Alternatives to DeepSeek-V2
Compare DeepSeek-V2 alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to DeepSeek-V2 in 2026. Compare features, ratings, user reviews, pricing, and more from DeepSeek-V2 competitors and alternatives in order to make an informed decision for your business.
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DeepSeek R2
DeepSeek
DeepSeek R2 is the anticipated successor to DeepSeek R1, a groundbreaking AI reasoning model launched in January 2025 by the Chinese AI startup DeepSeek. Building on R1’s success, which disrupted the AI industry with its cost-effective performance rivaling top-tier models like OpenAI’s o1, R2 promises a quantum leap in capabilities. It is expected to deliver exceptional speed and human-like reasoning, excelling in complex tasks such as advanced coding and high-level mathematical problem-solving. Leveraging DeepSeek’s innovative Mixture-of-Experts architecture and efficient training methods, R2 aims to outperform its predecessor while maintaining a low computational footprint, potentially expanding its reasoning abilities to languages beyond English.Starting Price: Free -
2
DeepSeek-V3.2
DeepSeek
DeepSeek-V3.2 is a next-generation open large language model designed for efficient reasoning, complex problem solving, and advanced agentic behavior. It introduces DeepSeek Sparse Attention (DSA), a long-context attention mechanism that dramatically reduces computation while preserving performance. The model is trained with a scalable reinforcement learning framework, allowing it to achieve results competitive with GPT-5 and even surpass it in its Speciale variant. DeepSeek-V3.2 also includes a large-scale agent task synthesis pipeline that generates structured reasoning and tool-use demonstrations for post-training. The model features an updated chat template with new tool-calling logic and the optional developer role for agent workflows. With gold-medal performance in the IMO and IOI 2025 competitions, DeepSeek-V3.2 demonstrates elite reasoning capabilities for both research and applied AI scenarios.Starting Price: Free -
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DeepSeek-V3.2-Exp
DeepSeek
Introducing DeepSeek-V3.2-Exp, our latest experimental model built on V3.1-Terminus, debuting DeepSeek Sparse Attention (DSA) for faster and more efficient inference and training on long contexts. DSA enables fine-grained sparse attention with minimal loss in output quality, boosting performance for long-context tasks while reducing compute costs. Benchmarks indicate that V3.2-Exp performs on par with V3.1-Terminus despite these efficiency gains. The model is now live across app, web, and API. Alongside this, the DeepSeek API prices have been cut by over 50% immediately to make access more affordable. For a transitional period, users can still access V3.1-Terminus via a temporary API endpoint until October 15, 2025. DeepSeek welcomes feedback on DSA via its feedback portal. In conjunction with the release, DeepSeek-V3.2-Exp has been open-sourced: the model weights and supporting technology (including key GPU kernels in TileLang and CUDA) are available on Hugging Face.Starting Price: Free -
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DeepSeek-V3.1-Terminus
DeepSeek
DeepSeek has released DeepSeek-V3.1-Terminus, which enhances the V3.1 architecture by incorporating user feedback to improve output stability, consistency, and agent performance. It notably reduces instances of mixed Chinese/English character output and unintended garbled characters, resulting in cleaner, more consistent language generation. The update upgrades both the code agent and search agent subsystems to yield stronger, more reliable performance across benchmarks. DeepSeek-V3.1-Terminus is also available as an open source model, and its weights are published on Hugging Face. The model structure remains the same as DeepSeek-V3, ensuring compatibility with existing deployment methods, with updated inference demos provided for community use. While trained at a scale of 685B parameters, the model includes FP8, BF16, and F32 tensor formats, offering flexibility across environments.Starting Price: Free -
5
DeepSeek-V3.2-Speciale
DeepSeek
DeepSeek-V3.2-Speciale is a high-compute variant of the DeepSeek-V3.2 model, created specifically for deep reasoning and advanced problem-solving tasks. It builds on DeepSeek Sparse Attention (DSA), a custom long-context attention mechanism that reduces computational overhead while preserving high performance. Through a large-scale reinforcement learning framework and extensive post-training compute, the Speciale variant surpasses GPT-5 on reasoning benchmarks and matches the capabilities of Gemini-3.0-Pro. The model achieved gold-medal performance in the International Mathematical Olympiad (IMO) 2025 and International Olympiad in Informatics (IOI) 2025. DeepSeek-V3.2-Speciale does not support tool-calling, making it purely optimized for uninterrupted reasoning and analytical accuracy. Released under the MIT license, it provides researchers and developers an open, state-of-the-art model focused entirely on high-precision reasoning.Starting Price: Free -
6
DeepSeek-V4
DeepSeek
