allows users to toggle between quick, efficient answers for everyday tasks and more thoughtful, reflective responses for complex challenges. Its advanced reasoning capabilities make it ideal for tasks like coding, natural language processing, and critical thinking, where nuanced understanding is essential. The ability to pause and reflect before providing an answer helps Claude Sonnet 3.7 tackle intricate problems more effectively, offering professionals and organizations a powerful AI tool that adapts to their specific needs for both speed and accuracy.

Claude Sonnet 4 is a breakthrough AI model, refining the strengths of Claude Sonnet 3.7 and delivering impressive results across software engineering tasks, coding, and advanced reasoning. With a robust 72.7% on SWE-bench, Sonnet 4 demonstrates remarkable improvements in handling complex tasks, clearer reasoning, and more effective code optimization. The model’s ability to execute complex instructions with higher accuracy and navigate intricate codebases with fewer errors makes it indispensable for developers. Whether for app development or addressing sophisticated software engineering challenges, Sonnet 4 balances performance and efficiency, offering an optimal solution for enterprises and individual developers seeking high-quality AI assistance.

Claude Opus 4.5 represents a major leap in Anthropic’s model development, delivering breakthrough performance across coding, research, mathematics, reasoning, and agentic tasks. The model consistently surpasses competitors on SWE-bench Verified, SWE-bench Multilingual, Aider Polyglot, BrowseComp-Plus, and other cutting-edge evaluations, demonstrating mastery across multiple programming languages and multi-turn, real-world workflows. Early users were struck by its ability to handle subtle trade-offs, interpret ambiguous instructions, and produce creative solutions—such as navigating airline booking rules by reasoning through policy loopholes. Alongside capability gains, Opus 4.5 is Anthropic’s safest and most robustly aligned model, showing industry-leading resistance to strong prompt-injection attacks and lower rates of concerning behavior. Developers benefit from major upgrades to the Claude API, including effort controls that balance speed versus capability, improved context efficiency, and longer-running agentic processes with richer memory. The platform also strengthens multi-agent coordination, enabling Opus 4.5 to manage subagents for complex, multi-step research and engineering tasks. Claude Code receives new enhancements like Plan Mode improvements, parallel local and remote sessions, and better GitHub research automation. Consumer apps gain better context handling, expanded Chrome integration, and broader access to Claude for Excel. Enterprise and premium users see increased usage limits and more flexible access to Opus-level performance. Altogether, Claude Opus 4.5 showcases what the next generation of AI can accomplish—faster work, deeper reasoning, safer operation, and richer support for modern development and productivity workflows.

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.

Claude Sonnet 4.5 marks a significant milestone in Anthropic's development of artificial intelligence, designed to excel in intricate coding environments, multifaceted workflows, and demanding computational challenges while emphasizing safety and alignment. This model establishes new standards, showcasing exceptional performance on the SWE-bench Verified benchmark for software engineering and achieving remarkable results in the OSWorld benchmark for computer usage; it is particularly noteworthy for its ability to sustain focus for over 30 hours on complex, multi-step tasks. With advancements in tool management, memory, and context interpretation, Claude Sonnet 4.5 enhances its reasoning capabilities, allowing it to better understand diverse domains such as finance, law, and STEM, along with a nuanced comprehension of coding complexities. It features context editing and memory management tools that support extended conversations or collaborative efforts among multiple agents, while also facilitating code execution and file creation within Claude applications. Operating at AI Safety Level 3 (ASL-3), this model is equipped with classifiers designed to prevent interactions involving dangerous content, alongside safeguards against prompt injection, thereby enhancing overall security during use. Ultimately, Sonnet 4.5 represents a transformative advancement in intelligent automation, poised to redefine user interactions with AI technologies and broaden the horizons of what is achievable with artificial intelligence. This evolution not only streamlines complex task management but also fosters a more intuitive relationship between technology and its users.

DeepSeek-V3.2-Speciale represents the pinnacle of DeepSeek’s open-source reasoning models, engineered to deliver elite performance on complex analytical tasks. It introduces DeepSeek Sparse Attention (DSA), a highly efficient long-context attention design that reduces the computational burden while maintaining deep comprehension and logical consistency. The model is trained with an expanded reinforcement learning framework capable of leveraging massive post-training compute, enabling performance not only comparable to GPT-5 but demonstrably surpassing it in internal tests. Its reasoning capabilities have been validated through gold-winning solutions across major global competitions, including IMO 2025 and IOI 2025, with official submissions released for transparency and peer assessment. DeepSeek-V3.2-Speciale is intentionally designed without tool-calling features, focusing every parameter on pure reasoning, multi-step logic, and structured problem solving. It introduces a reworked chat template featuring explicit thought-delimited sections and a structured message format optimized for agentic-style reasoning workflows. The repository includes Python-based utilities for encoding and parsing messages, illustrating how to format prompts correctly for the model. Supporting multiple tensor types (BF16, FP32, FP8_E4M3), it is built for both research experimentation and high-performance local deployment. Users are encouraged to use temperature = 1.0 and top_p = 0.95 for best results when running the model locally. With its open MIT license and transparent development process, DeepSeek-V3.2-Speciale stands as a breakthrough option for anyone requiring industry-leading reasoning capacity in an open LLM.

We are excited to present DeepSeek-V3.2-Exp, our latest experimental model that evolves from V3.1-Terminus, incorporating the cutting-edge DeepSeek Sparse Attention (DSA) technology designed to significantly improve both training and inference speeds for longer contexts. This innovative DSA framework enables accurate sparse attention while preserving the quality of outputs, resulting in enhanced performance for long-context tasks alongside reduced computational costs. Benchmark evaluations demonstrate that V3.2-Exp delivers performance on par with V3.1-Terminus, all while benefiting from these efficiency gains. The model is fully functional across various platforms, including app, web, and API. In addition, to promote wider accessibility, we have reduced DeepSeek API pricing by more than 50% starting now. During this transition phase, users will have access to V3.1-Terminus through a temporary API endpoint until October 15, 2025. DeepSeek invites feedback on DSA from users via our dedicated feedback portal, encouraging community engagement. To further support this initiative, DeepSeek-V3.2-Exp is now available as open-source, with model weights and key technologies—including essential GPU kernels in TileLang and CUDA—published on Hugging Face, and we are eager to observe how the community will leverage this significant technological advancement. As we unveil this new chapter, we anticipate fruitful interactions and innovative applications arising from the collective contributions of our user base.