10 Best Large Language Models (LLMs) in 2025

Language Models (LLMs) recognize human-like text patterns, translate languages, predict textual outcomes, and independently generate coherent and contextually relevant content.

Whether you want to enhance communication, automate content creation, or derive insights from vast textual data, LLMs automate repetitive tasks.

However, there are plenty of LLMs by OpenAI, Meta, Microsoft, Google, and other companies in the market. Each LLM has varying functionalities and multiple use cases, making it difficult to choose the right model.

We’ve compiled 10 large language models to help you choose the best one for your business needs. Let’s examine their features, benefits, and limitations.

10 Best Large Language Models (LLMs) in 2024

What Should You Look for In Large Language Models?

When selecting a large language model for natural language processing tasks, choose one that aligns with your scope and strategic goals. Here are the key capabilities to guide your choice:

Integration compatibility: The foundation models must be compatible with your existing technology stack, such as CRM, ERP, or custom apps. Seamless compatibility ensures streamlined processes and data flow without drastic modifications
Ease of use: LLM should be easy to use for different team members with varying technical expertise. It should have an intuitive interface with resources that can reduce the learning curve
Scalability: The model should be able to handle huge volumes of training data without a drop in performance
Language support: LLM should have multilingual and multi-dialect capabilities to scale business operations in different geographical locations
Cost-effectiveness: The total cost of ownership, including initial expenses, maintenance, and upgrades, should suit your budget
Customization: You should be able to tailor the models specific to your business needs
Data privacy: The model should have advanced data security and privacy features to protect your personal and confidential business information

The 10 Best Large Language Models to Use

1. GPT-4

GPT-4 is the latest iteration of OpenAI’s generative, pre-trained, transformer-based language model series. It generates human-like responses based on simple text prompts and natural language processing.

GPT-4 is a versatile AI tool that can perform technical and creative tasks such as composing songs, generating summaries, and preparing business reports. Users can also add images for classification and generate captions.

It can churn up to 25,000 words, making it suitable for long-form content creation.

GPT-4 best features

Automate content creation, summarization, translation, idea generation, coding, customer support, and other tasks
Process both text and image inputs simultaneously
Create frameworks for other apps or chatbots with cross-platform compatibility and API integration
Generate content free of harmful biases with its enhanced training methodologies

GPT-4 limitations

It can give biased responses at times
GPT-4 sometimes provides inaccurate data, especially related to the latest trends and events
Integrating it can be complex as it requires substantial IT infrastructure and expertise

GPT-4 pricing

Custom pricing

GPT-4 ratings and reviews

G2: 4.5/5 (30+ reviews)
Capterra: 4.7/5 (15+ reviews)

2. PaLM

PaLM (Pathways Language Model), developed by Google, is a significant step forward in AI and natural language processing technologies. It is trained on diverse datasets and can easily handle complex reasoning tasks such as coding, classification, and translation.

PaLM 2, the upgraded version of PaLM can be used for research and integrated with product applications.

PaLM best features

Perform nuanced tasks more accurately with PaLM’s exceptional language understanding capabilities
Scale more flexibly and efficiently with PaLM (built on Google’s Pathways system) without needing task-specific models
Reduce operational complexity and complete multiple tasks simultaneously with PaLM’s single model instance
Leverage its superior reasoning abilities in scenarios requiring logical deduction, problem-solving, and decision-making

PaLM limitations

Like other large models, PaLM requires substantial computational resources for training and inference, which creates a barrier for smaller entities or individual developers
Integrating PaLM with legacy technologies can be challenging and requires significant development effort
Being a newer and highly advanced model, PaLM’s accessibility is limited to organizations with the infrastructure and budget to support its implementation

PaLM pricing

Custom pricing

PaLM ratings and reviews

G2: Not available
Capterra: Not available

3. BERT

BERT (Bidirectional Encoder Representations from Transformers) is a machine learning (ML) model for natural language processing (NLP) developed by Google.

It is a bidirectional (can analyze text from both left and right) and unsupervised language representation algorithm that can analyze large volumes of datasets and train machine learning models easily.

You can use BERT for NLP tasks such as translation, sentence classification, and sentiment analysis.