DeepSeek-V4 is a next-generation open large language model built for efficient reasoning, complex problem solving, and advanced agentic behavior. It introduces DeepSeek Sparse Attention (DSA), a long-context attention mechanism that significantly reduces computational overhead while maintaining strong performance. The model is trained using a scalable reinforcement learning framework to achieve results competitive with leading frontier models. It also incorporates a large-scale agent task synthesis pipeline to generate structured reasoning and tool-use demonstrations during post-training. An updated chat template includes enhanced tool-calling logic and an optional developer role to support agent workflows. DeepSeek-V4 delivers elite reasoning performance across both research and applied AI use cases.Starting Price: Free -
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DeepSeek
DeepSeek
DeepSeek is a cutting-edge AI assistant powered by the advanced DeepSeek-V3 model, featuring over 600 billion parameters for exceptional performance. Designed to compete with top global AI systems, it offers fast responses and a wide range of features to make everyday tasks easier and more efficient. Available across multiple platforms, including iOS, Android, and the web, DeepSeek ensures accessibility for users everywhere. The app supports multiple languages and has been continually updated to improve functionality, add new language options, and resolve issues. With its seamless performance and versatility, DeepSeek has garnered positive feedback from users worldwide.Starting Price: Free -
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DeepSeek R1
DeepSeek
DeepSeek-R1 is an advanced open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible via web, app, and API, it excels in complex tasks such as mathematics and coding, demonstrating superior performance on benchmarks like the American Invitational Mathematics Examination (AIME) and MATH. DeepSeek-R1 employs a mixture of experts (MoE) architecture with 671 billion total parameters, activating 37 billion parameters per token, enabling efficient and accurate reasoning capabilities. This model is part of DeepSeek's commitment to advancing artificial general intelligence (AGI) through open-source innovation.Starting Price: Free -
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DeepSeek-Coder-V2
DeepSeek
DeepSeek-Coder-V2 is an open source code language model designed to excel in programming and mathematical reasoning tasks. It features a Mixture-of-Experts (MoE) architecture with 236 billion total parameters and 21 billion activated parameters per token, enabling efficient processing and high performance. The model was trained on an extensive dataset of 6 trillion tokens, enhancing its capabilities in code generation and mathematical problem-solving. DeepSeek-Coder-V2 supports over 300 programming languages and has demonstrated superior performance on benchmarks such surpassing other models. It is available in multiple variants, including DeepSeek-Coder-V2-Instruct, optimized for instruction-based tasks; DeepSeek-Coder-V2-Base, suitable for general text generation; and lightweight versions like DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct, designed for environments with limited computational resources. -
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Qwen2.5-Max
Alibaba
Qwen2.5-Max is a large-scale Mixture-of-Experts (MoE) model developed by the Qwen team, pretrained on over 20 trillion tokens and further refined through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). In evaluations, it outperforms models like DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro. Qwen2.5-Max is accessible via API through Alibaba Cloud and can be explored interactively on Qwen Chat.Starting Price: Free -
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DeepSeek-V3
DeepSeek
DeepSeek-V3 is a state-of-the-art AI model designed to deliver unparalleled performance in natural language understanding, advanced reasoning, and decision-making tasks. Leveraging next-generation neural architectures, it integrates extensive datasets and fine-tuned algorithms to tackle complex challenges across diverse domains such as research, development, business intelligence, and automation. With a focus on scalability and efficiency, DeepSeek-V3 provides developers and enterprises with cutting-edge tools to accelerate innovation and achieve transformative outcomes.Starting Price: Free -
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GLM-4.6
Zhipu AI
GLM-4.6 advances upon its predecessor with stronger reasoning, coding, and agentic capabilities: it demonstrates clear improvements in inferential performance, supports tool use during inference, and more effectively integrates into agent frameworks. In benchmark tests spanning reasoning, coding, and agents, GLM-4.6 outperforms GLM-4.5 and shows competitive strength against models such as DeepSeek-V3.2-Exp and Claude Sonnet 4, though it still trails Claude Sonnet 4.5 in pure coding performance. In real-world tests using an extended “CC-Bench” suite across front-end development, tool building, data analysis, and algorithmic tasks, GLM-4.6 beats GLM-4.5 and approaches parity with Claude Sonnet 4, winning ~48.6% of head-to-head comparisons, while also achieving ~15% better token efficiency. GLM-4.6 is available via the Z.ai API, and developers can integrate it as an LLM backend or agent core using the platform’s API.Starting Price: Free -
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Command A
Cohere AI
Command A, introduced by Cohere, is a high-performance AI model designed to maximize efficiency with minimal computational resources. This model outperforms or matches other top-tier models like GPT-4 and DeepSeek-V3 in agentic enterprise tasks while significantly reducing compute costs. It is tailored for applications requiring fast, efficient AI-driven solutions, providing businesses with the capability to perform advanced tasks across various domains, all while optimizing performance and computational demands.Starting Price: $2.50 / 1M tokens -
