You’re looking to choose the best LLM, but where do you start? The options can feel endless, But here’s the thing: the right model isn’t just about what’s trending or what has the most parameters. It’s about matching the model to your specific needs—performance, budget, and task requirements all matter.
From my experience, understanding key factors like model size, architecture, and training data is crucial. But even with all the options, there’s no one-size-fits-all. You need to know how to evaluate them effectively and align their strengths with your use case.
In this post, we’ll break down exactly how to choose the best LLM for your needs. I’ll cover the essential criteria, from parameters and inference speed to licensing and deployment considerations.
5 Core Evaluation Criteria for LLMs
Choosing the best LLM for your business starts with understanding how to evaluate them. Here are five essential criteria to consider:
1. Model Size (Parameters & Inference Cost)
Model size refers to the number of parameters in an LLM. Larger models (like GPT-4) tend to be more powerful but also require more computing power and cost more to run. Smaller models (like Mistral 7B) are faster and cheaper, often good enough for many business use cases. Always weigh performance gains against your infrastructure and budget.
2. Architecture Type (Encoder, Decoder, or Hybrid)
- Encoder-only (e.g., BERT): Best for structured tasks like classification, fraud detection.
- Decoder-only (e.g., GPT, LLaMA): Optimized for generation — chatbots, content, assistants.
- Encoder-decoder (e.g., T5, BART): Strong in summarization, translation, question answering. Advanced hybrids like Mixtral (MoE) reduce compute cost by activating only parts of the model per request — ideal for scaling usage affordably.
3. Benchmark Performance
Benchmarks like ARC, MMLU, and WinoGrande help compare models across reasoning, multitasking, and common-sense understanding. While high scores matter, look for models that perform well in your domain (e.g., legal, medical, finance).
4. Training Data & Bias
The quality and source of training data affect how an LLM responds. Biased or outdated data can lead to inaccurate or harmful outputs. Understanding what the model was trained on helps you assess reliability.
5. Licensing & Deployment
Some models are open-source and free to use commercially, while others are closed and require API access. Know your compliance requirements and deployment preferences (cloud vs. on-prem) before committing.
The “best” model is the one that aligns with your technical capacity, compliance needs, and strategic goals. Evaluating an LLM means balancing performance with trust, flexibility with risk, and short-term gains with long-term scalability.
Each of these factors affects performance, cost, and risk. Make sure your chosen LLM aligns with your business needs.
Understanding LLM Size, Speed, and Cost Trade-offs
Understanding how model size, architecture, and efficiency impact performance and cost is essential before selecting an LLM that fits your business goals. Let’s break down the technical trade-offs in clear terms to help you make informed decisions.
What Are Parameters in an LLM?
Parameters are the internal settings that a model learns during training. Think of them as the “knobs” the model adjusts to understand language patterns. The more parameters a model has, the more data it can capture, which can lead to better understanding and more accurate outputs.
However, more parameters also mean more computational power is needed. This directly impacts cost: running a 70B model will require more memory, more GPU capacity, and longer processing times than a 7B model — even for the same task
Why Parameter Count Isn’t Everything
While parameter count matters, it’s not a reliable indicator of performance by itself. Two models with similar sizes can behave very differently. For example, Mistral 7B outperforms GPT-3 (175B) on many tasks, thanks to a more efficient architecture and optimized training.
Other factors that impact performance:
- Context window: How much text the model can “see” at once (important for long documents).
- Tokenization strategy: Impacts how well the model understands complex words or phrases.
- Training optimizations: Some models are fine-tuned to be faster or more accurate without increasing size.
Inference Speed and Latency
Inference speed is the time a model takes to respond to a prompt. Larger models generally process more slowly due to more computations. This matters in real-world applications — a delay in a customer support chatbot or pricing engine can impact user experience or system performance.
- Smaller models: Faster responses, better for real-time systems.
- Larger models: More accurate but higher latency and cost.