BERT best features

Train the machine learning model on your textual data
Get better contextual results with BERT as it uses bi-directional context representation. It processes text from right to left and left to right, interpreting based on all surrounding words
Perform versatile tasks with BERT, including sentiment analysis, named entity recognition, and question-answering
Fine-tune it with just one additional output layer to create state-of-the-art models for various tasks. This significantly reduces the time and resources required for model training
Use its multilingual version that supports 104 languages, making it applicable in global applications where multiple language processing is required

BERT limitations

BERT is computationally expensive due to its size and complexity. It requires GPU resources for training and inference, creating integration challenges for organizations with limited technical infrastructure
Despite being bi-directional, BERT’s understanding is limited to 512 tokens within a context window
Its legacy version will be discontinued after January 31, 2025

BERT pricing

BERT is open-source and freely available under the Apache 2.0 license.

BERT ratings and reviews

G2: Not available
Capterra: Not available

4. Claude

Claude is an innovative large language model developed and trained by Anthropic using Constitutional AI. It is known for its ethical AI focus on being safe, accurate, and secure while generating human language.

Its ability to provide contextually appropriate responses makes Claude suitable for training conversational AI applications.

Claude can perform advanced reasoning tasks beyond pattern recognition or text generation. It can also transcribe and analyze handwritten notes, photos, and static images. Its other capabilities include code generation and multilingual processing.

Claude best features

Use Claude 3 to process roughly 30 pages of text per second. It can read elaborate research papers or large contracts faster than its peers
Easily integrate Claude into your existing technical stack without deep technical expertise
Ensure consistent tone and style in customer interactions by conversational AI with Claude
Use Claude to extract information from business emails or summarize survey responses

Claude limitations

Claude’s uses only the English language, limiting its applicability in global markets
You can not create text and images on Claude
Although Claude was trained on huge data, it sometimes generated inaccurate responses

Claude pricing

Business and Scale: Custom pricing
Free: 0
Pro: $20 per person per month
Team: $30 per person per month (minimum 5 people)

Claude ratings and reviews

G2: 4.7/5 (20+ reviews)
Capterra: 4.8/5 (4 reviews)

Looking for support on coding? Here’s a list of best AI tools for competitor analysis

5. Falcon

Falcon is a language model created by the Technology Innovation Institute. It was developed for various complex natural language processing tasks and trained using 40 billion parameters and one trillion tokens.

Falcon integrates the latest advancements in AI to enhance language understanding and generation.

Falcon best features

Generate coherent, context-aware text that closely mimics human writing style with Falcon
Do quicker decoding with minimal quality degradation with Falcon’s ability to reduce memory bandwidth
Deploy NLP solutions across global markets with Falcon’s ability to support multiple languages

Falcon limitations

It requires substantial computational resources for optimal operation, reducing its accessibility to smaller organizations with limited IT infrastructure
Integrating Falcon into existing systems can be technically demanding

Falcon pricing

Custom pricing

Falcon ratings and reviews

G2: Not available
Capterra: Not available

6. ERNIE

ERNIE (Enhanced Representation through Knowledge Integration), developed by Baidu, integrates structured knowledge graphs into language model training, enhancing its understanding of complex contexts.

ERNIE can process and understand language through immediate context and integrating external knowledge structures. It can continue to learn and adapt after its initial training, allowing for improvements over time as it is exposed to new data.

Ernie best features

Use it for applications requiring cross-lingual understanding because Ernie supports multiple languages
Perform a broad spectrum of NLP tasks, including sentiment analysis, text classification, and more, with ERNIE’s enriched training with knowledge graphs

Ernie limitations

Integrating ERNIE into existing systems, especially those not already AI-ready, is challenging
Its pre-training on specific knowledge graphs might limit its effectiveness or relevance in niche industries

Ernie pricing

Custom pricing

Ernie ratings and reviews

G2: Not available
Capterra: Not available

7. Cohere

Cohere is an enterprise AI platform that helps businesses integrate GenerativeAI into their daily processes, such as document search, discovery, and retrieval in over 100 languages.

It enables organizations to advance their GenerativeAI model from proof of concept to the production stage, helping them build scalable and efficient AI applications.