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Chat Stream
Chat Stream
Chat Stream provides access to two powerful language models from DeepSeek: Model Capabilities Utilizes DeepSeek V3 and R1 models with 671B parameters (37B activated per token) Achieves exceptional benchmark scores: MMLU (87.1%), BBH (87.5%) Features 128K context window length Supports code generation, mathematical computation, and multilingual processing Technical Features Advanced MoE (Mixture-of-Experts) architecture Multi-head Latent Attention (MLA) Auxiliary-loss-free load balancing Multi-token prediction objective Deployment Options Web-based chat interface with instant access One-click website integration via iframe Mobile apps for iOS and Android platforms Compatible with NVIDIA, AMD GPUs, and Huawei Ascend NPUs Supports both local inference and cloud deployment Access Methods Free chat access without registration Website embedding capabilities Mobile applications Premium subscription for ad-free experience -
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GLM-5
Zhipu AI
GLM-5 is Z.ai’s latest large language model built for complex systems engineering and long-horizon agentic tasks. It scales significantly beyond GLM-4.5, increasing total parameters and training data while integrating DeepSeek Sparse Attention to reduce deployment costs without sacrificing long-context capacity. The model combines enhanced pre-training with a new asynchronous reinforcement learning infrastructure called slime, improving training efficiency and post-training refinement. GLM-5 achieves best-in-class performance among open-source models across reasoning, coding, and agent benchmarks, narrowing the gap with leading frontier models. It ranks highly on evaluations such as Vending Bench 2, demonstrating strong long-term planning and operational capabilities. The model is open-sourced under the MIT License.Starting Price: Free -
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Kimi K2
Moonshot AI
Kimi K2 is a state-of-the-art open source large language model series built on a mixture-of-experts (MoE) architecture, featuring 1 trillion total parameters and 32 billion activated parameters for task-specific efficiency. Trained with the Muon optimizer on over 15.5 trillion tokens and stabilized by MuonClip’s attention-logit clamping, it delivers exceptional performance in frontier knowledge, reasoning, mathematics, coding, and general agentic workflows. Moonshot AI provides two variants, Kimi-K2-Base for research-level fine-tuning and Kimi-K2-Instruct pre-trained for immediate chat and tool-driven interactions, enabling both custom development and drop-in agentic capabilities. Benchmarks show it outperforms leading open source peers and rivals top proprietary models in coding tasks and complex task breakdowns, while its 128 K-token context length, tool-calling API compatibility, and support for industry-standard inference engines.Starting Price: Free -
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GigaChat 3 Ultra
Sberbank
GigaChat 3 Ultra is a 702-billion-parameter Mixture-of-Experts model built from scratch to deliver frontier-level reasoning, multilingual capability, and deep Russian-language fluency. It activates just 36 billion parameters per token, enabling massive scale with practical inference speeds. The model was trained on a 14-trillion-token corpus combining natural, multilingual, and high-quality synthetic data to strengthen reasoning, math, coding, and linguistic performance. Unlike modified foreign checkpoints, GigaChat 3 Ultra is entirely original—giving developers full control, modern alignment, and a dataset free of inherited limitations. Its architecture leverages MoE, MTP, and MLA to match open-source ecosystems and integrate easily with popular inference and fine-tuning tools. With leading results on Russian benchmarks and competitive performance on global tasks, GigaChat 3 Ultra represents one of the largest and most capable open-source LLMs in the world.Starting Price: Free -
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DeepSeek Coder
DeepSeek
DeepSeek Coder is a cutting-edge software tool designed to revolutionize the landscape of data analysis and coding. By leveraging advanced machine learning algorithms and natural language processing capabilities, it empowers users to seamlessly integrate data querying, analysis, and visualization into their workflow. The intuitive interface of DeepSeek Coder enables both novice and experienced programmers to efficiently write, test, and optimize code. Its robust set of features includes real-time syntax checking, intelligent code completion, and comprehensive debugging tools, all designed to streamline the coding process. Additionally, DeepSeek Coder's ability to understand and interpret complex data sets ensures that users can derive meaningful insights and create sophisticated data-driven applications with ease.Starting Price: Free -
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ERNIE X1 Turbo
Baidu