Size Categories: From Micro to Massive
Understanding size helps you match the right model to your business needs:
| Size Range | Ideal For |
| <1B | On-device use, mobile apps, IoT |
| 1–10B | Balanced quality, general business apps |
| 10–100B | Enterprise tasks, document generation |
| 100B+ | Premium use cases, legal, R&D copilots |
Efficiency Techniques That Matter Today
Modern LLM development often prioritizes efficiency over raw size:
- Quantization reduces the memory footprint with minimal accuracy loss.
- Pruning removes redundant parameters to speed up inference.
- Distillation trains smaller models to mimic larger ones.
These techniques mean a compact model can offer near-premium performance, at a fraction of the cost.
Model Size and Use Case Alignment
| Model | Params | Architecture | Ideal Use Case |
| BERT-base | 110M | Encoder-only | Classification, sentiment analysis |
| Mistral 7B | 7.2B | Decoder-only | Chatbots, general NLG |
| Falcon 40B | 40B | Decoder-only | Document summarization, pipelines |
| GPT-4 | ~1.76T | Mixture of Experts | High-stakes reasoning, agents |
- Takeaway: Don’t just chase model size. Choose based on task fit, infrastructure limits, and expected return. A smaller, optimized model might serve your business better than a massive, resource-heavy one.
Architecture Types and Their Ideal Use Cases
When you’re choosing an LLM, how the model is built matters just as much as how big or fast it is. Different architectures are designed for different kinds of tasks. Picking the right one helps you avoid unnecessary costs and ensures the model actually fits what you’re trying to do.
Encoder-Only Models
These models are built to understand text, not to generate it. They’re efficient and work well when you’re analyzing or labeling data. If you’re classifying emails, tagging support tickets, or generating embeddings for search, this type is a solid choice.
Example: BERT
Best For: Classification, search, and general text understanding.
Encoder-Decoder Models
This type of model reads something in, processes it, and then writes something out based on that input. It’s useful when the output depends closely on the input, like translating a sentence or summarizing a document.
Example: BART
Best For: Summarization, translation, and structured Q&A tasks.
Decoder-Only Models
These models are built for text generation. You give them a prompt, and they respond with new text. They’re the backbone of most chatbots and assistants today.
Examples: GPT, Mistral, LLaMA
Best For: Chat interfaces, writing assistants, and content generation.
Emerging Architectures
Some newer models are taking a different approach to improve efficiency or accuracy.
Mixtral uses a mixture of experts. Instead of running the full model every time, it activates only certain parts depending on the task. This helps reduce compute without sacrificing too much performance.
RAG, or retrieval-augmented generation, combines a language model with a search system. When it answers, it can pull in information from a database or document store. This is helpful if your business relies on internal knowledge that changes often.
Choose the architecture that matches your use case. If you need fast understanding, go with encoder-only. If you want the model to produce text, decoder-only is usually the best fit. For back-and-forth tasks like summarization, an encoder-decoder is better.
And if you’re working with private or frequently updated information, consider RAG or models like Mixtral to balance performance and cost.
Evaluating LLM Performance and Interaction Techniques for Business Use
To choose the best LLM for your business in 2025, you need to know how well it performs and how to use it effectively. Let’s get to know certain simple ways to measure an LLM’s abilities and interact with it to get the best results for your needs.
How LLMs Are Tested
Businesses want LLMs that give accurate and useful answers. Two main methods are used to check their performance:
- Academic Exams (like SAT, LSAT, or AP tests): These show if an LLM can understand and solve problems across different topics, such as answering customer questions or analyzing data.
- Q&A Datasets: These test how well an LLM responds to real-world questions, which matters for things like chatbots or support tools.
Key Tests to Know
Here are the main benchmarks that measure an LLM’s strengths:
- ARC (Reasoning Challenge): Checks if the LLM can think logically, like solving puzzles or making decisions in business scenarios.
- MMLU: Tests knowledge across many subjects, such as law, medicine, or finance, ensuring reliable answers for your industry.
- WinoGrande: Measures common sense, like understanding who or what a sentence refers to, which helps in natural conversations.
