Top 6 Chinese AI Models Like DeepSeek (LLMs)

China is making fast progress in artificial intelligence (AI) with smart language models that can compete with top AI like GPT-4o. 

Models like DeepSeek-V3, Qwen 2.5-Max, and Doubao 1.5 Pro—are great at solving problems, writing code, and understanding text, images, and videos. In fact, these AI models can handle long pieces of text and think more like humans. 

In this listicle comparison guide, we will explore their main features, how they work, and how they compare to other top AI models.

Chinese LLM models (similar to ChatGPT)

1. DeepSeek-V3

Developer/founder(s): Liang Wenfeng

Founded in: 2024

What it is: DeepSeek-V3 is a large language model (LLM) with 671 billion parameters. It understands and generates human-like text. The best part of DeepSeek-V3 is it excels in coding and mathematical tasks.

To enhance logical inference, mathematical reasoning, and real-time problem-solving capabilities, you now have DeepSeek R1, launched in 2025. 

It builds upon the V3 base model, incorporating reinforcement learning techniques to improve reasoning abilities.

However, DeepSeek AI settings have no option to control what data is shared with its servers in China. There are some topics that the LLM will avoid answering, like the 1989 Tiananmen Square massacre. 

Key features

Mixture-of-Experts (MoE) Architecture: 

DeepSeek-V3 has 671 billion parameters, but only 37 billion are active per input. This makes it highly efficient compared to dense models that activate all parameters at once. 

The model selects 8 out of 256 experts dynamically for each task, optimizing both performance and cost.

Multi-Head Latent Attention: 

The model implements an advanced form of attention mechanism that reduces memory usage while improving the accuracy of responses.

Extended Context Length:

DeepSeek-V3 can process up to 128,000 tokens in a single prompt, making it ideal for long-form content generation, such as legal documents, books, and research papers.

Multi-Token Prediction:

Instead of predicting one token at a time, DeepSeek-V3 predicts multiple tokens simultaneously, drastically increasing inference speed.

It uses parallel token generation to generate responses up to 40% faster than its previous versions.

Cost Efficiency

Training DeepSeek-V3 costs approximately $5.6 million, which is significantly lower than comparable models like GPT-4o. This cost-effectiveness occurs because of its MoE architecture, which reduces computational requirements.

The graph below depicts the total cost of different AI models according to Polyglot.

Performance

According to the Weights & Biases report, DeepSeek V3 is marking a significant development in the world of LLMs. 

It achieves a score of 88.5 on MMLU (Massive Multitask Language Understanding) benchmark, which is slightly above Llama 3.1’s 88.6, Qwen 2.5’s 85.3, and Claude-3.5 Sonnet’s 88.3. 

Some of its other performance stats are:

• DROP Benchmark: It achieved a score of 91.6, outperforming Llama 3.1's 88.7. 
• Codeforces Benchmark: DeepSeek V3 scored 51.6, indicating strong code generation capabilities. 
• MATH-500 Benchmark: It achieved a score of 90.2, demonstrating exceptional mathematical reasoning.

The graph below compares the performance of DeepSeek V3 with Qwen 2.5 and Llama 3.1. 

2. Qwen 2.5-Max

Developer/founder(s): Alibaba Cloud

Founded in: 2025

What it is: Qwen 2.5-Max is Alibaba’s latest AI model, built with advanced architecture for efficiency and performance. It supports large-scale AI applications across various industries and is available via Alibaba Cloud’s API. This LLM competes with top models like GPT-4o and excels in reasoning, coding, and multimodal processing.

Key features

MoE Architecture = More Power, Less Cost

Unlike traditional AI models that activate all parameters at once, Qwen 2.5-Max only uses the relevant parts for a given task. This makes it 30% more efficient, meaning it delivers high performance without burning through computing power.

Trained on 20 Trillion Tokens

This model has learned from a massive dataset that includes research papers, code, multilingual content, and real-world scenarios. Plus, Alibaba fine-tuned it with Supervised Learning (SFT) and Human Feedback (RLHF) to improve its accuracy.

Handles 128K Tokens in One Go

Qwen 2.5 Max is one of the highest context windows that can process long documents in one go. For example, you can process most of the legal documents, research papers, and codebases with Qwen 2.5 Max

Understands Text, Images, & Video

Unlike some AI models that are just text-based, Qwen 2.5-Max is multimodal. That means it can analyze images, process audio, and even understand video content.

It can easily create an image with any prompt you provide.

This LLM is so efficient in logical thinking that it can generate proper Python code with your instructions. 

Cost Efficiency

Qwen 2.5-Max is one of the most cost-effective AI models available today. With a pricing of $0.38 per million tokens, it is significantly cheaper than GPT-4o and Claude 3.5 Sonnet.