ERNIE X1 Turbo, developed by Baidu, is an advanced deep reasoning AI model introduced at the Baidu Create 2025 conference. Designed to handle complex multi-step tasks such as problem-solving, literary creation, and code generation, this model outperforms competitors like DeepSeek R1 in terms of reasoning abilities. With a focus on multimodal capabilities, ERNIE X1 Turbo supports text, audio, and image processing, making it an incredibly versatile AI solution. Despite its cutting-edge technology, it is priced at just a fraction of the cost of other top-tier models, offering a high-value solution for businesses and developers.Starting Price: $0.14 per 1M tokens -
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ModelArk
ByteDance
ModelArk is ByteDance’s one-stop large model service platform, providing access to cutting-edge AI models for video, image, and text generation. With powerful options like Seedance 1.0 for video, Seedream 3.0 for image creation, and DeepSeek-V3.1 for reasoning, it enables businesses and developers to build scalable, AI-driven applications. Each model is backed by enterprise-grade security, including end-to-end encryption, data isolation, and auditability, ensuring privacy and compliance. The platform’s token-based pricing keeps costs transparent, starting with 500,000 free inference tokens per LLM and 2 million tokens per vision model. Developers can quickly integrate APIs for inference, fine-tuning, evaluation, and plugins to extend model capabilities. Designed for scalability, ModelArk offers fast deployment, high GPU availability, and seamless enterprise integration. -
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DeepSeek V3.1
DeepSeek
DeepSeek V3.1 is a groundbreaking open-weight large language model featuring a massive 685-billion parameters and an extended 128,000‑token context window, enabling it to process documents equivalent to 400-page books in a single prompt. It delivers integrated capabilities for chat, reasoning, and code generation within a unified hybrid architecture, seamlessly blending these functions into one coherent model. V3.1 supports a variety of tensor formats to give developers flexibility in optimizing performance across different hardware. Early benchmark results show robust performance, including a 71.6% score on the Aider coding benchmark, putting it on par with or ahead of systems like Claude Opus 4 and doing so at a far lower cost. Made available under an open source license on Hugging Face with minimal fanfare, DeepSeek V3.1 is poised to reshape access to high-performance AI, challenging traditional proprietary models.Starting Price: Free -
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Command A Translate
Cohere AI
Command A Translate is Cohere’s enterprise-grade machine translation model crafted to deliver secure, high-quality translation across 23 business-relevant languages. Built on a powerful 111-billion-parameter architecture with an 8K-input / 8K-output context window, it achieves industry-leading performance that surpasses models like GPT-5, DeepSeek-V3, DeepL Pro, and Google Translate across a broad suite of benchmarks. The model supports private deployments for sensitive workflows, allowing enterprises full control over their data, and introduces an innovative “Deep Translation” workflow, an agentic, multi-step refinement process that iteratively enhances translation quality for complex use cases. External validation from RWS Group confirms its excellence in challenging translation tasks. Additionally, the model’s weights are available for research via Hugging Face under a CC-BY-NC license, enabling deep customization, fine-tuning, and private deployment flexibility. -
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Yi-Lightning
Yi-Lightning
Yi-Lightning, developed by 01.AI under the leadership of Kai-Fu Lee, represents the latest advancement in large language models with a focus on high performance and cost-efficiency. It boasts a maximum context length of 16K tokens and is priced at $0.14 per million tokens for both input and output, making it remarkably competitive. Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) architecture, incorporating fine-grained expert segmentation and advanced routing strategies, which contribute to its efficiency in training and inference. This model has excelled in various domains, achieving top rankings in categories like Chinese, math, coding, and hard prompts on the chatbot arena, where it secured the 6th position overall and 9th in style control. Its development included comprehensive pre-training, supervised fine-tuning, and reinforcement learning from human feedback, ensuring both performance and safety, with optimizations in memory usage and inference speed. -
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Open R1
Open R1
Open R1 is a community-driven, open-source initiative aimed at replicating the advanced AI capabilities of DeepSeek-R1 through transparent methodologies. You can try Open R1 AI model or DeepSeek R1 free online chat on Open R1. The project offers a comprehensive implementation of DeepSeek-R1's reasoning-optimized training pipeline, including tools for GRPO training, SFT fine-tuning, and synthetic data generation, all under the MIT license. While the original training data remains proprietary, Open R1 provides the complete toolchain for users to develop and fine-tune their own models.Starting Price: Free -
25
Qwen3.5-Plus
Alibaba
