The Best LLMs for Enhanced Language Processing in 2025

The tables below list large language models, their API providers, and key metrics for evaluating them for different use cases.

Quality overview

Cost and volumes overview

• Arena score is a performance metric used to evaluate and rank models based on their effectiveness in a competitive or benchmark setting.
• Context window represents the number of tokens the model can handle in a single session.
• Blended price is the average cost per million tokens.
• Input price is the cost of processing one million tokens sent as input to the model.
• Output price is the cost of generating one million tokens as a response from the model.
• Latency is the average time (in seconds) it takes for the model to process input and deliver output.

It's important to note that the models and providers listed in the tables are just a selection, and many more options are available in the market. For a more extended comparison, check the LLM API Providers Leaderboard and Chatbot Arena LLM Leaderboard

We understand that navigating these metrics can be complex, so you can contact our team for assistance in selecting the best model for your use case.

Challenges and limitations of LLM tools

Model bias and hallucinations

One important issue with LLMs is their tendency to "hallucinate." LLMs predict the next word in a sequence. This can make them sound believable, but they may generate false or nonsensical responses. This can be especially problematic in applications where accuracy is crucial. To avoid misinformation, users should verify LLMs' output with other sources.

• For instance, our data science engineers have encountered cases where models sometimes confused financial data from different companies. Even with instructions to admit uncertainty or missing data, the models still gave wrong answers. It shows how hard it is to ensure models provide accurate results in complex situations.

Input and output length limitations

Large language models are limited by the number of tokens they can process in a single instance. It restricts both the length of the input and the output. This limitation can be a challenge for processing long documents or generating detailed responses.

• Researchers are working on optimising models to process longer text sequences. In the meantime, users can break up lengthy inputs into smaller ones.

Limited multimodal capabilities

Most LLMs are focused on text and do not yet handle other forms of media effectively. Full integration across modalities is still developing.

• Large language models are being updated to handle both text and other media, like images or audio. Models like GPT-4 and Google Gemini are already starting to process multiple types of data, with plans for more advanced media handling in the future.

Vulnerability to misuse and ethical risks

LLM tools are also vulnerable to misuse. There are concerns about generated code vulnerabilities, contradictory suggestions from models, and unethical usage, such as using AI to cheat on exams or gain instructions on illegal activities. These issues highlight the need for careful oversight and regulation to prevent harmful or unintended uses of AI technologies.

Industry-specific applications of LLM tools

Healthcare

LLM-driven AI chatbot assistants in the healthcare industry help facilitate patient-doctor communication. These chatbots are being created for different fields, from helping patients and doctors communicate to improving internal processes. AI chatbots boost patient engagement, offer quick 24/7 assessments, reduce administrative tasks, and improve planning, thus making the work of healthcare providers more efficient and patient-centric.

Retail

LLMs analyse consumer behaviour in retail to improve marketing strategies and campaign precision. Building a chain of LLM-based agents that automates internal processes, from ordering and communication to hiring, significantly decreases operating costs.

Finance

LLMs act as financial advisors, tailoring investment recommendations and strategies based on customer preferences and historical trends. They also gather market data and expert opinions to generate actionable insights, helping financial institutions make informed investment decisions in the finance industry.

Media and entertainment

In the media and entertainment industry, SOTA (State-of-the-Art Large Language Models) LLMs are used to create personalised advertising and dynamically adjust the appearance of websites, apps, and marketing materials such as tailored ads and content for specific audiences. It leads to higher click-through rates (CTR) and improved engagement metrics.

Insurance

Personalised insurance products involve creating an LLM-based recommender system that combines underwriting policies with recognised consumption patterns and customer needs. This system analyses the limitations and possibilities of available policies and tailors recommendations to individual customers.

Automotive

LLM-based agents are used in the automotive industry for automated contractors' information search, filtering, and ranking based on usefulness and predefined conditions. This helps businesses find suppliers more efficiently and improve their internal processes. The automation allows for smoother negotiations and quicker RFQ preparation, ultimately leading to higher efficiency in operations.

Testing an LLM for healthcare: ELEKS case study

At ELEKS, we have developed a generative AI-powered solution for medical document summarisation. This solution aims to organise and manage large volumes of unstructured healthcare data.

Our team began by researching and selecting the task's best large language models (LLMs). We compared general-purpose models like GPT-3.5 and GPT-4 with specialised medical LLMs such as DHEIVER and MedLlama2.

We strictly adhered to HIPAA and GDPR regulations. We also implemented Optical Character Recognition (OCR) to convert unstructured medical documents into searchable text and a classification module to identify document types for targeted summarisation.

Our solution is built on a flexible tech stack. It uses Microsoft Azure and .NET to manage workflows and scalability. We refined the tool based on testing and feedback. We switched to GPT-4o to handle larger data volumes. Future upgrades include integrating the solution with electronic medical records (EMR) systems.

To learn more about our experience developing this innovative solution, read our full article: Generative AI in Healthcare: Solving Medical Staff Performance Issue

Future of LLMs

GPT-4 and Google's Gemini models are among the first LMMs to be widely deployed. Their full capabilities are still being rolled out.

However, in the near future, we will see more large language models (LLMs), especially from tech giants like Apple, Amazon, IBM, Intel, and NVIDIA. These models may be less known than some popular ones. Large companies will likely use them for internal tasks and customer support.

We may also see more efficient LLMs for smartphones and other lightweight devices. Google has already started this trend with Gemini Nano, which operates some features on the Google Pixel Pro 8. Similarly, Apple introduced Apple Intelligence.

Another trend is the rise of multimodal models that combine text generation with other media, including images and audio. These models will allow users to ask a chatbot about an image or receive an audio response.

Summary and final thoughts

Large Language Models (LLMs) are at the forefront of artificial intelligence. These models are changing how businesses and individuals interact with a language.

LLM APIs help organisations stay ahead in today's competitive landscape, improve user experiences, and automate routine tasks.

The future of LLMs looks bright as research continues to overcome their limitations. As we improve knowledge cutoffs, hallucinations, and multimodal skills, LLMs will evolve and help organisations be more productive and creative.