6 best open-source LLMs in 2025

Large language models (LLMs) power everything from conversational AI to data analysis. As the demand for flexible and accessible AI grows, open-source LLMs have emerged as powerful tools for businesses looking to boost efficiency, improve customer experiences, and unlock AI-driven insights.

Accessing these models with LLM libraries makes adoption even easier, giving companies the opportunity to try a wide range of customizable options while keeping costs low. In this guide, we highlight the top six open-source LLMs of 2025 and explore how businesses can use them to scale, strengthen security, and optimize their AI infrastructure.

What are large language models (LLMs)?

LLMs are advanced AI systems that are trained on vast amounts of text data to understand, generate, and analyze human language. They excel at various natural language processing (NLP) tasks, including text summarization, translation, question answering, and sentiment analysis. Their state-of-the-art capabilities have made them indispensable across many industries, from healthcare to customer service.

Types of LLMs

LLMs come in various types, each tailored to address specific tasks or challenges. Below is a breakdown of the major categories, along with examples to highlight their real-world applications:

When selecting an LLM, consider your organization’s goals, namely whether you need a general-purpose model or one tailored to a specific industry. Open-source models offer flexibility and customization, while closed-source options provide vendor support. Choosing the right one comes down to what works best for your needs.

Open-source vs. closed-source LLMs

The choice between open-source and closed-source LLMs is a defining factor in how these models can be used and adapted.

Open-source LLMs

Open-source LLMs are community-driven, providing both flexibility and transparency. With full access to the source code, businesses can tailor the model to their specific needs. These models are also cost-effective, as they don't involve licensing fees or vendor lock-in. While they may require more technical knowledge to implement and maintain, they offer robust capabilities for those who can manage them. Popular examples include BLOOM, Alpaca, and GPT-J.

When to choose open-source LLMs

• You need full customization to meet specific use cases.
• You value transparency and want to contribute to the model's development.
• You have the technical expertise to handle setup and maintenance.
• You want control over your data usage and privacy without vendor lock-in.
• You're looking for a cost-effective solution without licensing fees.

Closed-source LLMs

Closed-source LLMs are fully developed and maintained by specific vendors, offering optimized performance and dedicated support. These models come with pre-built features, making them ideal for businesses that need a ready-to-use solution. However, they’re less customizable and can be expensive due to licensing fees.

When to choose closed-source LLMs

• You need high performance with minimal setup and support.
• You require dedicated customer support from a provider.
• You want a turnkey solution with pre-built features.
• Data privacy is a major concern, and you prefer a provider-controlled environment.
• You need a solution that’s easy to implement with little technical overhead.

Why open-source is becoming a preferred choice in 2025

In 2025, open-source LLMs are becoming the go-to choice for businesses, offering key advantages like customization, transparency, and cost-effectiveness. With the growing need for flexible AI solutions, these models provide the adaptability organizations require to stay ahead. Some key benefits include:

• Customization: Adapt the model to meet your specific needs, ensuring it fits seamlessly into your business processes.
• Community-driven development: Open-source models benefit from global collaboration, with LLM libraries streamlining access, updates, and enhancements to keep them cutting-edge and widely usable.
• Transparency: With full visibility into the model's architecture, businesses can make informed decisions about its use and potential risks.
• Cost-effectiveness: By avoiding licensing fees, open-source models provide a more affordable alternative to closed-source solutions.
• No vendor lock-in: The code is publicly available, allowing anyone to access, modify, and redistribute it, which eliminates vendor dependency.
• Security: The community can actively identify and resolve security vulnerabilities, enhancing overall model safety.

Open-source LLMs are making a big impact across industries like healthcare, finance, e-commerce, and customer service. For example, they can help doctors catch diseases earlier, stop fraud in its tracks, and make online shopping and support more personal. As these models keep improving, businesses can adopt AI that gives them more control, adapts to their needs, and grows with them over time, ensuring they stay competitive in a rapidly changing world.

With the support of an active developer community, updates and improvements are constantly being made, making these models more reliable and accessible. By leveraging open-source LLMs, companies can reduce costs, innovate faster, and create fine-tuned AI-driven solutions that truly fit their industry and customers.

6 popular open-source LLMs to use in 2025

With so many open-source LLMs on the market, picking the right one can be challenging. Let’s take a closer look at 6 of the most popular models to explore in 2025.

1. LLaMA 3 by Meta

LLaMA 3, developed by Meta, is a powerful open-source language model optimized for text generation, sentiment analysis, and question answering across industries like healthcare, finance, and customer service. Its open-source design enables businesses to customize and fine-tune the model while benefiting from ongoing community improvements.

Key features

• State-of-the-art performance in multiple NLP tasks.
• Open-source, offering complete flexibility for customization and integration.
• Suitable for a wide range of industries, including healthcare, finance, and customer support.

2. Falcon

Falcon, developed by the Technology Innovation Institute, is a high-performance open-source LLM optimized for text generation, summarization, and translation in industries like e-commerce, marketing, and content creation. Designed for speed and scalability, it enables fast deployment in real-time applications while offering a cost-effective AI solution.