Qwen3.5-Plus is a high-performance native vision-language model designed for efficient text generation, deep reasoning, and multimodal understanding. Built on a hybrid architecture that combines linear attention with a sparse mixture-of-experts design, it delivers strong performance while optimizing inference efficiency. The model supports text, image, and video inputs and produces text outputs, making it suitable for complex multimodal workflows. With a massive 1 million token context window and up to 64K output tokens, Qwen3.5-Plus enables long-form reasoning and large-scale document analysis. It includes advanced capabilities such as structured outputs, function calling, web search, and tool integration via the Responses API. The model supports prefix continuation, caching, batch processing, and fine-tuning for flexible deployment. Designed for developers and enterprises, Qwen3.5-Plus provides scalable, high-throughput AI performance with OpenAI-compatible API access.Starting Price: $0.4 per 1M tokens -
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Kimi K2 Thinking
Moonshot AI
Kimi K2 Thinking is an advanced open source reasoning model developed by Moonshot AI, designed specifically for long-horizon, multi-step workflows where the system interleaves chain-of-thought processes with tool invocation across hundreds of sequential tasks. The model uses a mixture-of-experts architecture with a total of 1 trillion parameters, yet only about 32 billion parameters are activated per inference pass, optimizing efficiency while maintaining vast capacity. It supports a context window of up to 256,000 tokens, enabling the handling of extremely long inputs and reasoning chains without losing coherence. Native INT4 quantization is built in, which reduces inference latency and memory usage without performance degradation. Kimi K2 Thinking is explicitly built for agentic workflows; it can autonomously call external tools, manage sequential logic steps (up to and typically between 200-300 tool calls in a single chain), and maintain consistent reasoning.Starting Price: Free -
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DeepSeekMath
DeepSeek
DeepSeekMath is a specialized 7B parameter language model developed by DeepSeek-AI, designed to push the boundaries of mathematical reasoning in open-source language models. It starts from the DeepSeek-Coder-v1.5 7B model and undergoes further pre-training with 120B math-related tokens sourced from Common Crawl, alongside natural language and code data. DeepSeekMath has demonstrated remarkable performance, achieving a 51.7% score on the competition-level MATH benchmark without external tools or voting techniques, closely competing with the likes of Gemini-Ultra and GPT-4. The model's capabilities are enhanced by a meticulous data selection pipeline and the introduction of Group Relative Policy Optimization (GRPO), which optimizes both mathematical reasoning and memory usage. DeepSeekMath is available in base, instruct, and RL versions, supporting both research and commercial use, and is aimed at those looking to explore or apply advanced mathematical problem-solving in AI contexts.Starting Price: Free -
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Qwen3
Alibaba
Qwen3, the latest iteration of the Qwen family of large language models, introduces groundbreaking features that enhance performance across coding, math, and general capabilities. With models like the Qwen3-235B-A22B and Qwen3-30B-A3B, Qwen3 achieves impressive results compared to top-tier models, thanks to its hybrid thinking modes that allow users to control the balance between deep reasoning and quick responses. The platform supports 119 languages and dialects, making it an ideal choice for global applications. Its pre-training process, which uses 36 trillion tokens, enables robust performance, and advanced reinforcement learning (RL) techniques continue to refine its capabilities. Available on platforms like Hugging Face and ModelScope, Qwen3 offers a powerful tool for developers and researchers working in diverse fields.Starting Price: Free -
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Phi-4-reasoning
Microsoft
Phi-4-reasoning is a 14-billion parameter transformer-based language model optimized for complex reasoning tasks, including math, coding, algorithmic problem solving, and planning. Trained via supervised fine-tuning of Phi-4 on carefully curated "teachable" prompts and reasoning demonstrations generated using o3-mini, it generates detailed reasoning chains that effectively leverage inference-time compute. Phi-4-reasoning incorporates outcome-based reinforcement learning to produce longer reasoning traces. It outperforms significantly larger open-weight models such as DeepSeek-R1-Distill-Llama-70B and approaches the performance levels of the full DeepSeek-R1 model across a wide range of reasoning tasks. Phi-4-reasoning is designed for environments with constrained computing or latency. Fine-tuned with synthetic data generated by DeepSeek-R1, it provides high-quality, step-by-step problem solving. -
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Phi-4-reasoning-plus
Microsoft
Phi-4-reasoning-plus is a 14-billion parameter open-weight reasoning model that builds upon Phi-4-reasoning capabilities. It is further trained with reinforcement learning to utilize more inference-time compute, using 1.5x more tokens than Phi-4-reasoning, to deliver higher accuracy. Despite its significantly smaller size, Phi-4-reasoning-plus achieves better performance than OpenAI o1-mini and DeepSeek-R1 at most benchmarks, including mathematical reasoning and Ph.D. level science questions. It surpasses the full DeepSeek-R1 model (with 671 billion parameters) on the AIME 2025 test, the 2025 qualifier for the USA Math Olympiad. Phi-4-reasoning-plus is available on Azure AI Foundry and HuggingFace. -
