Top Open-Source LLMs for 2025

Businesses have increasingly adopted AI solutions, including AI text generation tools that use powerful large language models (LLMs). Over the past few years, a multitude of LLMs have emerged, including both open-source and proprietary options. Their significance continues to grow, as the global LLM market is projected to reach $140.8 billion by 2033, with an impressive 40% annual growth rate. Open-source LLMs lead this growth, as they could represent more than 30% of deployments by 2025 due to their cost-effectiveness and flexibility.

Smaller organizations favor open-source LLMs due to their free usage and accessible source code that can be modified to meet specific needs. This post will discuss the benefits of using open-source LLMs, their diverse applications, and some key players expected to shape the open-source LLM landscape in 2025.

What Exactly Are Open Source LLMs?

Open-source large language models (LLMs) are language models whose architecture, training code, and, in many cases, pre-trained weights are publicly available. These models are released under permissive licenses that allow users to study, modify, and use them freely.

The availability of the training code means that users can retrain or fine-tune the model on new datasets. When pre-trained weights are also shared, it becomes possible to use the model directly without the extensive computational resources needed for training from scratch.

Contrasting with Proprietary LLMs

Unlike open-source LLMs, proprietary models are developed and controlled by private organizations. The model’s inner workings, such as its architecture, training data, and exact tuning techniques, are confidential. Examples include OpenAI’s GPT-4, Anthropic’s Claude, and some commercial versions of Google’s Gemini.

Key differences include:

Accessibility: Open-source LLMs can typically be downloaded, modified, and deployed without restrictions. Proprietary models are often accessible only via APIs, with usage limited by service agreements.
Cost: Open-source models are generally free to use, though hardware costs still apply. In contrast, proprietary models usually operate on a pay-per-use basis, with ongoing expenses tied to API calls or subscription tiers.
Control and Customization: Open models offer the flexibility to modify internal mechanisms or fine-tune on custom data. Proprietary systems provide limited, if any, customization options.

Some of the top Open Source LLM

Open-source large language models (LLMs) are diverse and offer powerful tools for developers, researchers, and businesses alike. Below is a curated list of some of the top open-source LLMs, each distinguished by its key characteristics, developer, and notable features.

Llama 3.: Developed by Meta AI, Llama 3.1 stands out in the open-source community, known for its scalability and high performance. Available in sizes up to 405 billion parameters, it rivals top proprietary models like GPT-4 in reasoning, coding, and multilingual text generation tasks. Its notable features include a 128,000-token context window, support for tool use, and an open license that allows fine-tuning and deployment across diverse platforms, making it a versatile choice for enterprise and research applications.
BERT: Introduced by Google in 2018, BERT (Bidirectional Encoder Representations from Transformers) remains a foundational open-source LLM. Its bidirectional approach to understanding sentence context excels in tasks like question answering, sentiment analysis, and text classification. Though smaller and older than newer models, its lightweight variants (e.g., DistilBERT) and extensive pre-trained options keep it relevant for resource-constrained environments and specialized NLP tasks.
Falcon 180B (or Falcon 2): Created by the Technology Innovation Institute (TII) in Abu Dhabi, Falcon 180B is one of the largest open-source LLMs, boasting 180 billion parameters. Trained on 3.5 trillion tokens, it shines in reasoning, coding, and knowledge-intensive tasks, often outperforming models like Llama 2. Its potential multimodality and commercial-friendly licensing make it a top pick for developers seeking robust, scalable solutions.
Mixtral 8x22B (or Mistral-8x22B): From Mistral AI, Mixtral 8x22B leverages a Mixture-of-Experts (MoE) architecture, using 39 billion active parameters out of 141 billion total to deliver efficiency and power. It excels in math, coding, and multilingual applications, with a 32,000-token context window. Its permissive Apache 2.0 license and sixfold faster inference speed compared to peers make it a favorite for performance-driven projects.
BLOOM: A collaborative effort led by BigScience, BLOOM is a multilingual LLM designed to support research and accessibility across 46 languages. With a 176-billion-parameter version, it prioritizes ethical AI development and transparency. Its strengths lie in text generation and cross-lingual tasks, making it ideal for global applications and academic exploration.
GPT-J: Developed by EleutherAI, GPT-J is a 6-billion-parameter model that offers solid performance with fewer resources. Modeled after early GPT architectures, it’s a practical choice for text generation, sentiment analysis, and research, particularly for users with limited computational power. Its simplicity and open availability keep it in use despite the rise of larger models.
Vicuna 13-B: From LMSYS, Vicuna 13-B is fine-tuned from Llama for conversational excellence. With 13 billion parameters, it delivers natural, engaging dialogue, making it a go-to for chatbots and customer service applications. Its ability to achieve over 90% of ChatGPT’s quality in evaluations highlights its prowess in human-like interaction.
Grok AI (or Grok 1.5): Built by xAI, Grok (with iterations like Grok 1.5) excels in text summarization and comprehension, potentially incorporating multimodal capabilities. Designed to provide helpful, truthful answers, it leverages real-time data integration (e.g., from X) and a 1-million-token context in later versions, positioning it as a unique tool for dynamic, knowledge-rich tasks.
Google Gemma 2: Google’s Gemma 2, part of the Gemini research lineage, is a lightweight, efficient LLM available in sizes like 9 billion and 27 billion parameters. Optimized for research and development, it performs well across hardware setups and supports tasks like coding and multimodal processing, reflecting Google’s focus on responsible AI innovation.
Command R+: Developed by Cohere, Command R+ is an open research version optimized for enterprise use, retrieval-augmented generation (RAG), and tool integration. With strong performance in structured tasks and a focus on practical deployment, it’s a valuable asset for businesses needing reliable, customizable AI solutions.
Qwen1.5: From Alibaba Cloud, Qwen1.5 offers a series of models in various sizes (e.g., 72 billion parameters), emphasizing multilingual and multimodal capabilities. It competes with top-tier models in cost-effectiveness and scalability, making it suitable for e-commerce, customer service, and global language processing.
GPT-NeoX: EleutherAI’s GPT-NeoX, with a 20-billion-parameter flagship, is a robust autoregressive model excelling in language understanding and generation. Its open-source nature and strong performance on creative tasks like story writing make it a staple for researchers and developers seeking a balance of power and accessibility.

