The launch of ChatGPT in November 2022 started a race to build the most capable GenAI models. In 2025, this race has reached a new level as tech giants are vying to create large language models (LLMS) that are bigger, faster, and far more capable than anything we’ve seen before. 

Meta, Anthropic, Alibaba, and emerging open-source teams are pushing the boundaries with multimodal understanding, massive context windows, and cost-efficient deployments.  

Each of their models brings something unique to the table. Whether it’s GPT-4o’s real-time multimodality, LLaMA-4’s open-source might, Claude 3.7’s hybrid reasoning, Qwen’s multilingual open models, DeepSeek’s record-breaking scale, or Mistral’s enterprise- friendly efficiency. 

These models are unique in their own ways and can handle gargantuan workloads. Some of them are open-source or cost only a fraction, which gives AI development services, developers, and researchers the keys to innovation. 

This article will provide you with a comprehensive comparison of these latest LLMs, their capabilities, and how you can use them. 

What are LLMs? 

Large language models are AI systems designed to understand and generate human-like text. They’re trained on massive datasets which allow them to recognize patterns and create coherent, contextually relevant text. 

Their training data is gathered from public sources, such as: 

• Websites 
• Online articles 
• Code repositories 
• Research papers 
• Online conversations 

New paradigms like Model Context Protocols (MCPs) are also emerging to optimize how models engage with external systems and data. Using this data, AI developers apply deep learning techniques to enable LLMs to process and understand relationships between words and phrases in a way that mimics human comprehension. As a result, LLMs can write essays, summarize research, draft legal documents, debug code, and even hold a conversation.  

What’s more, LLMs are no longer limited to just reading text. Thanks to advances in multimodal training, many can also analyze images, understand spoken audio, and process video content. 

How do LLMs work? 

To put it simply, LLMs operate by predicting the next word in a sequence. This seemingly simple task is powered by complex deep learning architectures called neural networks. These networks learn the relationships between words, phrases, and concepts by parsing billions of lines of training data. 

For example, after seeing millions of sentences, an LLM learns that ‘cat’ is often followed by ‘sits’ or ‘purrs.’ It doesn’t understand in the human sense, but it becomes incredibly adept at recognizing patterns and probabilities. 

Transformer neural network: A paradigm shift 

The transformer architecture, introduced in 2017, revolutionized how LLMs processed language. Before the transformer architecture, LLMs relied on neural networks like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory).  

RNNs struggled to retain information from earlier parts of a sentence or paragraph. If a key detail appeared 20 words ago, the model often forgot it. LSTMs tried to fix this by adding a gating mechanism that helped the model decide which information to keep or discard. However, there were still flaws: LSTMs had slow computation because the model processed each word sequentially and struggled with complex interactions between words.  

Transformer architecture solved these bottlenecks by dropping the whole idea of sequential word processing. Instead, it introduced a mechanism called ‘attention’, which allowed every word in the input to look at every other word at the same time. 

This means it can focus on relevant parts of the input, even if they are far apart in the text. This ability to consider long-range dependencies is crucial for generating coherent and contextually appropriate responses, a core requirement in many enterprise-level GenAI services built on transformer-based models.

All the latest LLMs, including the ones mentioned in this article, are built using the transformer neural network. 

Why are LLMs important for AI? 

LLMs have quickly become one of the most influential breakthroughs in AI. They are the engine behind AI chatbots, content tools, smart agents, and automated coders. These advancements also tie into the growing popularity of AI agents, especially in automation and orchestration use cases.They’re changing how we search for information, how we work, and how businesses operate. 

More importantly, LLMs are a gateway to broader AI-powered developments. Their capabilities have sparked a new wave of custom AI solutions, AI software development, and enterprise AI solutions. 

5 leading LLMs in the market 

Now, let’s look at the top five latest LLMs that are setting the tone for everyone else. 

Meta Llama 4 

Meta made waves on April 6, 2025, by releasing the Llama 4 family of open models. Llama 4 models include Scout, Maverick, and the upcoming Behemoth, each designed for different deployment scales and uses. All Llama 4 models share the Mixture-of-Experts (MoE) architecture, a design that allows them to activate only a portion of their parameters for any given task, which makes them highly efficient.  