- FLOW: Tests how well an LLM adapts to new or changing problems, important for dynamic tasks like planning or forecasting.
How to Work with an LLM
Getting the best out of an LLM depends on how you ask it questions. These methods make it easier:
- Zero-Shot Prompting: Ask the LLM directly without examples. Use this for quick, simple tasks like summarizing a report.
- One-Shot Prompting: Give one example to show what you want. This helps with tasks like writing emails in a specific style.
- Few-Shot Prompting: Provide a few examples for better accuracy. Great for complex tasks like generating product descriptions.
- Chain-of-Thought Prompting (CoT): Ask the LLM to explain its steps. This is perfect for solving problems, like budgeting or troubleshooting.
Why This Matters for Your Business
By knowing these tests and techniques, you can pick an LLM that fits your needs, whether it’s answering customer queries, analyzing data, or automating tasks.
Focus on benchmarks that match your goals (like MMLU for accuracy or FLOW for flexibility) and use prompting to get clear, useful outputs. This will save time and boost results.
Quality Comparison Across LLMs (Expanded Table Format)
When evaluating the performance of different LLMs, it’s crucial to look at how they perform on standard benchmarks like ARC, MMLU, and WinoGrande. This helps you choose the best model for your specific use case based on accuracy and inference efficiency. Below is a comparison that breaks down model families, size, performance, and more.
Accuracy Across Benchmarks (ARC, MMLU, WinoGrande)
| Model Family | Model Size | Prompting Method | ARC Performance | MMLU Performance | WinoGrande Performance | Performance Notes |
| GPT-4 | ~1.76T | Few-shot | High | High | High | Best for reasoning and creative tasks. Requires significant compute resources. |
| Claude 3 | 175B | Few-shot | High | High | Moderate | Strong in reasoning tasks, but less efficient in complex generation. |
| Mistral 7B | 7.2B | Zero-shot, Few-shot | Moderate | High | High | Cost-efficient, great for chatbots and general text generation. |
| BERT | 110M | Zero-shot | High | Moderate | Low | Great for classification and embeddings, but less suited for generation. |
| LLaMa 2 7B | 7B | Zero-shot | Moderate | High | Moderate | Good for low-resource environments, balanced accuracy and efficiency. |
Performance Notes:
- GPT-4 stands out for its exceptional accuracy across ARC, MMLU, and WinoGrande, especially in reasoning tasks, but it demands high compute, making it less efficient for rapid inference or cost-sensitive use cases.
- Claude 3 performs similarly to GPT-4 in reasoning tasks, but it’s more efficient, although it may not handle very complex generation tasks as well.
- Mistral 7B is a strong contender in the cost-efficiency department, especially for applications like chatbots, where response speed and general text generation matter more than ultra-high accuracy.
- BERT is still one of the best for classification tasks, particularly in understanding and tagging text. However, it doesn’t perform as well in generating new content.
- LLaMa 2 7B is perfect for low-resource environments, where you need a balance of performance and efficiency, without sacrificing too much in accuracy for general tasks.
Use Case Matchmaking: Which Model for What?
Choosing the right model depends on your specific use case and requirements. Below is a quick guide to help match models to their most suitable tasks:
Chatbot Applications
- Best Choice: GPT-4
It provides top-tier conversation quality, handling complex queries and delivering nuanced responses. However, it’s more expensive and slower compared to smaller models.
- Cost-Efficient Option: Mistral 8x7B
Mistral offers great value for chatbots, with a good balance of cost and performance. It may not match GPT-4 in terms of creativity, but it excels at fast, cost-effective responses.
Text Classification
- Best Choice: BERT Variants (BERT, RoBERTa, etc.)
BERT is known for its accuracy in text classification tasks like sentiment analysis or tagging. Its encoder-only design makes it quick and efficient for these use cases.
Translation/Summarization
- Best Choice: BART, T5
These models excel in translation and summarization due to their encoder-decoder architecture. They understand context well and can transform one type of text into another efficiently.