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DeepScaleR
Agentica Project
DeepScaleR is a 1.5-billion-parameter language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning and a novel iterative context-lengthening strategy that gradually increases its context window from 8K to 24K tokens during training. It was trained on ~40,000 carefully curated mathematical problems drawn from competition-level datasets like AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. DeepScaleR achieves 43.1% accuracy on AIME 2024, a roughly 14.3 percentage point boost over the base model, and surpasses the performance of the proprietary O1-Preview model despite its much smaller size. It also posts strong results on a suite of math benchmarks (e.g., MATH-500, AMC 2023, Minerva Math, OlympiadBench), demonstrating that small, efficient models tuned with RL can match or exceed larger baselines on reasoning tasks.Starting Price: Free -
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Qwen3.5
Alibaba
Qwen3.5 is a next-generation open-weight multimodal large language model designed to power native vision-language agents. The flagship release, Qwen3.5-397B-A17B, combines a hybrid linear attention architecture with sparse mixture-of-experts, activating only 17 billion parameters per forward pass out of 397 billion total to maximize efficiency. It delivers strong benchmark performance across reasoning, coding, multilingual understanding, visual reasoning, and agent-based tasks. The model expands language support from 119 to 201 languages and dialects while introducing a 1M-token context window in its hosted version, Qwen3.5-Plus. Built for multimodal tasks, it processes text, images, and video with advanced spatial reasoning and tool integration. Qwen3.5 also incorporates scalable reinforcement learning environments to improve general agent capabilities. Designed for developers and enterprises, it enables efficient, tool-augmented, multimodal AI workflows.Starting Price: Free -
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Qwen2
Alibaba
Qwen2 is the large language model series developed by Qwen team, Alibaba Cloud. Qwen2 is a series of large language models developed by the Qwen team at Alibaba Cloud. It includes both base language models and instruction-tuned models, ranging from 0.5 billion to 72 billion parameters, and features both dense models and a Mixture-of-Experts model. The Qwen2 series is designed to surpass most previous open-weight models, including its predecessor Qwen1.5, and to compete with proprietary models across a broad spectrum of benchmarks in language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning.Starting Price: Free -
34
QwQ-32B
Alibaba
QwQ-32B is an advanced reasoning model developed by Alibaba Cloud's Qwen team, designed to enhance AI's problem-solving capabilities. With 32 billion parameters, it achieves performance comparable to state-of-the-art models like DeepSeek's R1, which has 671 billion parameters. This efficiency is achieved through optimized parameter utilization, allowing QwQ-32B to perform complex tasks such as mathematical reasoning, coding, and general problem-solving with fewer resources. The model supports a context length of up to 32,000 tokens, enabling it to process extensive input data effectively. QwQ-32B is accessible via Alibaba's chatbot service, Qwen Chat, and is open sourced under the Apache 2.0 license, promoting collaboration and further development within the AI community.Starting Price: Free -
35
Llama 2
Meta
The next generation of our open source large language model. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters. Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations. Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations. We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2.Starting Price: Free -
36
ERNIE X1.1
Baidu
ERNIE X1.1 is Baidu’s upgraded reasoning model that delivers major improvements over its predecessor. It achieves 34.8% higher factual accuracy, 12.5% better instruction following, and 9.6% stronger agentic capabilities compared to ERNIE X1. In benchmark testing, it surpasses DeepSeek R1-0528 and performs on par with GPT-5 and Gemini 2.5 Pro. Built on the foundation of ERNIE 4.5, it has been enhanced with extensive mid-training and post-training, including reinforcement learning. The model is available through ERNIE Bot, the Wenxiaoyan app, and Baidu’s Qianfan MaaS platform via API. These upgrades are designed to reduce hallucinations, improve reliability, and strengthen real-world AI task performance. -
37
Claude Sonnet 3.7
Anthropic
Claude Sonnet 3.7, developed by Anthropic, is a cutting-edge AI model that combines rapid response with deep reflective reasoning. This innovative model allows users to toggle between quick, efficient responses and more thoughtful, reflective answers, making it ideal for complex problem-solving. By allowing Claude to self-reflect before answering, it excels at tasks that require high-level reasoning and nuanced understanding. With its ability to engage in deeper thought processes, Claude Sonnet 3.7 enhances tasks such as coding, natural language processing, and critical thinking applications. Available across various platforms, it offers a powerful tool for professionals and organizations seeking a high-performance, adaptable AI.Starting Price: Free -
38
Janus-Pro-7B
DeepSeek