Meta made waves on April 6, 2025, by releasing the Llama 4 family of open models. Llama 4 models include Scout, Maverick, and the upcoming Behemoth, each designed for different deployment scales and uses. All Llama 4 models share the Mixture-of-Experts (MoE) architecture, a design that allows them to activate only a portion of their parameters for any given task, which makes them highly efficient.  

Technical Highlights

Maverick features 400B total parameters with 17B active at inference, while Scout is smaller with around 109B total parameters and 7B active parameters. Both models are natively multimodal, trained to process text, images, and videos jointly. Maverick supports an enormous 1 million token context window, ideal for very long documents and complex reasoning, whereas Scout stretches this further with a staggering 10 million token context for ultra-long inputs. 

Performance

Maverick delivers top-tier results in image reasoning (73.4% MMMU), coding (43.4% LiveCodeBench), and general knowledge benchmarks, outperforming models like OpenAI’s GPT-4o and Google Gemini 2.0 on many tasks. 

Unique Strengths

Meta Llama 4 models are open-weight, granting researchers and enterprises full access to the model weights for customization with native multimodal abilities, understanding text, images, and video simultaneously. These capabilities are especially relevant for businesses building tailored solutions in areas like Conversational AI, where multimodal reasoning and domain-specific fine-tuning play a critical role.These models handle advanced reasoning in science and math and can generate creative text and application code. 

How to access Llama 4

Llama 4 is free so you won’t have to pay Meta, but you must apply for access and agree to Meta’s license terms. It is released under a custom license that means users can download, run, and modify it for specific use cases. However, it isn’t open-source as full training data and methodology are not disclosed, and certain commercial use cases are restricted. 

Mistral Medium 3 

Mistral, a French startup, introduced Mistral Medium 3, an enterprise-grade LLM that competes with much larger models through its smart, efficient design. It is a dark horse in the race that offers 90% of its competitors’ performance at a much lower cost. 

Technical Highlights

Mistral Medium 3 is a frontier-class multimodal LLM designed for efficiency. While Mistral hasn’t publicly disclosed the exact parameter count, it’s described as a “Medium-sized” model – likely on the order of 20B parameters. It supports a 128K token context window, enabling long inputs. It is natively multimodal, meaning it can process images alongside text. 

Performance

Medium 3 delivers frontier-class results, achieving about 90% of Claude 3.7 Sonnet on benchmarks across the board at 8x lower cost ($0.4 input / $2 output per M token). It matches or beats top models like GPT-4o and Llama 4 Maverick on coding benchmarks (HumanEval, LiveCode), reasoning, and multimodal tasks (DocVQA). Its efficiency lets it run on as few as 4 GPUs, making it highly accessible. 

Unique Strengths

Mistral Medium 3 introduces a new class of models that balances SOTA performance with 8x lower cost. It excels in coding and multimodal understanding, making it ideal for enterprise use. With simpler deployment options, it supports hybrid or on-premises/in-VPC deployment, custom post-training, and easy integration into enterprise systems, making it a versatile and cost-effective solution for businesses. 

How to access

Mistral has launched this model under a commercial license, which means you cannot download or fine-tune it independently. Mistral Medium 3 is available through paid APIs or enterprise licensing. 

DeepSeek-V3-0324 

DeepSeek-V3-0324 is a cutting-edge Mixture-of-Experts (MoE) large language model developed by DeepSeek, a Chinese AI research startup. This model boasts 671 billion total parameters, with 37 billion activated per token, making it one of the most powerful open-weight models available. 

Technical Highlights

DeepSeek-V3-0324 is pre-trained on 14.8 trillion high-quality tokens. It offers impressive efficiency, requiring just 2.788 million H800 GPU hours—significantly less than similar models. With a 128K token context window, it can process large inputs. The model achieves state-of-the-art performance across various benchmarks like MMLU-Pro, GPQA, and LiveCodeBench, positioning it as a competitive force in AI, and increasingly viable for web-based integrations and deployments.

Performance

DeepSeek-V3-0324 has achieved a notable ranking of 5th place on the LMArena leaderboard, outperforming its predecessor, DeepSeek R1, and other open models. It achieved an increase in MMLU- Pro from 75.9% to 81.2%, GPQA improved from 59.1% to 68.4%, AIME rose from 39.6% to 59.4%, and LiveCodeBench enhanced from 39.2% to 49.2%. 