Key Advantages of Utilizing Open-Source LLMs

LLMs have emerged as transformative tools and offer various benefits. Below are the key advantages that set open-source LLMs apart and drive their adoption across diverse sectors.

Accessibility: Open-source LLMs are accessible to everyone, democratizing advanced AI technology for developers, researchers, startups, and students alike. This broad access allows individuals and organizations of all sizes to experiment with and build innovative AI applications.
Cost-Effectiveness: They are generally free to use, with no licensing fees typically involved. The primary costs are associated with the hardware and computational resources needed for fine-tuning and deploying, making them more affordable than proprietary models. This saves organizations time and money by avoiding creating models from scratch.
Fine-tune Capability and Customization: Users can modify the models and fine-tune them with their own data to tailor them to specific needs, niche industries, and unique use cases, such as sentiment analysis, summarization, or chatbot development. This level of adaptability often surpasses the limitations of proprietary LLMs.
Transparency: The source code is publicly accessible, allowing users to read the code, understand how the model works, and often examine the training data and methodologies. This fosters trust, assists with audits, and helps ensure ethical and legal compliance.
Community Support: Open-source LLMs benefit from a global community of developers, researchers, and enthusiasts who actively contribute to their improvement by fixing bugs, offering help, and sharing advancements. This collaborative environment boosts efficiency and accelerates innovation.
Enhanced Data Security and Privacy: Users have complete control over the data processed by these models and can deploy them on their private infrastructure, ensuring sensitive information remains in-house and complies with data protection requirements.
Reduced Vendor Dependency: Organizations can avoid vendor lock-in scenarios where they are tied to a specific provider for updates, support, and future developments. They can use contributions from multiple service providers and internal teams.
Versatility: These models are adaptable tools for various industries and applications, supporting a broad range of natural language processing activities from translation and text generation to sentiment analysis and content creation.
Innovation Acceleration: By utilizing open-source language models, academics and businesses can focus on creating cutting-edge applications and solutions rather than rewriting the underlying language model, thus advancing the field of natural language processing (NLP).

Diverse Applications of Open Source LLMs

Open-source LLMs offer various applications across various domains, using their ability to understand and generate human-like text. Their accessibility and customizability make them versatile tools for numerous tasks.