Janus-Pro-7B is an innovative open-source multimodal AI model from DeepSeek, designed to excel in both understanding and generating content across text, images, and videos. It leverages a unique autoregressive architecture with separate pathways for visual encoding, enabling high performance in tasks ranging from text-to-image generation to complex visual comprehension. This model outperforms competitors like DALL-E 3 and Stable Diffusion in various benchmarks, offering scalability with versions from 1 billion to 7 billion parameters. Licensed under the MIT License, Janus-Pro-7B is freely available for both academic and commercial use, providing a significant leap in AI capabilities while being accessible on major operating systems like Linux, MacOS, and Windows through Docker.Starting Price: Free -
39
Phi-4-mini-reasoning
Microsoft
Phi-4-mini-reasoning is a 3.8-billion parameter transformer-based language model optimized for mathematical reasoning and step-by-step problem solving in environments with constrained computing or latency. Fine-tuned with synthetic data generated by the DeepSeek-R1 model, it balances efficiency with advanced reasoning ability. Trained on over one million diverse math problems spanning multiple levels of difficulty from middle school to Ph.D. level, Phi-4-mini-reasoning outperforms its base model on long sentence generation across various evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. It features a 128K-token context window and supports function calling, enabling integration with external tools and APIs. Phi-4-mini-reasoning can be quantized using Microsoft Olive or Apple MLX Framework for deployment on edge devices such as IoT, laptops, and mobile devices. -
40
DeepCoder
Agentica Project
DeepCoder is a fully open source code-reasoning and generation model released by Agentica Project in collaboration with Together AI. It is fine-tuned from DeepSeek-R1-Distilled-Qwen-14B using distributed reinforcement learning, achieving a 60.6% accuracy on LiveCodeBench (representing an 8% improvement over the base), a performance level that matches that of proprietary models such as o3-mini (2025-01-031 Low) and o1 while using only 14 billion parameters. It was trained over 2.5 weeks on 32 H100 GPUs with a curated dataset of roughly 24,000 coding problems drawn from verified sources (including TACO-Verified, PrimeIntellect SYNTHETIC-1, and LiveCodeBench submissions), each problem requiring a verifiable solution and at least five unit tests to ensure reliability for RL training. To handle long-range context, DeepCoder employs techniques such as iterative context lengthening and overlong filtering.Starting Price: Free -
41
Nebius Token Factory
Nebius
Nebius Token Factory is a scalable AI inference platform designed to run open-source and custom AI models in production without manual infrastructure management. It offers enterprise-ready inference endpoints with predictable performance, autoscaling throughput, and sub-second latency — even at very high request volumes. It delivers 99.9% uptime availability and supports unlimited or tailored traffic profiles based on workload needs, simplifying the transition from experimentation to global deployment. Nebius Token Factory supports a broad set of open source models such as Llama, Qwen, DeepSeek, GPT-OSS, Flux, and many others, and lets teams host and fine-tune models through an API or dashboard. Users can upload LoRA adapters or full fine-tuned variants directly, with the same enterprise performance guarantees applied to custom models.Starting Price: $0.02 -
42
R1 1776
Perplexity AI
Perplexity AI has open-sourced R1 1776, a large language model (LLM) based on DeepSeek R1 designed to enhance transparency and foster community collaboration in AI development. This release allows researchers and developers to access the model's architecture and codebase, enabling them to contribute to its improvement and adaptation for various applications. By sharing R1 1776 openly, Perplexity AI aims to promote innovation and ethical practices within the AI community.Starting Price: Free -
43
MiMo-V2-Flash
Xiaomi Technology
MiMo-V2-Flash is an open weight large language model developed by Xiaomi based on a Mixture-of-Experts (MoE) architecture that blends high performance with inference efficiency. It has 309 billion total parameters but activates only 15 billion active parameters per inference, letting it balance reasoning quality and computational efficiency while supporting extremely long context handling, for tasks like long-document understanding, code generation, and multi-step agent workflows. It incorporates a hybrid attention mechanism that interleaves sliding-window and global attention layers to reduce memory usage and maintain long-range comprehension, and it uses a Multi-Token Prediction (MTP) design that accelerates inference by processing batches of tokens in parallel. MiMo-V2-Flash delivers very fast generation speeds (up to ~150 tokens/second) and is optimized for agentic applications requiring sustained reasoning and multi-turn interactions.Starting Price: Free -
44
Mixtral 8x22B
Mistral AI
Mixtral 8x22B is our latest open model. It sets a new standard for performance and efficiency within the AI community. It is a sparse Mixture-of-Experts (SMoE) model that uses only 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. It is fluent in English, French, Italian, German, and Spanish. It has strong mathematics and coding capabilities. It is natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale. Its 64K tokens context window allows precise information recall from large documents. We build models that offer unmatched cost efficiency for their respective sizes, delivering the best performance-to-cost ratio within models provided by the community. Mixtral 8x22B is a natural continuation of our open model family. Its sparse activation patterns make it faster than any dense 70B model.Starting Price: Free -
