Best Large Language Models in China

DeepSeek-V2, developed by DeepSeek-AI, is a cutting-edge Mixture-of-Experts (MoE) language model designed for cost-effective training and high-speed inference. Boasting a massive 236 billion parameters—though only 21 billion are active per token—it efficiently handles a context length of up to 128K tokens. The model leverages advanced architectural innovations such as Multi-head Latent Attention (MLA) to optimize inference by compressing the Key-Value (KV) cache and DeepSeekMoE to enable economical training via sparse computation. Compared to its predecessor, DeepSeek 67B, it slashes training costs by 42.5%, shrinks the KV cache by 93.3%, and boosts generation throughput by 5.76 times. Trained on a vast 8.1 trillion token dataset, DeepSeek-V2 excels in natural language understanding, programming, and complex reasoning, positioning itself as a premier choice in the open-source AI landscape.

Falcon Mamba 7B is the first open-source State Space Language Model (SSLM), introducing a revolutionary advancement in Falcon's architecture. Independently ranked as the top-performing open-source SSLM by Hugging Face, it redefines efficiency in AI language models. With low memory requirements and the ability to generate long text sequences without additional computational costs, Falcon Mamba 7B outperforms traditional transformer models like Meta’s Llama 3.1 8B and Mistral’s 7B. This cutting-edge model highlights Abu Dhabi’s leadership in AI research and innovation, pushing the boundaries of what’s possible in open-source machine learning.

Falcon 2 11B is a cutting-edge open-source AI model, designed for multilingual and multimodal tasks, and the only one featuring vision-to-language capabilities. It outperforms Meta’s Llama 3 8B and rivals Google’s Gemma 7B, as verified by the Hugging Face Leaderboard. The next step in its evolution includes integrating a 'Mixture of Experts' framework to further elevate its performance and expand its capabilities.

Falcon 3 is the latest open-source large language model (LLM) from the Technology Innovation Institute (TII), designed to bring powerful AI capabilities to a wider audience. Built for efficiency, it can run smoothly on lightweight devices, including laptops, without compromising speed or performance. The Falcon 3 ecosystem features four scalable models, each optimized for different applications, and supports multiple languages while maintaining resource efficiency. Excelling in tasks such as reasoning, language comprehension, instruction following, coding, and mathematics, Falcon 3 sets a new benchmark in AI accessibility. With its balance of high performance and low computational requirements, it aims to make advanced AI more available to users across industries.

Qwen2.5-Max is an advanced Mixture-of-Experts (MoE) model from the Qwen team, trained on more than 20 trillion tokens and enhanced through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). It surpasses models like DeepSeek V3 in key benchmarks, including Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also performing strongly in broader evaluations like MMLU-Pro. Available via API on Alibaba Cloud, Qwen2.5-Max can also be tested interactively through Qwen Chat, offering users a powerful tool for diverse AI-driven applications.

Qwen2.5-VL is an advanced vision-language model in the Qwen series, offering improved visual comprehension and reasoning over its predecessor, Qwen2-VL. It can accurately interpret a wide range of visual elements, including text, charts, icons, and layouts, making it highly effective for complex image and document analysis. Acting as an intelligent visual agent, the model can dynamically interact with tools, analyze extended video content over an hour long, and identify key segments with precision. It also excels in object localization, generating bounding boxes or points with structured JSON outputs for various attributes. Additionally, Qwen2.5-VL supports structured data extraction from documents such as invoices, forms, and tables, benefiting industries like finance and commerce. Available in base and instruct versions across 3B, 7B, and 72B model sizes, it is accessible on platforms like Hugging Face and ModelScope for seamless integration.

Mistral AI has introduced two state of the art models for on-device computing, and edge use cases. These models are called "les Ministraux", Ministral 3B, and Ministral 8B. These models are a new frontier for knowledge, commonsense, function-calling and efficiency within the sub-10B category. They can be used for a variety of applications, from orchestrating workflows to creating task workers. Both models support contexts up to 128k (currently 32k for vLLM) and Ministral 8B has a sliding-window attention pattern that allows for faster and more memory-efficient inference. These models were designed to provide a low-latency and compute-efficient solution for scenarios like on-device translators, internet-less intelligent assistants, local analytics and autonomous robotics. Les Ministraux, when used in conjunction with larger languages models such as Mistral Large or other agentic workflows, can also be efficient intermediaries in function-calling.

