The Best LLMs in 2025: Top 5 Models Compared for Real-World Use

Introduction

Large Language Models (LLMs) have evolved far beyond simple text generators. Today, they’re powering entire workflows, writing code, analyzing data, and even interpreting images and audio. 

Globally, over 300 million companies are in operation. Recent reports suggest that nearly 67% of organisations are now using generative AI tools powered by LLMs to work with human language and produce content.

But that’s merely scratching the surface. From automating customer service and powering conversational agents to assisting in code generation, data analysis, compliance monitoring, and decision support, modern LLMs can change the way businesses operate.

With major players like OpenAI, Google, Meta, and Anthropic releasing increasingly advanced models, it’s becoming harder to determine which LLM best suits your specific needs. 

This article offers a clear, unbiased look at the 5 best Large Language Models in 2025:

• ChatGPT-4o (OpenAI)
• Gemini 1.5 Pro (Google)
• Claude 3.5 Sonnet (Anthropic)
• DeepSeek-V3 (DeepSeek)
• Llama 3.3 70B (Meta)

But first, let’s briefly explore what LLMs are and how they work.

What Are LLMs?

LLMs, or Large Language Models, are advance AI systems designed to understand and generate human-like text. They’re trained using deep learning techniques on massive datasets, ranging from books and academic articles to websites and more. Today, LLMs are behind many tools you use every day, like chatbots, coding assistants, content generators, and even AI-enhanced search engines.

How Do LLMs Work?

At their core, LLMs work by predicting the next word in a sentence, but at a massive scale. They are based on a type of deep neural network architecture known as a transformer, which allows them to process and retain long-range context, understand grammar, and follow complex instructions. 

During training, the model learns linguistic patterns, structures, and semantic relationships. When deployed (also called "inference"), it applies this learning to generate responses, answer questions, write code, summarize content, and much more – all based on the input provided by the user.

1. ChatGPT-4o (OpenAI)

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OpenAI has been at the forefront of large language model development since the release of GPT-2. One of the latest flagship models, ChatGPT-4o (where “o” stands for omni), marks a significant step forward in multimodal AI, offering real-time interaction across text, vision, and audio, all within a single, unified model.

Unlike earlier iterations, such as GPT-3, which relied heavily on different models for different modalities, ChatGPT-4o is trained end-to-end across multiple formats, making it much faster and more fluid in its responses.

Key Features

1. Voice/Audio Processing: GPT-4o responds to voice input with an average of 320 milliseconds, almost as fast as a human. It offers near-real-time, natural dialogue with emotional tone and fluid speech. While it currently uses fixed voices per language, OpenAI plans to add more voice options soon.

2. High Context Window: Supports context windows of up to 128K tokens, allowing for deeper conversations, document processing, and code analysis.

3. Memory Features (opt-in): Can remember prior interactions to personalize responses, useful for recurring tasks or long-term engagements.

4. Live Tools Integration: In the ChatGPT product, users can switch between:

• Code Interpreter (Advanced Data Analysis)
• DALL·E for image generation/editing
• Browse with Bing for live web access

Safety & Alignment

GPT-4o is built with safety at its core, featuring filtered training data, refined post-training behavior, and external testing by over 70 experts to ensure it stays within Medium risk levels across critical categories like cybersecurity, persuasion, and model autonomy.

Use Cases

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1. Creative Work: Used by designers and writers for storyboarding, drafting, image editing, and prototyping. 

2. Data Analysis & Visualization: In-built code interpreter supports spreadsheet analysis, chart creation, and Python code execution.

3. Customer Support: With its contextual memory and fast response time, it's widely used in AI chatbots.

4. Education & Tutoring: The model can interpret images (like handwritten math problems) and explain concepts with voice responses, suitable for interactive learning.

5. Accessibility Tools: Its speech and vision capabilities are being tested for use by individuals with visual or motor impairments.

Benchmark Performance

GPT-4o delivers strong performance across multiple benchmarks. It scores 88.7% in MMLU (language understanding), 76.6% in MATH (arithmetic skills), 53.6% in GPQA (graduate-level reasoning), and 90.2% in HumanEval (coding tasks). The model also supports real-time data processing and broader use cases, reflecting its advanced capabilities.

Pros 

• Multimodal: Accepts and generates text, audio, image (real-time interaction).
• Fast response: As fast as 232ms, close to human speed.
• Cheaper: 50% lower API cost than GPT-4 Turbo.
• Smarter: Matches GPT-4 Turbo on text/coding; better at audio, vision, and non-English.
• Unified model: Processes everything in one network – more accurate & expressive.
• Supports emotion, tone, and multi-speaker audio.

