What Are the Best Open Source Large Language Models?

Large Language Models (LLMs) have become a significant part of natural language processing and artificial intelligence. Open source LLM provide opportunities for developers, researchers, and businesses to use, modify, and improve language models without the constraints of proprietary systems. This article explores some of the most notable open source LLMs available today, highlighting their features, strengths, and use cases.

The Rise of Open Source in the LLM Arena

While proprietary models from companies like OpenAI and Anthropic often grab headlines, the open-source community has been rapidly innovating, closing the performance gap and offering unparalleled flexibility. These models can be fine-tuned for specific tasks, run on-premise for enhanced data privacy, and are freely available for academic and commercial use, democratizing access to cutting-edge AI technology.

Here are some of the key players shaping the open-source LLM landscape.

1. Llama 2 & 3 (Meta AI)

Overview: Arguably the catalyst for the modern open-source LLM boom, Meta's Llama 2 (and its recent successor, Llama 3) set a new standard for what open-weight models can achieve. They are a family of models ranging from 7 billion to 70 billion parameters (with a massive 400B+ parameter version of Llama 3 on the horizon).

• Strengths: Llama 2/3 models are exceptionally strong all-purpose models. They excel in reasoning, code generation, and dialogue. Llama 3, in particular, represents a major leap forward, showcasing state-of-the-art performance in its class with significantly improved reasoning and instruction-following capabilities. They are released under a custom license that is free for most commercial use (with some restrictions for very large companies).
• Use Cases: Serving as a base for fine-tuning specialized models (e.g., for customer support, legal, or medical domains), powering research, and building commercial applications that require a powerful, general-purpose chatbot or assistant.

2. Mistral & Mixtral (Mistral AI)

Overview: This French startup has made waves by releasing incredibly powerful and efficient models. Their first model, Mistral 7B, punched well above its weight class. However, their true masterpiece is Mixtral 8x7B.

• Strengths: Mixtral is a Sparse Mixture of Experts (MoE) model. It has 47B total parameters but only uses about 13B during inference. This means it delivers the quality and knowledge of a much larger model at the speed and cost of a much smaller one. It is known for its excellent performance in reasoning, mathematics, and code generation, often outperforming Llama 2 70B.
• Use Cases: Ideal for applications where high throughput and low latency are critical without sacrificing quality. Perfect for cost-effective deployment of high-performance models in production environments.

3. BLOOM (BigScience Workshop)

Overview: Before the Llama era, BLOOM was a monumental achievement in collaborative open-source AI. It was created by the largest international collaboration of AI researchers ever assembled, with over 1,000 participants from more than 70 countries.

• Strengths: BLOOM's key differentiator is its multilingual focus. It was trained on a dataset covering 46 natural languages and 13 programming languages, making it exceptionally capable for tasks outside of English. It is one of the most transparent models regarding its training data.
• Use Cases: Perfect for multinational businesses, researchers, and developers building applications that require strong performance in languages other than English, such as translation, cross-lingual summarization, and non-English customer support.

4. Falcon (Technology Innovation Institute)

Overview: Developed by the Technology Innovation Institute (TII) in Abu Dhabi, the Falcon family of models, particularly the Falcon 40B and 180B, were designed to be top-tier, state-of-the-art models that could compete directly with the best available.

• Strengths: The Falcon models were trained on a massive, high-quality dataset (1 trillion tokens from curated web data) and are known for their strong reasoning and knowledge recall abilities. The Falcon 180B was a benchmark champion upon its release, rivaling even proprietary models like PaLM-2.
• Use Cases: Best suited for complex tasks that require deep knowledge and advanced reasoning, such as advanced research assistance, sophisticated dialogue systems, and complex code generation.

5. MPT (MosaicML / Databricks)

Overview: The MPT (Mosaic Pretrained Transformer) series was developed by MosaicML (now part of Databricks) with a clear focus on commercial use and easy deployment.

• Strengths: All MPT models are released under a permissive Apache 2.0 license, making them completely free for any commercial use. They are also optimized for fast training and inference. A key variant is MPT-30B-Instruct, which is fine-tuned for instruction-following and is known for its long context window (8k tokens, with versions extending to 65k+).
• Use Cases: An excellent default choice for businesses that require a commercially safe, robust, and deployable model for building customer-facing products, chatbots, and content generation tools.

Choosing the Right Model

The "best" open-source LLM depends entirely on your specific needs:

• For overall performance and a strong ecosystem: Llama 3 is the current frontrunner.
• For efficiency and speed: Mixtral offers an incredible performance-to-cost ratio.
• For commercial safety and permissive licensing: MPT is a top contender.
• For multilingual applications: BLOOM remains a powerful and purpose-built choice.

The Future is Open

The open-source LLM movement is moving at a breathtaking pace. These communities are not just replicating existing work but are driving innovation in model efficiency, fine-tuning techniques (like LoRA), and accessibility. By lowering the barriers to entry, open-source LLMs are ensuring that the future of AI is not just powerful, but also diverse, customizable, and accessible to all.