Low-Resource Environments
- Best Choice: Falcon 7B, LLaMa 2 7B
Both of these models provide a good balance of performance and efficiency, making them suitable for edge devices or environments where computational power is limited. They may not achieve the highest accuracy on complex tasks, but they handle basic needs well at a much lower cost.
Choosing the right model is about balancing accuracy, efficiency, and cost for your specific business needs. For high-quality, complex tasks, go with GPT-4 or Claude 3. For cost-sensitive or resource-limited environments, Mistral 7B, Falcon 7B, and LLaMa 2 7B are your best bets.
Fine-Tuning & Adaptability of LLMs
What is Fine-Tuning?
Fine-tuning is the process of adjusting a pre-trained language model to better suit specific tasks or domains. It involves training the model further on a specialized dataset, allowing it to learn patterns and nuances that are specific to your business needs or industry.
For example, a general model like GPT-4 might be fine-tuned with financial data to make it more accurate in understanding financial terminology and concepts. Fine-tuning doesn’t require starting from scratch but improves the model’s performance on specific tasks.
When You Should Fine-Tune
You should fine-tune an LLM when:
- Your task is domain-specific (e.g., finance, healthcare) and requires specialized knowledge.
- The model’s performance on your task is not ideal with a general pre-trained model.
- You need the model to adapt to industry-specific language or slang.
- You require more control over the model’s behavior and output.
Popular Fine-Tuned Models
- FinBERT: Fine-tuned for financial text, such as news articles, earnings reports, and other financial documents.
- BioBERT: Specialized for biomedical text, making it ideal for research papers, medical queries, and drug discovery.
- ChatFalcon: Fine-tuned for chat applications, with a focus on improving conversational ability in customer support and virtual assistants.
Trade-offs: Control vs Performance vs Effort
- Control: Fine-tuning allows you to have more control over the model’s outputs, which is essential for industries with strict regulatory requirements or where accuracy is paramount.
- Performance: Fine-tuned models often perform better on specific tasks compared to general models, but they can still fall short if the fine-tuning data isn’t comprehensive.
- Effort: Fine-tuning can be resource-intensive and requires expertise in machine learning. It also takes time to gather the right data and ensure the model is tuned effectively.
Open vs Closed Source Models
Closed Source
Closed-source models, such as GPT-4 or GPT-3, are only accessible via API, meaning you don’t have direct access to their underlying code or weights. You can use them for tasks, but you’re dependent on the vendor for updates, performance adjustments, and pricing.
Pros:
- No need for infrastructure or maintenance.
- Regular updates and improvements from the provider.
- Easy to integrate with cloud services.
Cons:
- Limited customization options.
- Dependency on the vendor, which can mean higher costs or changes in pricing.
Open Source
Open-source models like Mistral, LLaMa, and Falcon offer full access to the model’s weights and architecture. This means you can host the model yourself, customize it, and even fine-tune it for your specific needs.
Pros:
- Full control over deployment and fine-tuning.
- Lower cost in the long run if you have the infrastructure.
- No vendor lock-in.
Cons:
- Requires significant technical expertise and infrastructure.
- May require additional resources for hosting, maintenance, and updates.
Hybrid Access
Some models are open-source with certain license restrictions. These hybrid models provide access to the model’s weights but with specific terms of use, like requiring a commercial license for redistribution or use in production.
Example: LLaMa 2 offers open weights, but the model may come with licensing constraints on commercial usage.
Licensing Guide for Commercial Use
When choosing an LLM, understanding licensing is crucial, especially for commercial use. Here are the key license types and what they mean:
Key License Types
- Apache-2.0: Open-source, permissive license that allows you to use, modify, and distribute the model. Great for commercial use without restrictions.
- MIT: Similar to Apache, it’s permissive and allows for broad use, including commercial use.
- Custom Commercial Licenses: Some models, particularly closed-source ones, require specific commercial licenses. These can restrict how the model is used, shared, or redistributed.