Cons

• No video output yet (input supported).
• May misinterpret complex audio cues in real-world noise.
• Requires Plus plan or API use for GPT-4o access.

ChatGPT-4o stands out for its speed, multimodality, and versatility, particularly in scenarios that require real-time interactions, coding help, or document understanding. However, users looking for open-source transparency, offline deployment, or model customization may find other options more aligned with their needs. As with any LLM, the ideal use depends on your specific priorities, performance, access control, budget, or domain-specific needs.

2. Gemini 1.5 Pro (Google)

Google's Gemini 1.5 Pro is part of its cutting-edge Gemini family, which represents a significant leap in AI capabilities. The Gemini models bring state-of-the-art performance across a wide variety of tasks, including text generation, image recognition, and even multimodal functionalities.

Gemini 1.5 Pro, in particular, focuses on achieving unified multimodal processing with enhanced reasoning power and a highly refined natural language understanding. It is designed for both enterprise-scale applications and personal use, providing versatility in everything from customer support to creative tasks.

Key Features

• 1 Million Token Context Window: Gemini Pro 1.5 offers a context window of 1,048,576 tokens, which is nearly 8x larger than GPT-4 Turbo’s 128K tokens. This allows it to process entire books, large codebases, or extensive research notes in a single prompt.
  
  
• Full-Context Utilization: Unlike other LLMs, which lose coherence as prompt size increases, Gemini effectively uses the entire context window, maintaining detail and relevance across lengthy inputs.
  
  
• Multimodal Input Support: Accepts text, code, images, audio, and video, making it ideal for complex tasks across formats (though output is still text-only).
  
  
• Google Search Grounding: Integration with Google Search allows the model to retrieve real-time, grounded information, improving factual accuracy and reducing hallucinations.
  
  
• Batch Prediction & Function Calling: Includes advanced features like batch processing and calling external functions, making it attractive for enterprise use.

Safety & Alignment

Due to its design, Gemini better handles large and nuanced inputs, reducing the risk of misinterpretation or biased outputs seen in smaller context models. Grounding with Google search helps reduce hallucinations, although it’s not immune (as demonstrated in its fabricated quote anecdote). This tool offers system instructions, controlled generation, and context caching.

Use Cases

• Literature Analysis: Can read and summarize entire novels, identify specific scenes, and offer detailed literary interpretation without chunking.
  
  
• Code Understanding and Writing: Analyzes whole codebases, identifies integration points, and writes usable sample code, an asset for dev teams.
  
  
• Research & Knowledge Work: Processes entire libraries of user notes (like Readwise highlights), providing insights, summaries, and personal-context generation.
  
  
• Enterprise AI Assistants: Ideal for legal document parsing, technical manuals, financial reports, and customer service, where large-context understanding is key.

Benchmark Performance

Google has released a benchmark report covering the Gemini 1.5 Pro’s improvements in math, coding, and multimodal capabilities since the initial release. 

Pros 

• Massive Context Window: Unmatched ability to ingest long documents or datasets.
  
  
• Strong Multimodal Input Support: Can handle video, audio, PDFs, and images simultaneously.
  
  
• Reduced Need for RAG: Handles complex tasks without retrieval-augmented generation, simplifying development and reducing time to production.
  
  
• Effective Context Utilization: Maintains performance even with massive prompts.
  
  
• Developer & Enterprise Ready: Supports function calling, grounding, and batching.

Cons

• Hallucination Risk Still Exists: Despite grounding, still susceptible to factual errors, such as fabricated quotes.
  
  
• No Code Execution: Unlike some models, it doesn’t support executing code directly.

Gemini 1.5 Pro excels in multimodal capabilities, enterprise applications, and advanced reasoning tasks. Its seamless integration with Google's existing services makes it a strong contender for businesses already embedded in the Google ecosystem. However, its premium pricing and limited open-source access might be a barrier for some users. 

3. Claude 3.5 Sonnet (Anthropic)

Anthropic’s Claude 3.5 Sonnet is part of the Claude model family, which is known for its focus on AI alignment, safety, and robustness. The Claude models have been designed to address concerns around AI ethics, including reducing harmful outputs and promoting more responsible AI use. Claude 3.5 Sonnet offers an excellent balance between speed and quality, making it suitable for a wide range of use cases.

Key Features

• 200K token context window, ideal for handling long documents, multi-step workflows, or dense data.
  
  
• Artifacts, a Claude.ai feature that generates editable content like code, graphs, and documents in a side panel for real-time collaboration.
  
  
• State-of-the-art vision capabilities, excelling in interpreting charts, graphs, and transcribing text from imperfect images.
  
  
• Strong agentic coding capabilities, solving 64% of real-world software problems in internal benchmarks.
  
