Key Takeaways
- Open source LLMs such as LLaMA 3.1, BLOOM, and Vicuna 13-B empower researchers and developers to enhance AI applications with transparency, customization, and community engagement.
- Accessibility sets open source models apart from closed ones, allowing users to modify, adapt, and apply them without restrictions on code or cost.
- Concerns about data misuse exist, but peer review and frequent updates help maintain the reliability and accuracy of open source LLMs.
- Parameters like those in Falcon 180B influence performance, enabling users to fine-tune text generation for research, business, and creative applications.
In 2025, DeepSeek boldly defines the year as one in which open source large language models (LLMs) have firmly taken center stage. LLMs, capable of tasks ranging from text generation to complex problem-solving, are showing their potential to improve industries, create personalized user experiences, and spark new creativity.
So, why does the shift to open source LLMs matter so much?
Essentially, it opens up the floodgates for anyone—from developers to researchers to companies—to access and build on the most advanced models available. Their transparency and flexibility highlight the democratizing effect of open-source LLMs—a significant contrast to the opacity and restrictions of closed-source models.
In this article, we’ll explore the top open source LLMs of 2025, exploring their features, uses, and what sets them apart. We’ll also examine how this collection of tools is improving artificial intelligence (AI).
8 Open Source LLMs in 2025
| Tool | Company/Dev Team | Location |
|---|---|---|
| DeepSeek R1 | DeepSeek | China |
| LLaMA 3.1 | Meta AI | Menlo Park, USA |
| BLOOM | BigScience Workshop | Europe (collaborative) |
| BERT | Google Research | Mountain View, USA |
| OPT-175B | Meta AI | Menlo Park, USA |
| Falcon 180B | Technology Innovation Institute | Abu Dhabi, UAE |
| XGen-7B | MosaicML | San Francisco, USA |
| Vicuna 13-B | Vicuna Team | Global (distributed) |
DeepSeek R1 – DeepSeek
Launched in early 2025, DeepSeek R1 is a groundbreaking model developed by the Chinese startup DeepSeek. Despite utilizing less sophisticated hardware, R1 matches the performance of leading models like OpenAI’s ChatGPT-4. It uses reinforcement learning and a mixture of experts approach, activating only a subset of its 671 billion parameters during any operation.
DeepSeek’s design reduces computational requirements, so advanced AI becomes more accessible. R1 supports various applications, such as math problem-solving and coding assistance. Developers worldwide can use it as an open-source model.
LLaMA 3.1 – Meta AI
Meta AI released LLaMA 3.1, improving text generation and comprehension. The model arrived with updated parameter configurations and performance optimizations. Researchers and developers use LLaMA 3.1 for tasks requiring nuanced language understanding and creative text generation. Its structure, built with billions of adjustable elements, handles complex queries and produces coherent responses in various scenarios.
BLOOM – BigScience Workshop
BLOOM is a product of the BigScience project, an international collaboration of over 1,000 researchers. It was designed with multilingual proficiency in mind, allowing it to process and generate text in numerous languages. BLOOM’s versatility makes it invaluable for academic research and natural language processing tasks. Its open source nature means that the inner workings of BLOOM are available for study, which not only aids in understanding how LLM parameters contribute to performance but also encourages community-led improvements.
BERT – Google Research
Although BERT is one of the earlier open source language models, its influence continues to be felt across various applications. Developed by Google Research, BERT has been integral in sentiment analysis, question answering, and text classification tasks. Its strength lies in understanding context—capturing the nuances of language in ways that remain useful for academic research and industry projects. Even as newer models emerge, BERT’s design offers valuable insights into how deep learning techniques can be applied to interpret complex language patterns.
OPT-175B – Meta AI
Another noteworthy model from Meta AI is OPT-175B. With a design that mirrors other high-parameter models, OPT-175B provides a viable alternative for researchers and developers seeking transparent alternatives to proprietary systems. Since it is open source, users have full insight into its training data and architecture, which is a major advantage for those who prioritize understanding how a model works. The model’s robustness in handling complex language tasks underscores the potential of open source LLMS to serve as a reliable tool in academic and commercial projects.
