LLM Comparison: Which Foundation Model to Choose in 2025?

The idea of utilizing generative AI to enhance your application is quite a hot topic. It seems straightforward: use a large language model (LLM) API as the foundation for your advanced chatbot, smart assistant, or some other custom AI-powered tool. The LLM API providers like OpenAI, Anthropic, or other big names will do all the heavy lifting with the model, and all that's left is some tweaking and model training.

Well, spoiler, it's a lot harder than that. Comparing large language models will lead you to discover that they have various use cases and differ in capacity, output accuracy, pricing, and tons of other things. On this page, we look into the options available and compare LLMs to help you get a better understanding of the key factors to consider so that your integration is effective and in line with your unique business objectives.

What Is a Large Language Model (LLM)?

Let's start by noting that generative AI is a distinct segment of artificial intelligence and machine learning which puts the generation of new content in focus. It leverages deep learning models to identify patterns from existing datasets and produce original outputs such as text, images, code, and others as if a human would do it. This becomes possible thanks to the use of large language models (LLMs) and different deep learning frameworks trained on extensive datasets.

So, what exactly are large language models? Sometimes referred to as foundation models, LLMs are sophisticated AI systems capable of processing, analyzing, and generating natural language.

Importantly, they differ from conventional natural language processing (NLP) methods which depend on manually crafted and pre-written text interpretation rules. Unlike them, LLMs analyze vast amounts of text data to recognize language patterns. They utilize neural networks to understand word usage, context, and the relationships between words to form a language model and perform an array of tasks.

One of the most exciting aspects of such AI technologies is their ability to learn and self-improve over time. Trained on substantial text datasets, they grow in size and expand their ability to comprehend prompts created by humans, better adapting to their needs as time goes on.

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What Are LLM APIs and Where Are They Applied?

There are tons of LLM API use cases, but at their core, large language model API connections serve as a strong foundation for developing generative AI solutions. They're like the backbone linking your software or tech product to powerful AI systems.

In simpler terms, LLM APIs act as a bridge between your applications and the complex algorithms that drive LLMs. They provide the essential architecture and software environment for customizing, training, and deploying AI functionality.

If you integrate something like the GPT API into your app, your tech solutions and business ecosystems can tap into AI capabilities. For example, a medical laboratory that provides clinical blood test services can integrate an LLM to develop a chatbot that helps users interpret their blood test results. This chatbot could guide patients through understanding various markers, explain what each test measures, and answer common questions in real-time.

And the best part of AI integration into an app is that you don't have to be an expert in the billions of parameters forming the basis of these artificial intelligence models. In the case of the blood test clinic, they would need to train the selected LLM by "feeding" it with unique and tailored data about blood tests, including specific test names, reference ranges, potential health implications, and common patient concerns to enable the chatbot to provide relevant, accurate, and personalized responses to users.

Why else is this awesome for various business applications? First off, such a comprehensive toolkit has a high level of customizability which allows developers to build more complex and tailored solutions on top of existing ones. This means you can tweak and fine-tune LLMs to cater to specific domains or tasks, making them more functional for your use cases, specialized contexts, or diverse industry needs. Topping that, LLM APIs can simultaneously handle large volumes of requests, so they're highly scalable.

How exactly are they applied to empower software solutions and apps? Generating unique content like images and text, as well as engaging in conversations is their strength. That is exactly why they're used for creating numerous solutions, including:

• Generating content (creating all kinds of human-like text for writing purposes or content production like marketing copy, social media posts, articles, and so on);
• Chatbots, assistants, and customer support (conversational AI makes dialogues and real-time interaction with users possible, as such technology can manage frequently asked questions and give replies to queries);
• Analyzing sentiment (this ability helps dig into customer insights and spot market trends, facilitating data-driven decision-making);
• Translating languages (such AI also helps localize websites and translate text, broadening horizons to reach new global audiences);
• Games and entertainment (LLMs can assist in creating lots of things for games, including character dialogues).

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Popular LLM Providers

Who are LLM API providers and what do they do? Basically, these are companies offering access to LLMs via integrations through application programming interfaces. Providers work on advancing their core technologies, consistently releasing newer and more powerful solutions catered to certain use cases or industries. Let's overview the most well-known providers that bring the world some of the best LLM API solutions.

OpenAI — perhaps the most well-known leading provider that's recognized globally for its contributions to AI. Its groundbreaking and powerful GPT series excels in natural language processing tasks and generating text, which is why they're widely adopted across various industries for building AI chatbots, content creation tools, and more.

