Llama vs. Alpaca LLM: What’s the Difference?

Although ChatGPT may be among the most well-known LLMs, it’s one of many options to choose from, with Llama and Alpaca among them. Selecting an LLM requires considering each option’s inherent strengths, features, and use cases. Examine Llama vs. Alpaca LLM details to learn more about each and begin figuring out which might be best suited to your needs. 

What is Llama LLM?

Meta first released its Large Language Model Meta AI (Llama), an LLM initially capable of handling parameters of up to 65 billion, in February 2023. While the initial goal was to support researchers in furthering their work in studying LLMs, it quickly generated a buzz that led to broader adoption among users and 30 million downloads within the first seven months of its release [1]. Meta released the first two of its Llama 4 “herd” in the spring of 2025, with added speed, lower cost, and multimodality, which provides a knowledge base comprised of more diverse training resources.

Large language models (LLMs), like OpenAI’s ChatGPT, can deal with billions of parameters and work in multiple languages to perform diverse tasks ranging from answering questions and making predictions to generating text and other content based on users’ input. These deep learning models contain layers of nodes, all connected and working in tandem, much like human neurons in the brain. At the heart of LLMs is a part of its architecture known as the transformer, which enables the technology to evaluate context and relevance for improved responses. They train on and can ingest vast quantities of data, giving way to multiple personal and professional use cases. 

What can you use Llama for?

Meta released Llama as part of its commitment to open-source access, which the technology company believes enhances safety and alignment since it provides access to everyone It also notes that you can find its value in three primary areas, including improved collaboration and the ability to draw from the research community to boost the speed at which it incorporates what they learn to continue making progress. As Llama’s usage expands, it also provides Meta with the data necessary to learn about potential use cases and provide a robust AI development ecosystem. 

You can use it on cloud-based platforms like Amazon Web Services or Google Cloud for greater platform accessibility and a chance to get more out of the platform’s functions. You can also use Llama as a base to innovate and develop Generative AI (GenAI) products and test features powered by LLM technology. 

Additional uses of Llama LLM include:

• Translate text into various languages, including English, Spanish, Italian, French, Hindi, Portuguese, German, and Thai

• Virtual assistant duties, including appointment scheduling, making recommendations, and answering questions

• Analyze extensive sets of data to glean insights and identify patterns

• Aid medical diagnoses and clinical decision-making in areas with low levels of medical resources

• Personalize educational content and training materials

• Explain complicated concepts in simple terms for improved understanding

• Use in tandem with video conferencing software for robust recaps of anything you miss if you step away during a meeting 

• Create content, generating design concepts, and composing music

Advantages of Llama

Llama excels in processing multiple images, visual reasoning, and image grounding, all of which aid in the LLM’s ability to understand user intent and deliver more relevant results. Additional benefits include the following: 

• More than 100 billion parameters for many models, with the still-to-launch Behemoth set for 2 trillion parameters

• Ability to understand videos, images, and text all at once

• Multi-lingual capability for text

• Capable of advanced math and science reasoning and code generation

• Includes a large token context window for easy processing of extensive content volumes 

• Content summarization and full text generation

• Foundation model for creating other LLMs and applications

Disadvantages of Llama

Because Llama is an open-source LLM, it poses potential security risks. Maintaining the security of users’ data and providing measures to block hackers and those seeking to use the technology for criminal or unethical purposes are paramount. Additionally, it’s vital that Meta maintains strict oversight of Llama’s various models to ensure the availability of adequate user support and ongoing, accurate results and answers. 

What is Alpaca LLM?

Artificial intelligence researchers at Stanford University developed Alpaca, an LLM reputed to rival the performance of the ChatGPT-3.5 model, for less than $600. The team fine-tuned Llama’s 7B model, which allowed them to use Meta’s Llama as a pre-trained model to work from and text-davinci-003 from OpenAI to generate its 52,000 instruction-following demonstrations. The result was a fine-tuned LLM with specialized use cases. 

What can you use Alpaca LLM for?

Three faculty members and five PhD students working out of Stanford University’s Center for Research on Foundation Models developed Alpaca using 52,000 of ChatGPT-3.5’s question-answering examples to fine-tune Llama’s 7B model for use in academic research only. That said, Alpaca is a promising LLM for creative endeavors. It offers streamlined capabilities for refining designs and allows you to:

• Create design concepts and refine them

• Generate digital art 

• Render images from sketches

• Revise your work with precision iterations

Advantages of Alpaca LLM

Alpaca has the distinction of being the first to fine-tune Llama, which helped it gain traction. Its source code remains public and accessible on platforms like GitHub and Hugging Face. 

• Well-written, concise outputs

• Speedy, efficient operation for rapid concept iterations in creative tasks

• Minimal necessary resources

• Efficient, maximized quality of outputs

Disadvantages of Alpaca LLM

One of the primary disadvantages may be Alpaca’s limitations, which include its academic-only use. Research also reveals that this LLM may be particularly prone to misinformation and hallucinations, a phenomenon that occurs when an AI model creates inaccurate responses and outputs. This increases the risks of inadvertently spreading misinformation. Stanford determined that the release of Alpaca would need further research and safety studies. 

Meta Llama vs. Stanford Alpaca: An open-source LLM comparison

Although both LLMs share a basic foundation, each differs in its ability to cater to users’ needs. While Llama offers powerful artificial intelligence (AI) capabilities that make it ideal for researchers, developers, and business users, Alpaca provides specialized use cases. For example, you can use both for creative tasks and research; however, Alpaca’s fine-tuning provides a broader range of features that make it ideally suited for digital design and experimenting with image style, texture, and composition. 

Other ways the two differ include the following:

• Llama provides increased flexibility and allows more modifications, making it ideal for everything from enterprise-level projects to small-scale niche uses

• Alpaca features less flexibility, but offers more personalized tools for creative endeavors, making it ideal for creative work requiring precise refinement

• Llama LLM allows for commercial use, although its license may include some restrictions

• Stanford stipulates that Alpaca’s use is strictly for research, prohibiting commercial use

• You can use Alpaca in the cloud; Llama works in the cloud, and on Windows, Mac, and Linux operating systems

• Llama features more than 70 integrations; Alpaca has fewer than 10 

• Alpaca works well in smaller projects, while Llama can scale to enterprise-levels as needed

Continue learning about large language models on Coursera

Although Llama and Alpaca both offer LLM capabilities to their users, Alpaca is a fine-tuned model that has more specialized use cases, while Llama offers a broader range of uses, including commercial use. Continue exploring LLMs and begin gaining practical skills on Coursera. 

For example, you can go from a beginner to job-ready with educational programs like the IBM AI Developer Professional Certificate, which gives you opportunities to build GenAI applications, explore foundational concepts, and develop in-demand AI skills. You can also explore GenAI and its wide-ranging uses in the five-course Generative AI Fundamentals Specialization from IBM. You’ll find these options and more than 10,000 other courses and programs on Coursera.