Mistral AI has introduced "les Ministraux", two advanced models, for on-device computing applications and edge applications. These models are Ministral 3B (the Ministraux) and Ministral 8B (the Ministraux). These models excel at knowledge, commonsense logic, function-calling and efficiency in the sub-10B parameter area. They can handle up to 128k contexts and are suitable for a variety of applications, such as on-device translations, offline smart assistants and local analytics. Ministral 8B has an interleaved sliding window attention pattern that allows for faster and memory-efficient inference. Both models can be used as intermediaries for multi-step agentic processes, handling tasks such as input parsing and task routing and API calls with low latency. Benchmark evaluations show that les Ministraux consistently performs better than comparable models in multiple tasks. Both models will be available as of October 16, 2024. Ministral 8B is priced at $0.1 for every million tokens.

Mistral AI announced a number of key updates on September 17, 2024 to improve the accessibility and performance. They introduced a free version of "La Plateforme", their serverless platform, which allows developers to experiment with and prototype Mistral models at no cost. Mistral AI has also reduced the prices of their entire model line, including a 50% discount for Mistral Nemo, and an 80% discount for Mistral Small and Codestral. This makes advanced AI more affordable for users. The company also released Mistral Small v24.09 - a 22-billion parameter model that offers a balance between efficiency and performance, and is suitable for tasks such as translation, summarization and sentiment analysis. Pixtral 12B is a model with image understanding abilities that can be used to analyze and caption pictures without compromising text performance.

SuperGrok is an upgraded version or premium tier of xAI's Grok, built to provide expanded features like unlimited image generation, Grok 3 access, advanced reasoning, and in-depth research capabilities. It aims to be a more powerful yet cost-efficient alternative to other high-end AI services.

Perplexity AI has introduced R1 1776 as an open-source variant of DeepSeek R1, aiming to advance transparency and collaboration in AI research. By making the model’s architecture and codebase publicly accessible, developers and researchers can refine and expand its capabilities for diverse applications. This initiative not only encourages community-driven innovation but also reinforces ethical AI development by building on the foundation of DeepSeek R1.

QwQ-Max-Preview introduces an exciting glimpse into the capabilities of the Qwen2.5-Max-based AI model, optimized for advanced reasoning tasks, complex mathematics, coding, and agent-driven workflows. As a preview version, it showcases the model’s ability to process and provide solutions for diverse problems, setting the stage for the full release. The model is designed for deep learning tasks and promises further enhancements, with the official open-source release planned under the Apache 2.0 license. Future updates include the Qwen Chat app for seamless, user-friendly interaction and the availability of smaller models like QwQ-32B, which are ideal for developers and privacy-sensitive applications.

Alpa aims automate large-scale distributed training. Alpa was originally developed by people at UC Berkeley's Sky Lab. Alpa's advanced techniques were described in a paper published by OSDI'2022. Google is adding new members to the Alpa community. A language model is a probabilistic distribution of probability over a sequence of words. It uses all the words it has seen to predict the next word. It is useful in a variety AI applications, including the auto-completion of your email or chatbot service. You can find more information on the language model Wikipedia page. GPT-3 is a large language model with 175 billion parameters that uses deep learning to produce text that looks human-like. GPT-3 was described by many researchers and news articles as "one the most important and interesting AI systems ever created." GPT-3 is being used as a backbone for the latest NLP research.

InstructGPT is an open source framework that trains language models to generate natural language instruction from visual input. It uses a generative, pre-trained transformer model (GPT) and the state of the art object detector Mask R-CNN to detect objects in images. Natural language sentences are then generated that describe the image. InstructGPT has been designed to be useful in all domains including robotics, gaming, and education. It can help robots navigate complex tasks using natural language instructions or it can help students learn by giving descriptive explanations of events or processes.

