The Best LLMs for Enhanced Language Processing in 2025

Olha Zhydik

3 hours ago

Article

The Best LLMs for Enhanced Language Processing in 2025

Large Language Models (LLMs) have emerged as advanced artificial intelligence systems that can process and generate text with logical communication.

As a cornerstone of modern generative AI software development, LLMs often approach human-level proficiency across a variety of language-related tasks.

In this article, we'll overview top LLMs and their features, explore challenges and trends, and consider industry-specific applications of LLMs.

How LLMs work

Data collection

It starts with collecting a wide range of text from global sources, including books, research papers, news, and websites. Depending on the industry, the model can also train on various types of data organisations own, such as financial reports, customer behaviour data, patient records, equipment data, and even weather data. The more diverse the data, the better the model can learn.

Generally, LLMs have anywhere from 8 billion to 70 billion parameters and are trained on vast amounts of data. For example, Crawl, one of the largest datasets, includes web pages and information from the past decade, holding several petabytes of data.

Tokenisation

At this step, data is broken into tokens, words, or parts of words. In this way, the model processes and analyses the text.

Pre-training or knowledge distillation

In pre-training, the model learns by predicting the next token in a sequence and grasping language patterns, grammar, and word relationships. For example, given "The sky is," it predicts "blue." Using a transformer architecture, it processes tokens and applies self-attention to focus on the most important words in a sentence. This approach boosts the model's language skills and lets intelligent automation handle tasks with less human input.

On the other hand, Knowledge Distillation allows smaller models (like LLaMA or Mistral) to learn from larger and more complex models (like GPT-4). KD helps smaller models perform well with fewer resources. The smaller model is essentially "taught" by the larger one, which improves the smaller model's efficiency and performance while reducing its computational cost.

Fine-tuning

After pre-training, the model is fine-tuned for specific tasks like question answering or summarising text. This involves training the model on smaller, task-specific datasets. Fine-tuning helps the model specialise in particular tasks and improve its performance.

Inference

The model processes input, such as a question or prompt, and gives a relevant response. It understands language and context to provide accurate answers or generate text. Conversational AI systems, such as chatbots, use this process to interact meaningfully with users.

Response generation

The model creates text one token at a time, predicting each next token based on the input and its acquired knowledge. The output layer creates tokens and forms them into sentences. Methods like beam search are used to find the best and most coherent response.

For more insights into how generative AI is shaping the future of software development, check out this article: Expert Insights on Generative AI: Evolution, Challenges, and Future Trends

Top LLM models

1. GPT

GPT Models (OpenAI): OpenAI created the GPT series, which includes some of the most widely known and used language models.

GPT-3.5, the engine behind ChatGPT, builds on GPT-3 with improved learning from human feedback.

The latest GPT o3 processes both text and images. It has over 170 billion parameters, which makes it incredibly powerful for many tasks.

2. Gemini

Gemini is Google's family of large language models that can process text, images, and other media. The Gemini family includes different versions: Ultra (the largest and most capable), Pro (mid-tier), and Nano (efficient for on-device processing). Gemini 2.0 Flash builds on the success of 1.5 Flash, offering faster performance and even outperforming 1.5 Pro in key benchmarks.

In addition to handling multimodal inputs like images, video, and audio, 2.0 Flash supports new features like generating pictures mixed with text, steerable text-to-speech (TTS) multilingual audio and calling tools like Google Search, code execution, and third-party functions.

3. Claude

Claude is an LLM developed by Anthropic. It is built to focus on ethical and safe AI through constitutional AI principles. Claude 3.5 Sonnet is the latest iteration. It's designed to offer safer, more reliable interactions, especially for enterprise applications, and is available through platforms like Claude.ai and its iOS app.

4. Command

Command by Cohere blends real-time data with natural language generation to provide accurate, up-to-date responses. The Command R is built to scale, delivering fast, reliable results for complex tasks like customer support or content creation. Cohere's open-source approach lets users easily customise the models to fit their needs without being tied to a specific vendor. Command easily integrates with existing systems, helping businesses quickly innovate and stay competitive.

5. LLaMA

LLaMA (Large Language Model Meta AI) is Meta's series of open-source large language models. The latest version, LLaMA 3.1, was released in July 2024 and introduces an expanded context length of up to 128,000 tokens, multilingual support across eight languages, and improved reasoning and coding capabilities. LLaMA models range from 8 billion to 405 billion parameters. Meta emphasises accessibility and innovation, allowing developers to fine-tune these models for diverse applications while fostering collaboration in the AI community.

LLM APIs act as a communication channel between applications and the LLM models. With the help of APIs, developers don't need to understand the complexities of LLMs. Instead, developers interact with the API. They send text-based inputs and receive responses.

