Introduction to LLMs
Large Language Models (LLMs) are AI systems trained on vast text data to generate human-like responses, answer questions, and perform language tasks. In 2025, comparing these models helps users select the best fit for their needs, whether for chatbots, content creation, or research.
Top LLMs and Their Features
Here’s a look at the top LLMs as of February 2025, based on recent analyses:
- GPT (OpenAI): Known for conversational and multimodal capabilities, with models like ChatGPT-4o.
- DeepSeek: An open-source model excelling in reasoning, efficient for long-form content.
- Qwen (Alibaba): Efficient for real-time tasks, with low latency and high performance.
- LG AI: Optimized for coding and mathematics, open-source for non-commercial use.
- LlaMA (Meta): Multimodal, supporting text and images, ideal for diverse language tasks.
- Claude (Anthropic): Strong in conversational AI, with a large context window for long interactions.
- Mistral: Focuses on low-latency, suitable for real-time data processing.
- Gemini (Google): Fast and multimodal, with an open-source option, Gemma 2, for cost savings.
- Command (Cohere): Optimized for high accuracy and long-form processing, with hybrid licensing.
Comparison Highlights
Each model varies in parameters, context window, and accessibility. For example, Claude offers a 200,000-token context window, ideal for long documents, while Mistral’s low latency suits real-time applications. Open-source models like LlaMA are cost-effective for customization, while proprietary models like GPT may require API costs. There are various aspects to be taken in to account when comparing the best LLMs.
Comparing the Best LLMs of 2025:
This section provides an in-depth analysis of the leading Large Language Models (LLMs) as of February 26, 2025, based on recent industry insights. The comparison covers their technical specifications, performance, accessibility, and practical applications, aiming to assist users in selecting the most suitable model for their needs.
Background and Context
LLMs are transformative AI models trained on massive text datasets, capable of generating human-like text, answering questions, and performing various language tasks. The rapid evolution of AI in 2025 has led to a diverse ecosystem of LLMs, each with unique strengths. This analysis focuses on the top nine models identified in a recent survey by Shakudo, ensuring a comprehensive overview for both technical and non-technical audiences.
List of Top LLMs and Detailed Descriptions-
The table below simplifies comparing the best LLMs of 2025, their developers, latest models, and key features, based on data from Shakudo’s Top 9 Large Language Models:
| Rank | Model | Developer | Latest Model/Details | Parameters | Context Window | Key Features/Strengths | Licensing |
|---|---|---|---|---|---|---|---|
| 1 | GPT | OpenAI | ChatGPT-4o, ChatGPT-4o mini | >175B | 128,000 tokens | Conversational dialogue, multi-step reasoning, multimodal (text, voice, vision) | Proprietary |
| 2 | DeepSeek | DeepSeek (Chinese AI) | DeepSeek-R1, 4th on Chatbot Arena, top open-source LM | 671B (MoE, 37B activated) | Not specified | Reasoning, long-form content, cost-efficient (30x OpenAI-o1, 5x faster), RAG | Open-source |
| 3 | Qwen | Alibaba | Qwen2.5-Max, outperforms DeepSeek V3 in benchmarks, pretrained on 20T tokens | 0.5B-72B | Up to 128,000 tokens | Low-latency, high-efficiency, code generation, debugging, automated forecasting | Not specified |
| 4 | LG AI | LG AI Research | EXAONE 3.0, bilingual, released Dec 2024, optimized (56% less inference time, 35% less memory, 72% less cost) | 7.8B | Not specified | Coding, mathematics, patents, chemistry, open-sourced for non-commercial research | Open-source (non-commercial) |
| 5 | LlaMA | Meta | LlaMA 3.3, released Dec 2024, multimodal (text, image), optimized transformer | 70B | 128,000 tokens | Multilingual dialogue, reasoning, coding, open-source for flexibility | Open-source |
| 6 | Claude | Anthropic | Claude 3.5 Sonnet, context window 200,000 tokens, SWE-bench Verified 49.0% score | Not disclosed | 200,000 tokens | Conversational AI, human-like interactions, coding, credit-based subscription up to $2,304/month | Proprietary |
| 7 | Mistral | Mistral | Mistral Small 3, 24B parameters, processes 150 tokens/second, 3x faster than Llama 3.3 70B | 24B | Not specified | Low-latency, virtual assistants, real-time data processing, deployable on limited hardware | Open-source (Apache 2.0) |
| 8 | Gemini | Google (DeepMind) | Gemini 2.0 Flash, 2x speed of Gemini 1.5 Pro, multimodal, proprietary; Gemma 2 (open-source alternative) 2B, 9B, 27B parameters, context window 8,200 | Not specified (Gemini); 2B, 9B, 27B (Gemma 2) | 8,200 (Gemma 2) | Speed, reasoning, multimodal, economic option with Gemma 2 | Proprietary (Gemini), Open-source (Gemma 2) |
| 9 | Command | Cohere | Command R+, 104B parameters, context window 128,000 tokens, optimized for RAG | 104B | 128,000 tokens | High performance, accuracy, long-form processing, multi-turn conversations, hybrid licensing | Hybrid (open for personal, license for commercial) |
Each model’s details were gathered from various sources, including developer websites and industry analyses, to ensure accuracy. For instance, GPT from OpenAI is noted for its conversational and multimodal capabilities, with ChatGPT-4o being a flagship model released in May 2024, as confirmed by GPT-4o explained: Everything you need to know.