Cohere best features

Cohere stands out for its user-friendly API, which makes it accessible even to those with limited technical expertise
Cohere offers excellent scalability, catering to businesses of all sizes, from startups to large enterprises
Cohere allows users to fine-tune models on their own data, enabling more personalized and accurate responses tailored to specific business needs and contexts
The company emphasizes ethical AI development, providing transparency in how its models are trained

Cohere limitations

Like many AI models, Cohere’s performance relies heavily on the training data quality
While Cohere is accessible, its cost can escalate quickly for high-volume users
Although improving, Cohere’s support for languages other than English is not as extensive as some other models

Cohere pricing

Free
Default model
- Command R+: Input: $3/1M Tokens; Output: $15/1M Tokens
- Command R: Input: $0.5/1M Tokens; Output: $1.5/1M Tokens
Fine-tuned model
- Command R: Input: $2/1M Tokens; Output: $4/1M Tokens; Training: $8/1M Tokens

Cohere ratings and reviews

G2: Not available
Capterra: Not available

8. Gemini

Gemini (formerly Bard), by Google, is a large language model that handles various complex natural language processing tasks. It is known for versatility and high performance as it aims to provide advanced AI capabilities across multiple domains.

The model has been trained on an extensive dataset, enabling it to understand and generate text with high accuracy and context sensitivity. Gemini is optimized for real-time applications, providing quick responses necessary for customer service bots, real-time translations, and other interactive applications.

Gemini best features

Perform NLP tasks, including text generation, sentiment analysis, summarization, and language translation
Fine-tune Gemini on specific datasets, allowing greater customization to cater to niche requirements or particular business needs

Gemini limitations

Integrating Gemini into your existing software systems can be complex, requiring technical expertise
While Gemini supports multiple languages, its performance can vary significantly between languages
It cannot generate images

Gemini pricing

Free
Pay-as-you-go: Input: $7/1M Tokens; Output: $21/1M Tokens

Gemini ratings and reviews

G2: 4.5/5 (100+ reviews)
Capterra: Not available

Want more of these? Check out the best AI tools for developers

9. LlaMA

LlaMA (Large Language Model Meta AI), by Meta, is primarily built for developers and researchers to facilitate innovation. However, it can also perform other complex tasks like translation and dialogue generation.

It also creates codes and natural language about code from prompts.

LlaMA best features

Perform NLP tasks such as text generation, comprehension, summarization, and translation
Built as an open-source large language model (LLM) designed for developers, researchers, and businesses to build, experiment, and responsibly scale their generative AI ideas
Generate code and natural language prompts with Llama

LLaMA limitations

It takes 30-120 seconds to generate a response, which is delayed compared to other tools
Setting up and customizing LLaMA, particularly for specific or advanced uses, may require considerable technical expertise in machine learning and NLP
The availability of ready-to-use, pre-trained models may be limited, increasing the effort needed to get started

LLaMA pricing

Free or Open Access for Researchers

LLaMA ratings and reviews

G2: Not available
Capterra: 4.0/5 (1 review)

10. Orca

Microsoft developed Orca for small language models (~10B parameters or less). It is based on self-improvement and feedback-driven methodology.

Orca creates synthetic data for training small models, providing them with better reasoning capabilities and custom behaviors.

Orca best features

Use Orca for text summarization and complex question-answering tasks
Give smaller language models enhanced reasoning as Orca imitates reasoning processes of larger models with explanation tuning
Utilize pre-trained on diverse data sources across different domains, from legal and medical to entertainment and finance
Fine-tune Orca on specific datasets, allowing the model to adapt to unique industry needs or specialized applications
Use newer algorithms that optimize processing power, reducing the energy consumption typically associated with running large language models, making it more sustainable and cost-effective

Orca limitations

Orca requires significant computational resources for optimal performance, particularly in data-heavy environments
Integrating Orca into existing systems can be a complex process requiring technical expertise, especially in systems not already AI-enabled
Orca’s capabilities in less commonly used languages is limited

Orca pricing

Advisory: $1,325 per month (3-month contract)
LLM Subscription: $2,200+ per month (12-month contract)
Enterprise: $27,500+ per month (with a flexible blend of services)

Orca ratings and reviews

G2: Not available
Capterra: Not available

If you’re lost on the AI terminology, use the ultimate AI glossary

Automate Tasks with ClickUp

Language learning models make content creation, communication, and translation easier but require high computational power and heavy investment, especially for enterprise-level tasks.

ClickUp offers user-friendly tools to automate business tasks and streamline workflows.Its AI integration, ClickUp Brain, generates content, summarizes reports, and automates task management to improve organizational efficiency.

Everything you need to stay organized and get work done.

Contact Sales