Top 10 Open-Source LLMs models for commercial use

The advancement of large language models (LLMs) is a major development in the technology field. These tools not only revolutionise interaction with machines but also present unique opportunities for creativity and innovation. In this blog post, we will explore open-source LLMs, an area that has gained popularity due to its accessibility and versatility.

Why should you be interested in open-source LLMs?

Open-source LLMs are free for anyone to access, use, modify, and share. They are designed to be transparent, allowing developers and researchers to collaborate and improve them. This helps reduce biases and supports innovation. These models are transforming how we process information, communicate, and solve problems, with a wide impact on industries ranging from startups to large companies in various areas of our digital lives.

With so many tools out there, it is hard to know which ones are truly valuable. In this blog, we’ve carefully curated the top 10 open-source LLMs based on performance benchmarks and community feedback. Whether you’re a developer, business owner, or tech enthusiast, these models can give you an edge.

What is a Large Language Model (LLM)?

Open-source Large Language Models: Visual representation of accessible AI models for language processing

A large language model (LLM) is a type of artificial intelligence (AI) that uses deep learning and lots of data to understand, summarize, create, and predict new content.

LLMs are trained on huge amounts textual datasets, allowing them to perform tasks like recognizing patterns, translating languages, and generating text. These models are based on neural networks, which are computing systems inspired by the structure and function of the human brain.

LLMs, or large language models, are typically classified as open-source or proprietary.

The unique advantage of open-source LLMs is that they are accessible to everyone. This transparency allows us to explore the training data, understand their construction, and see how they function. Open-source LLMs encourage collaboration and innovation, enabling researchers and developers to contribute to and improve these models.

Proprietary models like OpenAI’s GPT, Anthropic Claude, and others typically do not provide access to their trained weights or full codebases. However, they often share detailed information about their design and capabilities. Now, let’s explore what is an Open Weight LLM.

What is Open Weight LLM?

Open Weight Large Language Model refers to a model where the trained weights, or parameters, are publicly available. This allows developers and researchers to use, fine-tune, and experiment with a fully trained model without needing access to the original training data or process.

However, the underlying code and architecture might not be open, restricting modifications to the model’s structure or training methodology.

What is Open Source LLM?

The Open Source Large Language Model includes both the trained weights and the source code under an open-source license. This means that users have full access to the model’s architecture, training data, and processes, allowing comprehensive modifications, replications, and improvements.

Open-source LLMs aim for transparency, collaboration, and innovation by providing the complete framework and methodology behind the model.

Top 10 Open-Source LLMs You Should Know in 2025

Discover the Best Open-Source Large Language Models for Advanced AI Solutions

Choosing the right open-source language model can be hard. That is why we have curated this list of open-source LLMs. We have carefully selected the top 10 open-source LLMs, each with unique key features, using this foundation and our industry expertise of AI and LLMs. The list of open source LLMs follows below:

1. Deepseek R1

Cutting-Edge Technology: DeepSeek R1, launched in January 2025, is an open-source LLM known for its advanced logical reasoning, mathematical problem-solving, and real-time decision-making. It’s available under the MIT License for free use and modification.
Cost-Efficient and High Performance: Trained at a significantly lower cost compared to other leading models like GPT-4, DeepSeek R1 still delivers high performance, excelling in tasks such as coding and complex calculations, while being more resource-efficient.
Industry Adoption and Growth: DeepSeek R1 is gaining traction across various industries, with its availability on platforms like Azure AI Foundry and GitHub, establishing itself as a strong competitor in the AI space.

Explore Deepseek R1 on Hugging Face

2. Llama 3

Introduction of Meta Llama 3: Meta Llama 3, the latest generation of the state-of-the-art open-source large language model, is introduced, with models soon available on various platforms including AWS, Google Cloud, Microsoft Azure, and more.
State-of-the-art Performance: Llama 3 aims to offer state-of-the-art performance with 8B and 70B parameter models, showcasing improvements in reasoning, code generation, and instruction following over previous iterations.
Development Focus of Llama 3: The development of Llama 3 focuses on model architecture enhancements, scaling up pretraining with a large dataset, instruction fine-tuning, and providing trust and safety tools such as Llama Guard 2, Code Shield, and CyberSec Eval 2.

Explore Llama 3 on Hugging Face

3. SnowFlake Arctic

Model Availability: Both base and instruct-tuned versions of Arctic are accessible under the Apache-2.0 license and can easily integrate into various research, prototype, and product development activities.
Comprehensive Documentation: Extensive resources, including tutorials and a live demo of the Streamlit app are provided in the Snowflake Arctic GitHub repository, ensuring ease of access and understanding.
Advanced Architecture: Arctic’s architecture combines a 10B dense transformer model with a residual 128×3.66B MoE MLP, resulting in 480B total and 17B active parameters, strategically selected using a top-2 gating approach.