45
QwQ-Max-Preview
Alibaba
QwQ-Max-Preview is an advanced AI model built on the Qwen2.5-Max architecture, designed to excel in deep reasoning, mathematical problem-solving, coding, and agent-related tasks. This preview version offers a sneak peek at its capabilities, which include improved performance in a wide range of general-domain tasks and the ability to handle complex workflows. QwQ-Max-Preview is slated for an official open-source release under the Apache 2.0 license, offering further advancements and refinements in its full version. It also paves the way for a more accessible AI ecosystem, with the upcoming launch of the Qwen Chat app and smaller variants of the model like QwQ-32B, aimed at developers seeking local deployment options.Starting Price: Free -
46
Tencent Yuanbao
Tencent
Tencent Yuanbao is an AI-powered assistant that has quickly become popular in China, leveraging advanced large language models, including Tencent's proprietary Hunyuan model, and integrating with DeepSeek. The application excels in areas like Chinese language processing, logical reasoning, and efficient task execution. Yuanbao's popularity has surged in recent months, even surpassing competitors such as DeepSeek to top the Apple App Store download charts in China. A key driver of its growth is its deep integration into the Tencent ecosystem, particularly within WeChat, further enhancing its accessibility and functionality. This rapid rise highlights Tencent's growing ambition in the competitive AI assistant market. -
47
SWE-1.5
Cognition
SWE-1.5 is the latest agent-model release by Cognition, purpose-built for software engineering and characterized by a “frontier-size” architecture comprising hundreds of billions of parameters and optimized end-to-end (model, inference engine, and agent harness) for both speed and intelligence. It achieves near-state-of-the-art coding performance and sets a new benchmark in latency, delivering inference speeds up to 950 tokens/second, roughly six times faster than its predecessor Haiku 4.5 and thirteen times faster than Sonnet 4.5. The model was trained using extensive reinforcement learning in realistic coding-agent environments with multi-turn workflows, unit tests, quality rubrics, and browser-based agentic execution; it also benefits from tightly integrated software tooling and high-throughput hardware (including thousands of GB200 NVL72 chips and a custom hypervisor infrastructure). -
48
Stable LM
Stability AI
Stable LM: Stability AI Language Models. The release of Stable LM builds on our experience in open-sourcing earlier language models with EleutherAI, a nonprofit research hub. These language models include GPT-J, GPT-NeoX, and the Pythia suite, which were trained on The Pile open-source dataset. Many recent open-source language models continue to build on these efforts, including Cerebras-GPT and Dolly-2. Stable LM is trained on a new experimental dataset built on The Pile, but three times larger with 1.5 trillion tokens of content. We will release details on the dataset in due course. The richness of this dataset gives Stable LM surprisingly high performance in conversational and coding tasks, despite its small size of 3 to 7 billion parameters (by comparison, GPT-3 has 175 billion parameters). Stable LM 3B is a compact language model designed to operate on portable digital devices like handhelds and laptops, and we’re excited about its capabilities and portability.Starting Price: Free -
49
StarCoder
BigCode
StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained a ~15B parameter model for 1 trillion tokens. We fine-tuned StarCoderBase model for 35B Python tokens, resulting in a new model that we call StarCoder. We found that StarCoderBase outperforms existing open Code LLMs on popular programming benchmarks and matches or surpasses closed models such as code-cushman-001 from OpenAI (the original Codex model that powered early versions of GitHub Copilot). With a context length of over 8,000 tokens, the StarCoder models can process more input than any other open LLM, enabling a wide range of interesting applications. For example, by prompting the StarCoder models with a series of dialogues, we enabled them to act as a technical assistant.Starting Price: Free -
50
Olmo 2
Ai2
Olmo 2 is a family of fully open language models developed by the Allen Institute for AI (AI2), designed to provide researchers and developers with transparent access to training data, open-source code, reproducible training recipes, and comprehensive evaluations. These models are trained on up to 5 trillion tokens and are competitive with leading open-weight models like Llama 3.1 on English academic benchmarks. Olmo 2 emphasizes training stability, implementing techniques to prevent loss spikes during long training runs, and utilizes staged training interventions during late pretraining to address capability deficiencies. The models incorporate state-of-the-art post-training methodologies from AI2's Tülu 3, resulting in the creation of Olmo 2-Instruct models. An actionable evaluation framework, the Open Language Modeling Evaluation System (OLMES), was established to guide improvements through development stages, consisting of 20 evaluation benchmarks assessing core capabilities.