Key features

• High-performance text generation and summarization capabilities
• Optimized for scalability, making it suitable for real-time applications
• Community-driven improvements and open-source flexibility

3. BLOOM ++

BLOOM++ builds on its predecessor with enhanced language understanding and improved response accuracy, making it a powerful open-source LLM for multilingual applications. It excels in generating context-aware text for customer support, content creation, and conversational AI while offering flexibility for customization.

Key features

• Enhanced multilingual support for global applications
• Improved context-aware text generation for more accurate responses
• Open-source model with flexibility for customization

4. Open GPT-X

Open GPT-X is a powerful open-source LLM with a scalable architecture that supports natural language processing and complex problem-solving across industries like healthcare, finance, and customer service. Optimized for speed and accuracy, it offers developers a flexible, customizable model for diverse business applications.

Key features

• Scalable architecture for a wide range of use cases
• Optimized for fast and accurate natural language processing
• Open-source model, allowing for full customization

5. GPT-NeoX 3.0

GPT-NeoX 3.0 is a high-performance open-source LLM optimized for natural language understanding, generation, and multi-modal tasks. Its flexibility and extensive training make it ideal for industries like finance, healthcare, and customer service, enabling applications such as fraud detection and predictive analytics.

Key features

• High-performance, scalable architecture for complex tasks
• Robust support for natural language understanding and generation
• Open-source, enabling full customization for diverse applications

6. Mistral AI

Mistral AI is a high-performance open-source LLM built for tasks like text generation, summarization, and translation across industries such as healthcare, finance, and customer service. Its lightweight architecture ensures fast processing and scalability, making it a powerful yet efficient AI solution.

Key features

• Efficient, lightweight design for fast processing
• High versatility for tasks like text generation and summarization
• Open-source, providing full customization and community-driven development

How to choose the right open-source LLM for your organization

Choosing the right open-source LLM requires a clear strategy that aligns with your organization’s goals, infrastructure, and AI capabilities. With various options available, decision-makers should follow a structured evaluation process to ensure the best fit. Here’s a step-by-step approach to selecting an LLM, with key considerations at each stage.

1. Define your objectives

Identify the primary AI tasks your organization needs to address, such as customer support automation, predictive analytics, or multilingual content generation. Different LLMs specialize in different areas—BLOOM++ excels in multilingual tasks, while GPT-NeoX 3.0 is well-suited for fraud detection and data analysis.

2. Assess infrastructure and costs

Consider the computational requirements and budget constraints. High-performance models like Open GPT-X and Falcon 2025 require significant computing power, whereas lightweight models like Mistral AI offer cost-effective deployment without sacrificing efficiency.

3. Evaluate customization needs

Determine whether you need extensive fine-tuning for proprietary datasets or if a robust pre-trained model will suffice. Some LLMs, like LLaMA 3, provide strong adaptability, while others are designed for plug-and-play functionality.

4. Ensure compliance and security

If operating in regulated industries like healthcare or finance, prioritize LLMs that support secure API access or on-premise deployment to meet HIPAA, GDPR, or other compliance standards.

5. Test and iterate

Run performance benchmarks with real-world data to evaluate accuracy, response time, and integration ease. A pilot deployment can help refine your approach before full-scale implementation.

By following this structured strategy, your organization can confidently select an open-source LLM that balances performance, cost, and long-term scalability while ensuring a seamless fit for your operational needs.

Future trends in open-source LLMs

The open-source LLM landscape is changing quickly, with an emphasis on improving efficiency, accessibility, and ethical development. Researchers are working to make powerful models more sustainable and affordable by reducing their computational needs, while new, user-friendly tools are making it easier for non-technical users to integrate and customize LLMs. These improvements are helping AI move beyond just tech companies, with industries like healthcare, finance, and education seeing increased adoption. We’re also seeing AI breakthroughs in sectors like agriculture, energy, and logistics.

As these open-source models continue to evolve, transparency and bias mitigation will remain essential for ethical AI. We may also see a mix of open and closed-source models that offer businesses the right balance of performance, security, and flexibility. By investing in open-source LLMs, organizations can gain a competitive edge, leveraging scalability and customization to foster innovation and maintain control over their AI infrastructure. With these advancements, companies strategically adopting open-source LLMs will be in a strong position for long-term growth and success.

Embrace open-source LLMs for smarter AI

Open-source LLMs are becoming the key to a strong competitive edge in the AI space by offering businesses powerful, flexible, and cost-effective solutions. From LLaMA 3 to GPT-NeoX 3.0, these models enable organizations to enhance operations, improve customer experiences, and drive innovation with greater control and scalability.

Whether you're focused on customer support, data analysis, or multilingual tasks, open-source LLMs provide the adaptability to meet diverse needs across industries. Keep in mind that this isn’t an exclusive list, and exploring additional models that best fit your organization's specific requirements can unlock even greater potential.

As AI technology continues to evolve, staying ahead of the curve can be challenging. Telnyx’s LLM Library offers a simple way to access both open-source and proprietary large language models in Inference, enabling you to easily integrate advanced AI into your projects. With a range of models to choose from, the platform gives you the flexibility to select the right tools to meet your specific needs without the burden of heavy infrastructure costs. Whether you’re building a cutting-edge product or improving operational workflows, the LLM Library allows you to leverage the power of AI to stay competitive.