How to choose the right LLM

Why use LLMs in 2025?

Many organizations have already tried traditional machine learning models like decision trees, classification or keyword-based systems. In most cases, the results are not good, especially when dealing with complex tasks.

This is where LLMs win. The main difference is that LLMs are trained on massive datasets and have deep contextual understanding in comparison with ML. That means they don’t just process text, they actually understand it. What exactly can they do?

• Generate reports or summaries from any data
• Powering intelligent chatbots and virtual agents
• Automating email or message responses
• Enhancing customer support workflows
• Detecting anomalies or summarizing legal documents

For companies, this means faster processing times, better customer experiences and smarter automation, exactly what matters.

How to evaluate and choose the right LLM

Before choosing an LLM vendor or model, the best advice is to try it first. Every model has different strengths. The key is to measure the results: speed, accuracy, compliance and cost.

Below is a practical framework that will help you evaluate options, avoid unnecessary costs and select an LLM approach that aligns with your business priorities.

Step 1: Start with the fundamentals

Before diving into integration, ask:

Do you really need an LLM?
An LLM is a major commitment, not a simple plugin. If a simpler, rule-based algorithm can solve your problem, it will always be cheaper, faster and more reliable.

Are you ready for ongoing management?
An LLM is not a one-time setup. It requires a dedicated team for integration, ongoing monitoring and management (LLM-Ops) to handle performance, costs and outputs.

Step 2: Defining your integration strategy

Once you've confirmed an LLM is the right approach, the next step is to choose how to integrate it. There are three main options, each with its own balance of cost, control and complexity.

The first is to use an API. This is the fastest way to start, you use a pre-trained model from providers like OpenAI or Google. It's best for rapid prototyping, general tasks or standard chatbots. Setup is easy and you get access to top models. The downside is that costs can grow fast and sending data to a third party can create privacy risks. But in most of the case prompting or building a RAG system with LLM API is the best way to solve your problem. And privacy can be managed if you pay a license or self-host your model.

The second option is to fine-tune a model. You adapt an open-source model to your needs by training it on your own data. This gives better accuracy for your specific use case and allows self-hosting for privacy. But it also requires more technical resources & costs of maintenance. It must be considered for very specific tasks.

The third is to build from scratch. This means developing a foundational model from the ground up, including data collection and large-scale training. It’s a massive investment in time, infrastructure, and expertise. Realistically, this is only feasible for a few large tech companies. For most organizations, it’s not necessary or practical.

In a more simple way: use an API if you want to move fast, fine-tune if you need control and build from scratch only if you're building the next OpenAI.

Step 3: Compare and evaluate models

Once the strategy is selected, the next step is to evaluate specific models based on your business use case.

• Context Window (memory): This is the amount of information the model can process at once. A large window is essential for complex tasks involving long documents, but it comes at a higher cost and needs more time to answer. As of september 2025, most models provide at least a 128k tokens context window. Google models can accept up to 2M tokens!

The complete Harry Potter saga counts approximately 1.6M tokens.

• Performance & Benchmarks: Start with standard tests like MMLU, but don’t stop there. Create custom prompts based on your real tasks to measure relevance and reliability.
• Model Size and Speed: Bigger isn’t always better. A smaller, optimized model may perform better (and cheaper) on your use case than a massive generic one.
• Multimodality Needs: If your applications involve not just text, but also images, voice, or video, you’ll need a model that supports multimodal input. Otherwise, stick to a more efficient text-only LLM.
• Realtime models: a new series of models that can answer in realtime, often designed for speech to speech cases, they can work in a range of real-time applications. Those systems provide timely and accurate outputs.
• Thinking models:  As of september 2025 almost all new models are thinking models. It means they 'think' before answering. It leads to better answers, but it increases the cost and the latency.

All in all, selecting an LLM is a strategic process. Here are some simple key steps to follow: 

The field of AI is evolving rapidly. An iterative approach like starting small, measuring results and staying flexible, is the most effective way to navigate the complexities of LLM adoption and deliver measurable business value.

The price of LLMs-dropping fast

Just a year ago, deploying an LLM at scale seemed expensive and out of reach for most enterprises. But now?

• Open-source models like Mistral, LLaMA 3 or the last GPT-OSS are free to use and highly performant, making them a strong option for organizations that want flexibility and control without licensing fees.
• API pricing (like OpenAI’s GPT or Google’s Gemini) has dropped significantly, with flexible plans. This means companies can start small and scale gradually, without committing to large upfront investments.
• Quantized versions -optimized versions of LLMs that require fewer resources- can run efficiently even on mid-range servers. This significantly reduces the cost of infrastructure and makes on-premise deployments viable for more organizations, including those in highly regulated industries like finance or insurance. Even 1 bit LLM exists, it’s very light but still “smart” depending on your usage. However, the less bit is used, the dummiest it is.

These trends make LLMs more accessible than ever, it is easier than ever to integrate them using SDKs like adk, langchain etc. For many companies, LLMs are now a practical and strategic tool.

It’s not about the name, it’s about the fit

When it comes to LLMs, there is no one-size that fits all solution. What really matters in choosing a LLM is to fit your use cases, your data needs and constraints. Can it solve your business problem? Can it integrate securely into your environment? Can it scale as you grow? The right LLM is the one that delivers results, not the one that is in trend at that moment.