  
• Better natural language understanding, humor detection, and writing quality compared to previous models.

Safety & Alignment

Claude 3.5 Sonnet remains at AI Safety Level 2 (ASL-2), the same as its predecessor, meaning it is suitable for general-purpose tasks but not high-risk decision-making.

Key safety highlights:

• Underwent red teaming and external audits, including evaluations by the UK’s Artificial Intelligence Safety Institute (UK AISI), with results shared with the US AISI.
  
  
• Integrated feedback from subject matter experts (e.g., Thorn for child safety) to fine-tune its misuse detection.
  
  
• User-submitted data is not used for training unless explicit permission is granted.

Use Cases 

• Visual Presentations: Need to turn data into visuals? Claude 3.5 Sonnet can help you do that effortlessly.
  
  
• Coding Assistant: If you're a developer trying to fix bugs or write tests, for example, for a feature that crops images into circles for user profile pictures, Claude 3.5 Sonnet can help. It understands code well and can offer practical support in debugging and improving your logic.
  
  
• Writing Support: Anthropic also pitches Claude 3.5 Sonnet as a creative writing partner. 

Benchmark Performance

In an internal evaluation, Claude 3.5 Sonnet showed major improvements in agentic coding tasks over its predecessor, Claude 3 Opus. It can write, edit, and run code independently, making it well-suited for code migration and legacy updates.

Pros

• High performance at cost-effective pricing
• Massive 200K token context window
• Fast response times (2x Claude 3 Opus)
• State-of-the-art image and chart interpretation
• Strong agentic behavior in coding tasks
• Safe, privacy-focused, and externally validated

Cons

• Free tier has low usage limits (often hit after ~10 prompts)
• Artifacts is only available on Claude.ai (not API-integrated yet)
• Still under ASL-2, so not recommended for high-stakes applications

Claude 3.5 Sonnet is one of the most balanced AI models currently available in terms of price, performance, and usability. With industry-leading reasoning and coding abilities, enhanced vision, and the dynamic Artifacts interface, it’s a serious competitor to GPT-4o and Gemini 1.5 Pro.

4. DeepSeek-V3

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DeepSeek-V3 is an advanced Large Language Model (LLM) designed with a focus on task-specific precision, contextual understanding, and performance efficiency. Unlike some more general-purpose models, DeepSeek-V3 excels in producing highly accurate results for users who require specialized outputs across various domains, such as data analysis, image generation, translation, and personalized recommendations. The model is especially effective in environments where both speed and accuracy are critical.

Key Features

• 671B MoE Architecture: DeepSeek-V3 employs a Mixture-of-Experts (MoE) design with 671 billion total parameters, of which only 37 billion are activated per token, significantly reducing compute demands while preserving accuracy.
  
  
• Trained on 14.8 Trillion Tokens: Trained on an enormous and diverse dataset, which powers its broad domain understanding.
  
  
• 128K Context Window: Ideal for processing long documents, complex conversations, or intricate codebases.
  
  
• Multi-Token Prediction: Enhances both performance and inference speed.
  
  
• 60 Tokens/Second Inference: About 3x faster than DeepSeek v2, making it one of the fastest LLMs at scale.
  
  
  

Safety & Alignment

DeepSeek v3 benefits from stable training with no catastrophic loss spikes, indicating robust alignment stability under normal prompts.

However, as an open-source frontier model, its safety depends heavily on how it's deployed. Without strong (Reinforcement Learning from Human Feedback) or guardrails, custom deployments could expose misuse risks. Enterprises may need to layer custom filters, toxicity screening, or moderation systems when deploying, especially in sensitive applications.

Use Cases

• Code Generation: On par with Claude 3.5 Sonnet in code-specific tasks; ideal for development environments.
  
  
• Mathematical Reasoning: Shows strong results on math benchmarks, suggesting suitability for STEM tutoring, education tools, and technical writing.
  
  
• Multilingual Support: Can handle tasks in multiple languages with high fluency and contextual accuracy.
  
  
• Long-Form Document Analysis: Thanks to its 128K context window, it is particularly strong at handling long inputs, legal documents, books, and research papers.
  
  
• Custom Fine-Tuning: As it's open-source, researchers and startups can fine-tune it for niche use cases such as chatbots, assistants, or domain-specific agents.

Benchmark Performance

Pros 

• Excels in coding, mathematics, and multilingual benchmarks – comparable to GPT-4o and Claude 3.5 Sonnet.
  
  
• Freely available for use, modification, and self-hosting with transparent licensing.
  
  
• Full model weights, papers, and training details are available for research and experimentation.