Google — a notable provider of several LLMs, including Gemini, PaLM, and Bard. They are integrated into the Google ecosystem, making them available in their services and suitable for building apps designed for natural language understanding tasks like translating or producing text.

Meta — another well-established figure in the LLM API landscape, it puts emphasis on social media interaction and generating content, so their solutions are frequently used for marketing purposes and user engagement.

Anthropic ​​— this established provider of conversational AI solutions is noted for putting security and safety in the spotlight, as well as their incline toward responsible AI usage. 

Mistral — a relatively new provider that's been gaining attention for creating scalable LLMs with high efficiency and innovative approaches. This makes it a favored choice among those who want to integrate AI that'll have low latency and high performance.

AssemblyAI — this provider's priorities are speech recognition and transcription, allowing people to convert audio data into text and get insights from spoken language, making them useful in areas like customer service, education, and media.

‍DeepSeek AI — a currently hyping player in the industry with Chinese roots offers a more cost-effective and faster alternative to GPT, its solutions are currently thought of as the best free LLM that is distinguished for its chatbot and multilingual text generation capabilities.

Which AI Models and LLMs Are Common?

Now that the most common LLM vendors out there are clear, let's go over the specific foundation models that can be used as a basis for equipping your app or software with AI.

‍GPT models — Generative Pre-trained Transformer models by OpenAI can handle an array of language processing tasks like making content, writing and summarizing text, or communicating as a chatbot, which is why it's commonly used for creating AI assistants.

DALL-E — another OpenAI solution built for creatively generating unique images based on prompts and textual descriptions, which can be used for forming visual concepts, design purposes, and even making art.

Gemini — a series of AI models brought by Google DeepMind that dive into advanced reasoning and comprehending context, they can generate code, write essays, translate text, and a lot more.

PaLM — the Pathways Language Model is another one designed by Google, which can deliver coherent and contextually relevant text and learn from diverse data sources, making its use for education and research highly popular.

Bard — one more Google model that's aimed at a conversational experience for users, giving creative and informative responses is in focus, leading to use cases like personal recommendations or storytelling.

Llama — the Large Language Model Meta AI is another solution often applied for academic cases and research, it's computationally efficient, scalable, and produces high-quality outputs, which is why it's considered by many as the best open source LLM in this respect.

Claude — developed by Anthropic and named after Claude Shannon, it's a safety-focused model with a heavy emphasis on ethical AI use that's suitable for sensitive applications that are considerate to humans.

Mistral — this one shifts attention to delivering high-performance, scalable, and top-quality large language models that are easy to integrate into various software solutions.

LeMur — stands for Leveraging Large Language Models to Understand Recognized Speech and is provided by AssemblyAI, it zooms in on spoken data and transcribed speech and is used for text summarization and solutions that answer questions.

deepseek — an advanced and customizable AI solution by DeepSeek AI that's able to comprehend and generate text in multiple languages, its multilingual support and contextual understanding are useful for conversational AI applications and when tailoring the solution to business needs, making it one of the top open source LLMs.

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Pros and Cons of LLM Usage

No LLM models comparison would be complete without an understanding of the advantages and cons of applying foundation models. Next, we'll look into cases when to use LLM APIs and which obstacles you may encounter.

Benefits of Using an LLM API

Choosing in favor of an LLM API like GPT, Gemini, Mistral, or others we noted above can significantly enhance applications and tech products, here's what you can expect:

• You power your solution with AI (LLM APIs let you link up generative AI functionality to boost your application or tech product, be it a chatbot, virtual assistant, text generation tool, language translator, or something else you need);
• There are lots of ways to use them (there are practically no limits to how you can apply the LLM, meaning you can automate customer support, boost user satisfaction, and come up with lots of other AI solution use cases and contexts to fit your business needs and enhance your app or tech solution);
• They can be customized (LLMs are typically flexible and can be molded to solve certain tasks or cater to specific domains once you fine-tune them and give them large enough datasets to work with to improve accuracy and relevance);
• Integrations are faster than custom AI model creation (obviously, tweaking a ready-made LLM API is much quicker than creating your own AI solution from scratch, instead you use the pre-trained models and embed them into existing applications, reducing delivery time and avoiding the complexities of fundamental AI model training);
• They're capable of improving (LLMs can learn from new data, and you can retrain and fine-tune them to enhance their capabilities);
• They're scalable (depending on the infrastructure provided by the API, LLMs can cope with large volumes of requests at the same time, accommodating to increased traffic without lagging or harming performance);
• They get ongoing updates from the providers (LLM providers like OpenAI do the heavy lifting and hand-holding, regularly releasing advancements like performance boosts, security enhancements, or new features, so whenever there's an update, you automatically get a hold of it too).