Falcon 180B – Technology Innovation Institute
Falcon 180B is particularly striking due to its sheer scale. It boasts 180 billion parameters and has been trained on 3.5 trillion tokens, allowing it to handle intricate language tasks precisely. Developed by the Technology Innovation Institute in Abu Dhabi, Falcon 180B is already drawing attention from academic circles and practical industries. The model’s large parameter count enables it to generate detailed and contextually rich outputs. Users have found Falcon 180B to be exceptionally useful in research settings where subtle language distinctions and detailed analysis are required.
XGen-7B – MosaicML
XGen-7B is a product of MosaicML’s focus on balancing performance with computational efficiency. With a more modest parameter count than some of its larger counterparts, XGen-7B serves projects that consider resource limitations. Despite its lighter footprint, it delivers a commendable performance in generating and processing text. Its efficiency makes it a popular choice for smaller-scale applications and environments where quick iteration and low overhead are essential.
Vicuna 13-B – Vicuna Team
Vicuna 13-B is distinct in its fine-tuning of conversational data. Developed by the Vicuna Team, this model has been carefully adjusted to offer an engaging dialogue experience. Users report that Vicuna 13-B generates responses that are contextually appropriate and natural in tone, making it a strong contender for applications such as chatbots and virtual agents. Its development highlights how specialized training can yield models that perform admirably in interactive settings.
Open source vs. Closed Source LLMs
A fundamental discussion in today’s AI community revolves around the distinction between open and closed source language models. Open source LLMs make their underlying code and training data publicly accessible. This openness allows researchers and developers to study how the models work, modify them for specific applications, and contribute improvements based on firsthand insights. In contrast, closed source models are developed by private companies and are often accompanied by proprietary restrictions that limit access to internal methodologies and training data.
For example, consider a well-known closed source model like OpenAI’s GPT-4. With GPT-4, users enjoy high-quality outputs, but the internal architecture and training details remain confidential. The transparency difference influences how developers adopt each model. While closed source systems might offer polished performance in specific commercial applications, open source alternatives provide full visibility, leading to more robust evaluation and continual refinement by a diverse community of experts.
Below is a table summarizing their differences:
| Aspect | Open Source LLMs | Closed Source LLMs |
|---|---|---|
| Code Accessibility | Publicly available for inspection and modification | Proprietary and restricted |
| Customizability | High, with community-led contributions | Limited to developer-led updates |
| Cost | Often free, inviting extensive experimentation | May involve licensing fees or subscription models |
| Transparency | Full insight into training methods and architecture | Limited visibility into internal workings |
Are Open Source LLMs Reliable?
A recurring question among users is the reliability of open source language models. Although their performance has advanced considerably, some skepticism persists. Critics sometimes voice concerns that free models may have hidden trade-offs, such as the risk of companies repurposing data without full consent. This skepticism is directed at emerging platforms like Deepseek, with some questioning whether free access might lead to subtle biases or data usage that benefits certain interests.
On the other hand, the very openness of these models serves as a safeguard. When the inner workings and training data are available for community review, it becomes easier to identify and correct issues. Many community members point to the robust peer-review process and iterative improvements as evidence that open source LLMS can be reliable and trustworthy. While debates continue, the prevailing sentiment is that transparency fosters accountability, which ultimately builds user confidence over time.
What are LLM Parameters?
Understanding LLM parameters may seem technical, but comparing models helps. LLM parameters adjust during training and shape how a model processes and generates text. Each parameter fine-tunes the model’s ability to recognize patterns. A model’s total parameters give a rough idea of its complexity. Falcon 180B, for example, has 180 billion parameters, allowing it to detect intricate language patterns.
More parameters don’t always mean better performance. Higher counts often require more computing power. A model’s efficiency depends on how it organizes and uses parameters. DeepChecks offers a glossary and analysis of LLM parameters for further insights. Comparing parameters helps developers choose the best system for their needs.
Closing Thoughts
Open source LLMs challenge how we think about AI development. They offer transparency, flexibility, and collaboration. Researchers can inspect, modify, and improve these models without restriction. Developers gain tools that adapt to specific needs. Businesses explore AI without relying on closed systems. Yet, open access raises concerns. Who ensures accuracy? How do we prevent misuse? The balance between innovation and responsibility remains critical. Each model reflects the choices of its creators and the values of its community. Understanding these developments requires more than technical knowledge. It demands careful thought about the role of AI in shaping human interaction.