Cons

• As an open-source model, it can be deployed without safeguards, increasing the risk of misuse in unmoderated environments.
  
  
• Being new, it lacks the extensive plugin ecosystem and third-party integrations that models from OpenAI or Anthropic enjoy.
  
  
• Despite efficient inference, running it locally still demands high-end infrastructure.
  
  
• Fine-tuning and alignment mechanisms aren’t as well-documented, which may limit its reliability in sensitive or edge-case conversations.
  
  

DeepSeek-V3 is a powerful, performance-optimised LLM built for developers, researchers, and enterprise users with specific, high-precision needs. Its open-source nature, strong coding and reasoning capabilities, and efficient architecture make it an attractive option for those seeking customisable, task-oriented AI.

However, its deployment requires careful consideration around infrastructure, safety protocols, and tooling gaps.

5. Llama 3.3 70B (Meta)

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Llama 3.3 70B is a powerful large language model developed by Meta AI, representing the high end of the Llama 3 family. With 70 billion parameters, it delivers strong performance in natural language understanding, reasoning, coding, and multilingual applications. Llama 3.3 70B is built with efficiency in mind and can be deployed both in the cloud and on high-end local infrastructure.

Key Features

• Model Size: 70 billion parameters.
  
  
• Architecture: Transformer-based with Grouped Query Attention (GQA) for speed and efficiency.
  
  
• Media Support: Text-only (no image, video, or audio support).
  
  
• Multilingual: Supports 8 languages, including English, Spanish, Hindi, and German.

Use Cases

• Multilingual Chatbots: Ideal for lightweight customer support agents or education tools.
  
  
• Coding Assistants: Strong performance on HumanEval (88.4), capable of code completion, debugging, and boilerplate generation.
  
  
• Synthetic Data Generation: Great for generating large-scale text datasets for model pretraining or testing.
  
  
• Research & Prototyping: Efficient enough to run locally for academic NLP and AI R&D.
  
  
• RAG Applications: Can be integrated into Retrieval-Augmented Generation workflows for domain-specific Q&A.

Benchmark Performance

The Meta team conducted their own benchmark evaluation:

Pros 

• Strong reasoning, coding, and multilingual fluency
• Open weights: available for commercial and research use
• Efficient architecture: GQA enables better scalability and deployment
• Excellent HumanEval score for dev-focused applications

Cons

• Text-only: No image, vision, or multimodal capabilities.
• Still trails top-tier closed models (GPT-4, Claude 3.5) in some benchmarks.
• Alignment safety is not as tight as proprietary models.

Llama 3.3 70B provides advanced capabilities in areas requiring high-performance reasoning, long-form content generation, and multilingual support. While it is especially powerful in tasks that demand complex understanding and multilingual communication, its resource-intensive nature and pricing structure may make it more suited for enterprise-level use or high-demand applications. For businesses that need to handle cross-lingual content generation, long-form writing, or complex problem-solving, Llama 3.3 70B is a great choice.

Conclusion

Choosing the right LLM isn’t just about chasing the highest benchmark scores – it’s about finding the model that aligns best with your specific goals, whether that’s generating accurate code, solving complex math problems, handling multilingual queries, or offering reliable instruction following. 

At KnackLabs, we help businesses and teams make sense of the evolving AI landscape. Whether you're looking to integrate the right LLM into your product, optimize workflows with AI, or build something custom, we've got you covered. Let’s talk about how we can bring your AI vision to life.

FAQs

What is the main difference between ChatGPT-4o and Gemini 1.5 Pro?
ChatGPT-4o focuses on conversational AI, making it ideal for dynamic and natural exchanges. In contrast, Gemini 1.5 Pro excels in scalability and integration with Google Cloud, offering high-end capabilities for data-driven tasks and advanced AI systems.

Which model is best for multilingual content generation?
Llama 3.3 70B (Meta) is highly suited for multilingual tasks, thanks to its robust support for multiple languages.

Is DeepSeek-V3 better for natural language processing tasks?
DeepSeek-V3 specializes in AI-assisted search and data retrieval, making it better suited for tasks related to search engines or knowledge base systems rather than conversational or creative writing tasks.

How do I choose between Claude 3.5 Sonnet and ChatGPT-4o?
Choose Claude 3.5 Sonnet if safety, alignment, and structured outputs are your top priorities. It’s ideal for sensitive applications and high-stakes industries. Opt for ChatGPT-4o if you need a versatile, multimodal assistant that balances performance, creativity, and speed across a wide range of general-use tasks.

Can these models be integrated into existing applications?
Yes, all these models offer API access, allowing seamless integration into web apps, chatbots, enterprise software, and more. Depending on the model, custom fine-tuning may be available for specific use cases or industries.