Text Generation: Open-source LLMs can create diverse forms of written content, such as emails, blog posts, articles, and creative stories. Models like Falcon-40B can respond to prompts with high-quality text suggestions that can be refined. They can also automate content generation, such as writing product descriptions.
Code Generation: These models can assist developers by generating code and identifying errors and security-related faults in existing code.
Virtual Tutoring: Open-source LLMs enable the creation of personalized learning experiences that can be customized to individual learning styles.
Content Summarization: LLMs can efficiently summarize long articles, news stories, and research reports, making it easier to extract key information. BERT, for instance, is well-suited for summarization tasks. Grok AI is specifically designed to revolutionize text summarization and comprehension.
AI-Driven Chatbots: They are excellent for building chat applications and virtual assistants that can understand and answer questions, offer suggestions, and engage in natural language conversation. Vicuna 13-B is designed as a conversational specialist, feeling natural and almost human-like in interactions, making it great for customer support.
Sentiment Analysis: LLMs can analyze text to determine the emotional or sentiment tone, which is valuable for brand reputation management and analyzing customer feedback. BERT is particularly suitable for sentiment classification.
Content Filtering and Moderation: These models can identify and filter out inappropriate or harmful online content, contributing to a safer online environment.
Question Answering and Information Retrieval: Models like BERT, Falcon 180B, and Vicuna 13-B can be used to answer questions and streamline information retrieval.
Research and Analysis: Grok AI can aid researchers with swift insights from papers and support business planning with market data analysis.
Domain-Specific Applications: They can be fine-tuned for specific industries. For example, FinGPT was developed for the financial industry. Vision-to-language models like Falcon 2 11B VLM can interpret images to support applications across healthcare, finance, eCommerce, education, and legal sectors.

Challenges and Considerations of Open-Source LLMs

While open-source large language models offer numerous advantages, it is crucial to understand the challenges and considerations associated with their use.

Ethical and Responsible Use: Open-source LLMs can be used for harmful purposes due to their unrestricted access, such as misinformation or privacy violations. Ensuring their ethical and responsible use requires proactive community governance and clear guidelines.
Data Biases: Like all LLMs, open-source models can perpetuate or amplify biases present in their training data, leading to skewed outputs. Mitigating this issue requires vigilant oversight and diverse data curation to ensure fairness and inclusivity in AI-generated content and decisions.
Quality and Reliability: While fostering innovation, open community involvement can also introduce variability in the quality and reliability of contributions. Without standardized oversight, these models can yield inconsistent results, posing challenges in applications where accuracy is paramount. Ensuring high standards requires concerted efforts in vetting and refining contributions.
Need for Resources (Computational Demands): Training and deploying LLMs, including open-source ones, can be resource-intensive, requiring significant computational power and storage capacity. Even running smaller open-source models might necessitate access to GPUs or TPUs.
Skilled In-House Team: Effectively utilizing and customizing open-source LLMs requires an experienced in-house team with expertise in machine learning, natural language processing, and software development. Organizations without this talent may face deployment, fine-tuning, and maintenance difficulties.
Intellectual Property (IP) Concerns: Depending on the licensing of the open-source LLM and any modifications made, organizations may need to consider intellectual property concerns, especially regarding commercial use. Some licenses might restrict commercial applications.
Community Support Limitations: While community support is a strength, the level and responsiveness of support can vary compared to dedicated vendor support for proprietary models. Organizations might need to rely on forums and documentation, and bug fixes or feature requests might not be addressed as quickly as with commercial products.
Performance Compared to Proprietary Models: While many open-source LLMs are powerful, some proprietary models might offer superior performance and capabilities due to the significant resources invested by their creators. Organizations need to evaluate the performance of different models based on their specific use cases.

Conclusion

Open-source LLMs are AI models for understanding and generating human language with freely available code, allowing for modification and distribution. They offer significant benefits like cost-effectiveness, customization, transparency, and community support. However, challenges exist, including ethical use, data biases, and the need for computational resources and expertise.

Despite these, open-source LLMs are expected to become more advanced and specialized, focusing more on ethical considerations and accessibility and driving further innovation in AI. They are a democratizing force in AI, offering powerful tools for various applications.

Further Resources

FAQs

Q 1. Are there any open-source LLMs?

Yes, there are open-source LLMs (Large Language Models) like LLaMA, BLOOM, and OPT

Q 2. Are open-source LLMs secure?

Open-source LLMs can be secure if properly maintained and updated, but their security depends on the community’s involvement and the model’s specific implementation.

Q 3. Is LLM studio open-source?

LLM Studio is not inherently open-source; it’s a platform that can be used for various LLMs, including open-source ones.

Q 4. What are LLMs with examples?

LLMs are large language models that can process human-like language. Examples include language translation models, chatbots, and text summarization tools, such as LLaMA, BERT, and transformer models.

Q 5. What is the best LLM for coding?

The best LLM for coding depends on your specific needs, but popular options include models like CodeBERT, Codex, and GitHub Copilot, which are designed to assist with code completion, debugging, and suggestions.