LLM Challanges and Limitations 

Without any doubt, they are powerful tools that can visibly enhance applications, yet they come with notable disadvantages and possible obstacles. Here are a few worth keeping in mind:

• Most advanced LLMs aren't free (surely, using an LLM is much more cost-effective than building your own AI model, yet the majority of LLMs, including GPT, Gemini, and PaLM are available on a paid subscription basis);
• Usage can get costly (what you pay for having the LLM linked up is determined by your usage, so if you have high volumes of API calls, it might be pricey);
• Requires developer input (regardless of whether you want to link up the best open source LLM model or use a close-sourced one, you will need skilled developers to handle the integration and its consequent fine-tuning);
• Needs extensive datasets (if you want the gen AI solution to bring back value, you have to prepare high-quality sets of data to train it, which can be time-consuming);
• You rely on the providers (matters like security, sensitive data processing, performance, and even possible pricing changes create a dependency on the providers).

How to Choose the Optimal LLM Solution

Many business owners find the idea of integrating generative AI capabilities into their apps tempting. Whether you're looking into GPT, Gemini, Mistral, or other options and aren't sure how to compare LLMs, this list of factors can help you determine which LLM engines are worth your time.

1. Define Your Business Use Case

Before you rush off to compare LLMs, clarify what you'd like to achieve with the help of generative AI. Do you need a powerful chatbot to communicate with your users and unburden your customer support team? Could you use assistance with feedback processing and customer sentiment insights? Are you seeking a helper to reduce the time a specific team spends on research and brainstorming?

Whatever the case, think about which primary goal you have for the LLM. Different models are designed for different tasks, say, DALL-E is a good choice for image-related work but won't be helpful with text or conversational AI. So, mark which tasks you want the LLM to perform and how much domain-specific knowledge will be necessary to train it to bring value.

2. Evaluate Model Size and Performance

If a model is only emerging, bigger and more established players might have more to offer. But a larger size doesn't necessarily guarantee that you're choosing something better. Even in nature, larger creatures tend to be slower than smaller ones, which in this scenario might as well mean more precise accuracy at the cost of slower response times.

Without any doubt, you have to pay attention to performance, accuracy, and tech capabilities when you compare LLM models. Mind every API's speed and how capable it is to generate relevant outputs. Are they good at comprehending context? Can they support multiple languages and dialects? A good piece of advice here is to give it a test run to try to understand its performance in realistic circumstances for more accurate LLM size comparison results.

3. Customization and Fine-Tuning

The majority of those linking up LLM APIs are doing so to power their app with a worthwhile solution. Say, a round-the-clock assistant capable of answering even the toughest customer questions as if a manager would. Either way, this has to be a business-specific tool if the aim is to bring value, and that's only achievable if it's possible to transform a given generic kit into something niche-focused and precise.

The freedom to tailor the solution as you need and how flexible it is are also notable points. Your comparison of LLM models has to factor in the opportunities to adapt the model's outputs to fit your domain.

Remember the blood test clinic example we brought up? Well, not every LLM API could be capable of coping with such difficult tasks, and the effort and time required to train it as well as the size of necessary custom datasets will also differ. Say, Llama is among the best opensource LLM options, so it can give you more room for maneuver but will mean you might need more technical expertise.

4. The Scalability of LLM APIs 

Your product or app will likely pick up steam after a while. The linked-up LLM API has to be able to handle the growing volumes. You should decide early on whether you want the API to handle high request volumes without significant latency. If so, then you must definitely seek an API that can scale seamlessly with your needs. Also, note its ability to maintain consistent performance even if traffic spikes and add it to your LLM comparison.

5. Ease of Integration

You'll need experienced developers who know the generative AI ropes either way. They'll be the ones to make the integration happen. However, assessing how easily the API can integrate into your existing infrastructure is surely not a point you'd want to skip in your LLM models comparison.

If the infrastructure isn't compatible, it'll take extra effort to try to make the integration work with no certainty that it'll last. The availability of clear documentation, as well as SDKs, and possibly even modular APIs are some of the points regarding the minimization of development efforts to keep an eye out on.

6. Evaluate the Ratio of Cost and Performance

Ignoring the budget when dealing with LLM APIs is a rookie mistake. For starters, you have to understand that some providers charge based on token usage or API calls, while others offer a variety of subscription-based packages.

Choosing the cheapest LLM API to test the waters can possibly be a strategy that'll end up costing you double. Free cheese, you know?