Qwen 2.5-Max achieves this cost efficiency using its Mixture-of-Experts (MoE) architecture, which reduces computational costs by 30% compared to traditional dense models.

Performance

Here’s how Qwen 2.5-Max performs compared to leading AI models like GPT-4o, Claude 3.5 Sonnet, and DeepSeek V3:

• Arena-Hard (User Preference Alignment): Qwen 2.5-Max scores 89.4, ahead of DeepSeek V3 (85.5) and Claude 3.5 Sonnet (85.2).
• MMLU-Pro (Knowledge and Reasoning): It scores 76.1, surpassing DeepSeek V3 (75.9) but slightly behind Claude's 3.5 Sonnet (78.0).
• LiveCodeBench & HumanEval (Coding Ability): It achieves 92.7%, outperforming GPT-4o (90.1%) and DeepSeek V3 (88.9%).
• LiveBench (Overall AI Tasks): Qwen 2.5-Max leads with 62.2, exceeding DeepSeek V3 (60.5) and Claude 3.5 Sonnet (60.3).

The graph below depicts Qwen 2.5-Max performance across multiple benchmarks in comparison to top LLMs.

3. Doubao 1.5 Pro

Developer/founder(s): ByteDance

Founded in: 2025

What it is: Doubao 1.5 Pro is an AI model equipped with deep thinking abilities. It solves multiple challenges like addressing long context understanding and balancing computational efficiency with accuracy. 

Key features

Sparse Mixture-of-Experts (MoE) architecture:

It activates only a fraction of parameters per operation, reducing computational costs while maintaining high performance. This LLM outperforms dense models with seven times the activation parameters.

Multimodal capabilities:

It supports text, vision, and speech for diverse applications. Doubao 1.5 Pro improves document recognition and fine-grained visual understanding.

Advanced deep thinking & reasoning:

Doubao 1.5 Pro uses reinforcement learning (RL) to enhance logical and analytical capabilities. It performs well in complex problem-solving tasks.

Heterogeneous system design

Its heterogeneous system design is suitable for pre-fill decode and attention FNN tasks, optimising throughput and minimising latency.

Extended context window

It can process upto 256,000 tokens at a single point, suitable for legal document analysis, academic research, and customer service.

Cost Efficiency

It is 5 times cheaper than DeepSeek, and 200 times cheaper than OpenAI’s 01. Doubao 1.5 pro uses a server cluster that supports low end chips, reducing infrastructure costs. 

Performance

Doubao-1.5-Pro matches or surpasses models like GPT-4o and Claude 3.5 Sonnet in various benchmarks, demonstrating robust capabilities in language understanding and generation tasks.

Some of the area where it performs the best are: 

• DROP (93.0): Excels in reading comprehension and reasoning.
• BBH (91.6): High performance in complex reasoning tasks.
• CMMLU (90.9) & C-Eval (91.8): Strong results in Chinese language understanding.
• IFEVal (89.5): High proficiency in instruction following. 

Here's how Doubao 1.5 pro performs in comparison to other tools: 

4. Kimi (Kimi k1.5)

Developer/founder(s): Moonshot AI

Founded in: January 21, 2025

What it is: Kimi k1.5 is a multimodal AI model that can work with both text and visual inputs, like images and videos. Unlike DeepSeek V3 and R1 models, which are primarily reasoning models, this LLM solves complex problems across multiple domains including mathematics, coding and multimodal reasoning. 

Key features

Long-Context Processing (128k Tokens)

The model can process large amounts of text (up to 128,000 tokens) in a single pass, making it ideal for analyzing books, research papers, and lengthy reports. 

Enhanced Policy Optimization

It employs an advanced policy optimization technique called online mirror descent, ensuring stable decision-making.

Multimodal Integration

Kimi k1.5 can process both text and images together, enabling it to analyze charts, graphs, and visual data. This makes it particularly useful for applications like medical imaging and financial data interpretation.

The diagram below shows how the tool analyses an image and solves a puzzle. It provides the entire logic behind the solution. 

Enhanced Chain of Thought (CoT) reasoning: It offers detailed and concise reasoning modes, improving problem-solving abilities.

Here is how Kimi solves logical problems in seconds. 

Parallel Computing Infrastructure

It employs three-way parallel computing—pipeline, expert, and tensor parallelism—to optimize speed and efficiency. This allows it to process large-scale computations across multiple GPUs.

Cost Efficiency

 It is cost effective due to lower development costs. 

Performance

Kimi K1.5 excels in text, reasoning, and vision benchmarks. The long-CoT model enhances long-term reasoning via supervised fine-tuning and reinforcement learning.