How LLM API works

Data transmission: The user provides a text input like a question or command. The application formats this input and transmits it to the LLM API.
Natural language processing by the LLM: Upon receiving the input, the API forwards it to the LLM model. The model processes the language.
API response generation: LLM generates an appropriate response, from simple facts to creative content.
Application integration: The response is returned to the app. It then integrates it into the user experience. This could mean showing the response on the screen, playing it as audio, or triggering actions in the app.

Key considerations for choosing the right LLM API

Before exploring the different language model providers, understand your project's needs.

What do you want the LLM to do? Think about the specific tasks it will handle.
Who will use it, and what do they need? Consider your audience and what they expect.
How much will you use it? Estimate how often you'll send requests to the API.
What's your budget? Decide how much money you can spend on monthly or yearly LLM services.

Narrow down your choices and focus on models that suit your needs. Then, you can compare the features and abilities of different LLMs to find the best fit.

The factors influencing the selection of the right large language model (LLM) begin with a clear understanding of the domain and the specific task. Beyond that, considerations such as the intended usage, the organisation's FinOps strategy, and the model's positioning within competitive arenas—like the Chatbot Arena or Language Model Arena—play a critical role. Choosing the right model is about its capabilities and aligning it with business goals, operational requirements, and cost-efficiency strategies to ensure optimal performance and scalability.

Volodymyr Getmanskyi

Head of Data Science at ELEKS

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

Quality overview

Model	API Providers	Arena Score	Latency (s)	Context Window
o1-preview	OpenAI	1334	23.57	128k
o1-mini	OpenAI	1306	9.44	128k
GPT-4o-2024-08-06	Microsoft Azure	1265	0.83	128k
Claude 3.5 Sonnet (20241022)	AWS	1283	1.01	200k
Claude 3 Opus	AWS	1248	1.61	200k
Claude 3 Haiku	Anthropic	1179	0.51	200k
Command R+ (04-2024)	Cohere	1190	0.32	128k
Llama-3.1-Nemotron-70B-Instruct	Nebius	1269	0.33	128k
Llama-3.3-70B-Instruct	Microsoft Azure	1256	0.44	128k
Gemini-1.5-Flash-002	Google (AI Studio)	1271	0.35	1m

Cost and volumes overview

Model	API Providers	Blended Price (USD/1m tokens)	Input Price (USD/1m tokens)	Output Price (USD/1m tokens)	Latency (s)
o1-preview	OpenAI	$26.25	$15.00	$60.00	23.57
o1-mini	OpenAI	$5.25	$3.00	$12.00	9.44
GPT-4o-2024-08-06	Microsoft Azure	$4.38	$2.50	$10.00	0.83
Claude 3.5 Sonnet (20241022)	AWS	$6.00	$3.00	$15.00	1.01
Claude 3 Opus	AWS	$30.00	$15.00	$75.00	1.61
Claude 3 Haiku	Anthropic	$0.50	$0.25	$1.25	0.51
Command R+ (04-2024)	Cohere	$6.00	$3.00	$15.00	0.32
Llama-3.1-Nemotron-70B-Instruct	Nebius	$0.20	$0.13	$0.40	0.33
Llama-3.3-70B-Instruct	Microsoft Azure	$0.71	$0.71	$0.71	0.44
Gemini-1.5-Flash-002	Google (AI Studio)	$0.13	$0.13	($0.30)	0.35

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.

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.

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.

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.

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.

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FAQs

Is ChatGPT is LLM?

Yes, ChatGPT is an AI-powered large language model. It uses deep learning and neural networks to let you have human-like conversations with a chatbot.

Is LLM free?

Yes, there are free options available! While many advanced large language models require payment, there are open-source models trained on extensive training data that can be used for free.

What are LLM apps?

LLM apps are applications that use large language models (LLMs) and AI models to perform various tasks, including language translation, content generation, and other language processing tasks. These apps are often built on the latest breakthroughs in AI research.

What are LLM model tools?

LLM model tools are software applications powered by advanced artificial intelligence models. These tools can understand and generate human-like text, as well as perform language processing tasks, and are the result of ongoing AI research.

Is Bert an LLM?

Yes, BERT was one of the first modern LLMs. It uses neural networks and deep learning, making it widely used and very successful.

What are the three features of a smart grid?

LLMs are a type of generative AI that focuses on creating text. Generative AI, however, can produce many types of outputs, including text, images, audio, and code.

What is conversational AI?

Conversational AI is a technology that allows computers to understand and respond to human language in real-time, often through chatbots or voice assistants, leveraging deep learning and extensive training data.

How do AI LLM models and machine learning work?

AI LLM models and machine learning use deep learning and neural networks to process language and perform language processing tasks, enabling accurate and natural conversations.