Even if you think you found the best free LLM API, you have to consider the potential tradeoff in performance and the quality you get for what you pay. Compare pricing tiers across providers, perhaps you can find better value for the workload you have in mind than you would using solutions by OpenAI, Google, or others.

Overall, smaller models can be a good fit for lightweight tasks and can help you cut costs. Plus, you need to look far into the horizon and the long-term ROI. What if an API that's a bit more expensive will deliver higher-quality outputs to save you time and money down the road? Moreover, it's never a bad idea to optimize the usage of your tokens, after you're through with initial setups, going back and trying to improve it can save you cash.

7. Available Support and Ecosystem

If an LLM API has a dedicated customer support system in place to help you in case something goes wrong, this is a good sign. They can help your business manage its account, resolve issues if something happens, and keep you in the loop of new releases.

Likewise, if the community is active, especially on the developers' end, this is another noteworthy point for LLM comparisons. It can signal that there are enough learning resources, tutorials, or people available to keep this "machine" going.

8. Features and Language Support

Lastly, the feature set on offer is another factor to mind while you compare LLM models. Does this provider's API solution have what you need to bring your idea to life? Are there extra advanced tools available in the API ecosystem to make your life easier? Can some of this readily available functionality save you months wasted on custom tweaks?

Some solutions have tons of things available out of the box. These could be code generation tools or some multi-modal inputs for images or text tools that can speed up the delivery of something for your use case.

A primary example regards language support. Not all LLM APIs are equal in terms of how well they work in different language sets. Hence, if something like multilingual support or translation matters to you, this could make a difference.

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A Comparison of LLMs

But which LLM is the best? Artificial Analysis compared more than 100 LLM endpoints, ranking the performance of various solutions, and placed their findings into an AI provider leadboard. Various versions of models of GPT, Llama, Gemini, Claude, Mistral, DeepSeek, and others made their closed-source and open source LLM comparison.

They compare LLMs side by side, noting such factors as latency, price (in USD per 1 million tokens), output speed and quality, licenses, context windows, and various features. Take a look at a fragment of the LLM model comparison table below for a quick overview.

As you see, even the different versions of a given model vary in terms of their capabilities and use cases. For instance, the size of the context window can hint at how well a model can process extensive input data sizes, so if you aim for something like long-form content creation or a solution for analyzing massive documents (such as legal ones), then a large context window should be a vital point in your LLM comparison. Moreover, pricing varies greatly, with some models turning out really expensive or super cheap based on the use case, therefore, if you're very cost-sensitive, mind this evaluation aspect.

Either way, you need a detailed LLM model performance comparison to find out what's a better fit for you. Here are a few major takeaways from the LLM model comparisons above.

• As such, Llama models are typically noted among the best open source LLM models valued for being rather efficient and cost-effective. They're suitable for apps like real-time data processing where speed and scalability matter and super high-quality outputs aren't in focus.
• If you're seeking to balance performance with affordability Mistral's lightweight architecture and high-speed performance are often highlighted. These models are a good choice for chatbots or if low latency and fast token generation are crucial, as well as for environments with limited computational resources.
• What's for GPT models like GPT-4 and GPT-4 Turbo, these stand out if high-quality outputs, accuracy, and versatility are your priority. If your tasks emphasize creativity or the need for in-depth and complex reasoning, these models could be a good choice. However, this may not be a viable option if your application is high-volume or low-margin.
• In case sensitive data handling and ethical outputs are an area of your concern, then Claude's models can be a good fit. They typically have large context windows and are useful for conversational tools that can lead in-depth discussions, as well as for industries like healthcare, customer service, and education.
• Google's Gemini models are also popular, multimodal capabilities are their strength, so those with creativity needs commonly opt for them. And if you're leveraging the Google ecosystem, it integrates naturally.
• The DeepSeek models are gaining attention recently as well since they're outperforming many of the established solutions. Consider them if your app requires something research-intensive, good at reasoning, linguistic variety, and outputs that are context-aware.

Concluding Thoughts on the LLM Model Comparison

Foundation models like Gemini, GPT, Llama, and others allow you to build upon the LLM's architecture and access AI capabilities. But they differ in many ways, including pricing, degree of customizability, difficulty in integration, performance speed, output accuracy, operational efficiency, and so on. When comparing LLMs, you have to evaluate not only the tech peculiarities but the overall fit of the solution for your case.

If you're seeking a hand with the tech side and need someone experienced to handle your LLM API integration, Upsilon's team augmentation services can be a good fit. Feel free to reach out to us to discuss which generative AI you'd like to add to your application or existing solution, our pros can advise on which model to choose and how to approach the process most optimally in terms of your budget and business case.