You can use advanced language models and coding to solve a variety of problems. To build cutting-edge applications, leverage large-scale, generative AI models that have deep understandings of code and language to allow for new reasoning and comprehension. These coding and language models can be applied to a variety use cases, including writing assistance, code generation, reasoning over data, and code generation. Access enterprise-grade Azure security and detect and mitigate harmful use. Access generative models that have been pretrained with trillions upon trillions of words. You can use them to create new scenarios, including code, reasoning, inferencing and comprehension. A simple REST API allows you to customize generative models with labeled information for your particular scenario. To improve the accuracy of your outputs, fine-tune the hyperparameters of your model. You can use the API's few-shot learning capability for more relevant results and to provide examples.

Production-ready AI models that are fast and accurate. High-availability inference API that leverages the most advanced NVIDIA GPUs. We have selected the most popular open-source natural language processing models (NLP) and deployed them for the community. You can fine-tune your models (including GPT-J) or upload your custom models. Then, deploy them to production. Upload your AI models, including GPT-J, to your dashboard and immediately use them in production.

AI21 Studio provides API access to Jurassic-1 large-language-models. Our models are used to generate text and provide comprehension features in thousands upon thousands of applications. You can tackle any language task. Our Jurassic-1 models can follow natural language instructions and only need a few examples to adapt for new tasks. Our APIs are perfect for common tasks such as paraphrasing, summarization, and more. Superior results at a lower price without having to reinvent the wheel Do you need to fine-tune your custom model? Just 3 clicks away. Training is quick, affordable, and models can be deployed immediately. Embed an AI co-writer into your app to give your users superpowers. Features like paraphrasing, long-form draft generation, repurposing, and custom auto-complete can increase user engagement and help you to achieve success.

Jurassic-2 is the latest generation AI21 Studio foundation models. It's a game changer in the field AI, with new capabilities and top-tier quality. We're also releasing task-specific APIs with superior reading and writing capabilities. AI21 Studio's focus is to help businesses and developers leverage reading and writing AI in order to build real-world, tangible products. The release of Task-Specific and Jurassic-2 APIs marks two significant milestones. They will enable you to bring generative AI into production. Jurassic-2 (or J2, as we like to call it) is the next generation of our foundation models with significant improvements in quality and new capabilities including zero-shot instruction-following, reduced latency, and multi-language support. Task-specific APIs offer developers industry-leading APIs for performing specialized reading and/or writing tasks.

FLAN-T5 was released in the paper Scaling Instruction-Finetuned Language Models - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.

CodeGen is a model for program synthesis that is open-source. Trained on TPU v4. OpenAI Codex is competitive with TPU-v4.

A model parallel autoregressive transformator implementation on GPUs based on the DeepSpeed Library. This repository contains EleutherAI’s library for training large language models on GPUs. Our current framework is based upon NVIDIA's Megatron Language Model, and has been enhanced with techniques from DeepSpeed, as well as some novel improvements. This repo is intended to be a central and accessible place for techniques to train large-scale autoregressive models and to accelerate research into large scale training.

GPT-J, a cutting edge language model developed by EleutherAI, is a leading-edge language model. GPT-J's performance is comparable to OpenAI's GPT-3 model on a variety of zero-shot tasks. GPT-J, in particular, has shown that it can surpass GPT-3 at tasks relating to code generation. The latest version of this language model is GPT-J-6B and is built on a linguistic data set called The Pile. This dataset is publically available and contains 825 gibibytes worth of language data organized into 22 subsets. GPT-J has some similarities with ChatGPT. However, GPTJ is not intended to be a chatbot. Its primary function is to predict texts. Databricks made a major development in March 2023 when they introduced Dolly, an Apache-licensed model that follows instructions.

Pythia combines interpretationability analysis and scaling law to understand how knowledge evolves and develops during training with autoregressive transformators.

Dolly is an inexpensive LLM that demonstrates a surprising amount of the capabilities of ChatGPT. Whereas the work from the Alpaca team showed that state-of-the-art models could be coaxed into high quality instruction-following behavior, we find that even years-old open source models with much earlier architectures exhibit striking behaviors when fine tuned on a small corpus of instruction training data. Dolly uses an open source model with 6 billion parameters from EleutherAI, which is modified to include new capabilities like brainstorming and text creation that were not present in the original.

Multilingual T5 is a massively pretrained text-totext transformer model that has been trained using a similar recipe to T5. This repo can used to reproduce the experiments described in the mT5 article. The mC4 corpus covers 101 languages. Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, and more.