The short-CoT model optimizes token efficiency. It outperforms GPT-4o and Claude Sonnet 3.5 on AIME, MATH-500, and LiveCodeBench by a large margin (up to +550%).

Kimi K1.5 achieves leading results in MATH-500, AIME 2024, and MathVista, demonstrating advanced AI capabilities across diverse tasks.

5. GLM-4 plus (ChatGLM)

Developer/founder(s): Zhipu AI

Founded in: 2024

What it is: GLM-4-Plus is Zhipu AI's latest flagship model, offering improvements in language understanding, long-text processing, and reasoning capabilities. It utilizes PPO technology for better performance in mathematical and coding tasks. The model competes with top-tier AI like GPT-4o and supports multi-modal interactions. 

Key features

Advanced Conversational Abilities

The GLM-4-9B-Chat model supports multi-round conversations, ensuring more natural and coherent interactions. It can maintain long discussions while understanding context effectively.

Powerful Tool Integration

This model can browse the web, execute code, make custom tool calls (Function Call), and process long text reasoning with support for up to 128K tokens.

Multilingual Capabilities

GLM-4 now supports 26 languages, including Japanese, Korean, and German. This makes it more accessible to a global audience.

Extended Context Length

The GLM-4-9B-Chat-1M model can handle up to 1 million tokens, which is roughly 2 million Chinese characters. This allows it to process extremely long documents with ease.

Advanced Multimodal Understanding

GLM-4V-9B can generate and analyze high-resolution images (1120×1120) while maintaining strong conversational abilities in both Chinese and English.

PPO Optimization

Proximal Policy Optimization (PPO) enhances its ability to solve mathematical and coding tasks efficiently.

Cost Efficiency

Performance

Language Capabilities: GLM-4-Plus performs at the level of top-tier models like GPT-4o, excelling in reasoning tasks such as mathematics and code algorithms. 

It demonstrates 99% to 104% efficiency compared to models like Claude 3.5 Sonnet and GPT-4o in benchmarks like AlignBench, MMLU, and MATH.
 

Long Text Processing: The model efficiently handles long text reasoning, surpassing Claude 3.5 Sonnet and reaching 103% of GPT-4o's performance in InfiniteBench/EN.MC. It ensures better comprehension of extended content.

6. WuDao 3.0

Developer/founder(s): Beijing Academy of Artificial Intelligence (BAAI)

Founded in: 2023

What it is: WuDao 3.0 is a collection of smaller, dense, open-source large language models (LLMs) under the name Wu Dao Aquila, designed to enable Chinese startups and smaller entities to build their own generative AI applications.

Key features

Multilingual support

It understands and processes both Chinese and English, making it useful for a wide range of users.

Multimodal capabilities

WuDao 3.0 can process both text and images, enabling applications in chatbots, content creation, and image analysis.

AquilaChat Dialogue Model

WuDao 3.0 includes AquilaChat, a powerful dialogue model that enables fluent and natural conversations in multiple languages, including Chinese and English.

AquilaCode for Code Generation

The model can generate code from text inputs, making it useful for developers looking to automate programming tasks or assist in software development.

Advanced Visual Processing

WuDao 3.0 supports multimodal AI, allowing it to generate images from text descriptions and understand visual content, making it useful for applications in design and media.

Cost Efficiency

WuDao 3.0's smaller, dense models are more cost-efficient compared to larger models like WuDao 2.0. It uses a sparse model approach, activating only a subset of parameters during inference. 

This makes WuDao 3.0 a cost-effective choice for startups and growing businesses. Its open-source model removes licensing fees, and teams can work with a custom software development company to adapt the technology to their specific goals. This helps align the tools with real business needs while keeping performance high and systems scalable.
 

Performance

Image source

• Zero-Shot Learning: Outperformed OpenAI’s CLIP on ImageNet and UC Merced Land-Use classification.
• Few-Shot Learning: Beat GPT-3 in SuperGLUE (FewGLUE).
• Knowledge & Language Understanding: Retrieved factual knowledge better than AutoPrompt (LAMA) and surpassed Microsoft Turing-NLG in reading comprehension (LAMBADA).
• Text-Image Tasks: Generated better images from text than OpenAI’s DALL·E and outperformed CLIP & Google ALIGN in image-text retrieval (MS COCO).

Comparison of top Chinese AI models LLMs

Final words

Chinese AI models are catching up fast with popular Western AI like ChatGPT. Models like DeepSeek-V3 and Qwen 2.5-Max offer great value for companies looking to build AI products.

They're much cheaper but still very smart– perfect for the talented developers you can hire through Index.dev. 

When your new tech team starts working on AI projects, these affordable Chinese models can help them build amazing applications without spending too much money. 

This makes Index.dev's talent network even more valuable for growing your business with AI.

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