Explore Snowflake Arctic on Hugging Face

3. Microsoft Phi 3

Microsoft introduces a family of small language and multi-modal models called Phi-3, available in various context lengths to suit diverse needs.

Versatile Models: Phi-3 offers models in different sizes and formats, from mini to medium, supporting both text and vision tasks.
Compact Design: With models ranging from 4k to 128k parameters, Phi-3 balances performance with efficiency.
Hugging Face Format: Phi-3 models in Hugging Face format cater to text generation tasks and are available in various sizes.

Explore Phi 3 on Hugging Face

5. Vicuna-33B

Fine-Grained Contextual Understanding: Captures intricate nuances of context for accurate responses.
Cross-Domain Versatility: Trained on diverse text sources for proficiency across various domains.
Rapid Inference Speed: Delivers fast responses without compromising accuracy.

Explore Vicuna-33B on Hugging Face

5. Cohere Command R+

📝Note: Cohere R+ weights are openly available. However, it is not an open-source LLM because they do not provide a license for commercial use; it is available for research use only.

Cutting-Edge AI Release: C4AI Command R+ emerges as an open weights research breakthrough, boasting a colossal 104 billion parameter model equipped with advanced capabilities like Retrieval Augmented Generation (RAG) and multi-step tool use for automating intricate tasks.
Multilingual: Command R+ undergoes rigorous evaluation across 10 languages, ensuring stellar performance in English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese.
Comprehensive Tool Use: This model revolutionises task execution with its pioneering tool utilisation, enabling multi-step tool use to tackle complex challenges effectively. With its refined conversational tool use capabilities, Command R+ integrates seamlessly into various workflows, ranging from reasoning to summarization and question answering.

Explore Cohere on Hugging Face

7. Qwen 2.5

Chinese Language Proficiency: specialises in understanding and generating Chinese content.
Advanced Performance: Qwen2.5-Max, developed by Alibaba Cloud, is a large-scale Mixture-of-Expert (MoE) model trained on over 20 trillion tokens, excelling in benchmarks like MMLU-Pro, LiveCodeBench, and Arena-Hard.
API Access: Available via Alibaba Cloud’s Model Studio, developers can access Qwen2.5-Max using an API key, making it easy to integrate into existing applications with compatibility to OpenAI’s API format.
Interactive Experience: Users can engage with Qwen2.5-Max in real-time through Qwen Chat, exploring its capabilities and receiving instant feedback on their interactions.

Explore Qwen on Hugging Face

8. Cohere AI Aya

Multilingual Capability: Aya-23-8B shines in generating text of exceptional quality and coherence, covering diverse topics and languages.
Adaptability with Minimal Data: Demonstrates remarkable performance in natural language processing tasks even with limited training data, making it an efficient choice for various applications.
Seamless Integration: With a simple API, Aya-23-8B offers effortless integration into applications, ensuring accessibility and user-friendliness for developers and users alike.

Explore cohere aya on Hugging Face

9. Gemma

Dual-Sized Offering: Gemma offers two sizes tailored for diverse deployment scenarios. The 7B parameter model is ideal for efficient development on consumer-size GPUs and TPUs, while the 2B version caters to CPU and on-device applications. Both sizes are available in base and instruction-tuned variants.
Performance Overview: Gemma-7B is also a good option for top models in the 7B weight category. While Gemma-2B offers intriguing potential for its size, it may not match the leaderboard scores of similarly sized models.

Explore Gemma on Hugging Face

10. Mistral Codestral

Cutting-Edge Programming AI: Codestral-22B-v0.1 boasts training on an extensive dataset covering over 80 programming languages, including popular ones like Python, Java, C, C++, JavaScript, and Bash.
Versatile Usage: This model is versatile, capable of providing documentation, explanations, code factorization, and generating code based on specific instructions. It’s also proficient in predicting middle tokens between prefixes and suffixes, making it invaluable for software development enhancements like in VS Code.
Compatibility: Codestral-22B-v0.1 seamlessly integrates with the transformers library, allowing for straightforward usage within existing workflows.

Explore Mistral on Hugging Face

Each of these models has been designed with specific features that make them suitable for various applications in natural language processing, from text generation to complex problem-solving tasks.

Conclusion

In this blog, we looked at ten open-source language models, like Deepseek, Llama 3, and Mistral. Each one has its own strengths and weaknesses. These models are not just about reading and writing; they are a big step in how we use technology to work with complex information. Some of these models are open-weight, but you can’t always use them for business purposes.

The open-source nature of these models is critical. It enables greater access, collaboration, and the development of new AI ideas. Whether you’re a developer, researcher, or simply curious, these models provide numerous chances for learning, development, and application.

With these large language models, they will change things in a big way, from schools to businesses. We can work more efficiently, be more creative, and solve problems more effectively if we understand them and know how to use them. The future of AI is bright, and these models are leading the way!