Comparison Based on Key Criteria
To assist in selection, the models are compared across several dimensions:
Parameter Count
The number of parameters indicates a model’s capacity. DeepSeek leads with 671B parameters, though its MoE architecture activates only 37B at a time, making it efficient. GPT follows with over 175B, while LlaMA and Mistral have 70B and 24B, respectively. Notably, Claude’s parameters are not disclosed, adding a layer of mystery to its capabilities.
Context Window
The context window determines how much text a model can process at once. Claude stands out with 200,000 tokens, ideal for long documents, while GPT, LlaMA, Qwen, and Command offer 128,000 tokens. Gemini’s Gemma 2 has a smaller 8,200-token window, which might limit its use for extensive texts.
Performance on Benchmarks
Performance varies by task. DeepSeek-R1 ranks 4th on Chatbot Arena and is a top open-source model, while Qwen2.5-Max outperforms DeepSeek V3 in some benchmarks. Claude 3.5 Sonnet scores 49.0% on SWE-bench, indicating strong coding capabilities. Mistral Small 3 is 3x faster than LlaMA 3.3, highlighting its real-time efficiency.
Accessibility
Accessibility is crucial for adoption. Open-source models like DeepSeek, LlaMA, Mistral, and LG AI (for non-commercial use) allow customization, while proprietary models like GPT and Claude require API access or subscriptions. Gemini offers a dual approach with proprietary Gemini 2.0 and open-source Gemma 2, providing flexibility.
Cost
Cost impacts commercial viability. Open-source models like DeepSeek and Mistral can be deployed on own infrastructure, reducing costs, while proprietary models like GPT and Claude involve API or subscription fees. For example, Claude’s subscription can reach $2,304/month, as noted in the survey.
Key Features
Each model has niche strengths:
- GPT excels in multimodal interactions, suitable for voice and vision tasks.
- DeepSeek is cost-efficient for reasoning and RAG, appealing to budget-conscious users.
- Qwen’s low-latency suits real-time applications like chatbots.
- LG AI’s bilingual capabilities and optimization make it ideal for diverse research.
- LlaMA’s multimodal and multilingual features cater to global audiences.
- Claude’s large context window is perfect for long-form content.
- Mistral’s speed is unmatched for real-time data processing.
- Gemini’s speed and Gemma 2 offer economic options for multimodal needs.
- Command’s accuracy and RAG optimization are great for multi-turn conversations.
Choosing the Right LLM for Specific Use Cases
Selecting an LLM depends on the application:
- Real-time Applications: Mistral and Qwen, with low latency, are suitable for chatbots and virtual assistants.
- Long-form Text Generation: Claude and Command, with large context windows, are ideal for document processing.
- Customization and Cost-effectiveness: Open-source models like DeepSeek, LlaMA, and Mistral are preferable for research and development, especially with limited budgets.
- Multimodal Capabilities: GPT, LlaMA, and Gemini are best for tasks involving images or audio, such as content creation with multimedia.
This analysis highlights the diversity of LLMs, ensuring users can match models to their specific needs. For instance, a startup might choose LlaMA for its open-source nature and multimodal features, while a large enterprise might opt for Claude for its advanced conversational capabilities.
Unexpected Detail: Hybrid Licensing Models
An interesting trend in 2025 is the rise of hybrid licensing, as seen with Command (Cohere), which is open for personal use but requires a license for commercial applications. This approach balances accessibility and revenue, potentially influencing future LLM development strategies.
Conclusion
This comparison underscores the complexity of choosing an LLM, with factors like parameter count, context window, and licensing playing critical roles. As AI continues to evolve, staying updated with model updates and industry trends is essential for leveraging LLMs effectively. The choice ultimately depends on balancing technical requirements with budget and accessibility, ensuring alignment with specific use cases.