Unique Strengths

DeepSeek-V3-0324 excels in multilingual understanding, code generation, and complex reasoning tasks. Its open-source nature and efficient deployment make it highly accessible for both research and real- world applications. 

How to access

The model is open-weight and is freely available. Users can download and use the model weights for both research and commercial purposes. 

Claude 3.7 Sonnet 

Claude 3.7 Sonnet is Anthropic’s flagship model, described as their most intelligent model to date. It features hybrid reasoning, which allows it to switch between fast, near-instant responses and slower, more detailed thinking. This model is designed to optimize performance across speed and depth in response generation, marking a first in the LLM space. 

Technical Highlights

Claude 3.7 Sonnet boasts a 200K token context window (160K input + 40K output). Computer use capabilities enable Claude 3.7 to act as an agent that can write and execute code, with integration to Claude Code, a CLI tool that lets developers delegate coding tasks directly from the terminal. 

Performance

Claude 3.7 Sonnet achieves ~92% on HumanEval (coding), 83% on Math Benchmark, and ~70% on GPQA (graduate-level QA), demonstrating its strong capabilities in real-world scenarios. Consistently ranking near the top in instruction-following, general knowledge, and multi-step reasoning, Claude 3.7 outperforms competitors like GPT-4 and GPT-4o in several areas, cementing its position as a leading model in the field. 

Unique Strengths

Claude 3.7 excels in coding, especially in front-end web development, debugging, and generation, providing top-tier performance for real-world applications. Its multimodal integration allows it to handle both text and image inputs. Claude 3.7 ensures ethical alignment and reliable outputs, making it a trustworthy choice for sensitive applications.

How to access

Claude 3.7 Sonnet is a propriety commercial model accessed only through paid API platforms. The common APIs to access the model are Calude.ai, Amazon Bedrock, and third-party integrations like Slack and Notion. The price of using Claude 3.7 Sonnet can vary slightly depending on the platform and volume of tokens. 

Qwen 3 

Qwen3, develloped by Alibaba, brings a new level of advancement in both natural language processing and multimodal capabilities (processing text, images, audio, and video). Qwen3 offers a full range of dense and mixture-of-experts (MoE) models. Built on extensive training, Qwen3 introduces breakthroughs in reasoning, instruction-following, agent capabilities, and multilingual support. Its performance reflects how far language modeling in NLP has come. It is available as open-source under the Apache 2.0 license. 

Technical Highlights

Qwen 3 features dual operational modes, with Thinking Mode for deep reasoning and Non-Thinking Mode for quick responses, optimizing performance based on task complexity. With a 128K token context window, it can handle large documents and complex datasets in a single inference. Qwen 3 models range from 0.6B to 235B parameters. In addition, six dense models — Qwen3-32B, Qwen3-14B, Qwen3-8B, Qwen3-4B, Qwen3-1.7B, and Qwen3-0.6B — are open-weighted and released under the Apache 2.0 license. 

Performance

Qwen 3 achieved 94.2% on Codeforces, outperforming models like DeepSeek-R1 and Grok-3. In mathematical reasoning, it scores 89.7% on the AIME math benchmark, proving its advanced problem- solving capabilities. Additionally, Qwen 3 attains 83.9% on MMLU in general knowledge, reflecting its broad knowledge comprehension across various subjects. 

How to access

Qwen3 consists of various models, some of which are completely free and fully open source. Developers can run these models locally and fine-tune them on custom data. However, larger models that are capable of MoE variants have commercial or research restrictions. 

Conclusion 

LLMs are one of the most pivotal technologies of our time. While people think of size when they think about LLMs, architecture, speed, versatility, and cost all matter for an effective LLM. 2025 is the beginning of a new LLM era which offers a dynamic, customizable set of tools to build smarter software. 

Models like Llama 4, Mistral Medium 3, Claude Sonnet 3.7, DeepSeek-V3, and Qwen 3 will define the future of AI. Each of these models will allow researchers, developers, and enterprises to work smarter and efficeintly.  

Explore how Xavor’s AI development services can help you harness the power of these next-gen LLMs to accelerate innovation. Talk to our experts today at [email protected] and start building smarter, faster, and secure AI solutions.