Commercial Use Summary Table
| Model Name | License Type | Usage Rights | Adaptability |
| GPT-4 | Closed (API Only) | Commercial via API | Limited |
| Mistral | Apache-2.0 | Full use and modification | High |
| LLaMa 2 | Custom (Open Weights) | Full use with restrictions | High |
| Falcon | Apache-2.0 | Full use, including commercial | High |
Deployment Factors to Consider
When deciding between deploying an LLM in the cloud or hosting it locally, here are the main factors to consider:
Cloud API vs Local Deployment
- Cloud API: Ideal for businesses that want quick setup and don’t have the infrastructure to support large models. It’s easy to scale but can become expensive over time.
- Local Deployment: Offers greater control and can be more cost-effective long-term. It requires managing your own servers, storage, and infrastructure but allows for customization and avoids vendor lock-in.
UbiOps & Other Platforms
Platforms like UbiOps provide cloud-based environments where you can deploy and scale your models easily, reducing the technical overhead. They also allow you to monitor and optimize performance in real-time.
When to Host Yourself vs Plug into an API
- Host locally when you need greater control, customization, or have strict data privacy requirements.
- Use an API when you want rapid deployment without worrying about infrastructure, scaling, or maintenance.
Scalability, Latency, and TCO (Total Cost of Ownership)
- Scalability: Cloud APIs scale easily but may become expensive with high usage. Local hosting requires upfront investment but offers long-term cost savings.
- Latency: Local deployment may reduce latency, especially for real-time applications.
- TCO: Consider the cost of infrastructure, maintenance, and updates when hosting locally versus the ongoing costs of API access.
Final Checklist: Choosing the Right LLM
Here’s a quick checklist to help you finalize your choice of LLM:
- Budget Constraints: What is your budget for both deployment and operational costs?
- Task Requirements: Are you focused on NLG (natural language generation), NLU (natural language understanding), embeddings, or another task?
- Desired Accuracy and Efficiency: Do you need a model with high accuracy, or is cost-efficiency more important for your use case?
- Hosting and Fine-Tuning Capabilities: Do you need to fine-tune the model? Can you host it yourself, or do you need to rely on an API?
- License Compatibility: Does the model’s license align with your commercial plans?
By considering these factors, you can choose the LLM that best fits your needs and objectives.
Wrap Up: Choosing the Best LLM for Your Business
Selecting the right Large Language Model (LLM) is a key decision for your business’s AI strategy. It’s not just about choosing the most powerful model; it’s about finding the one that aligns with your specific goals, infrastructure, and budget. Whether you’re automating customer support, analyzing text data, or building more sophisticated AI tools, the right LLM can drive significant value.
Focus on understanding your task requirements, benchmarking models across key metrics, and weighing the trade-offs between model performance, cost, and control. Don’t forget to consider fine-tuning needs, licensing options, and deployment methods.
Remember, the best LLM is the one that meets your unique needs, not just the one with the most parameters. As you evaluate your options, ensure you’re making a choice that provides long-term scalability and efficiency for your business.
Related Reads
- Chatbot vs ChatGPT: Understanding the Main Differences
- What Is Contextual AI and How Does It Work?
- What is AI-Powered Knowledge Management? Overview, Applications, Steps, and Benefits
FAQs on How to Choose the Best LLM
1. Which LLM is most in demand?
As of 2025, GPT-4 and Claude 3 are among the most in-demand LLMs due to their versatility, advanced performance across various tasks, and ability to handle both generative and reasoning tasks. Their popularity spans industries such as customer service, content generation, and legal analysis.
2. Which LLM is best for coding in 2025?
For coding-related tasks, Codex (by OpenAI) and DeepMind’s AlphaCode are currently leading models. These models excel in generating, debugging, and understanding code in a variety of programming languages. As for general-purpose LLMs, GPT-4 is widely used for coding tasks due to its ability to handle code generation and problem-solving.
3. Which branch of LLM is best?
The best branch of an LLM program depends on your professional goals. For those interested in corporate law, a focus on business law or intellectual property may be ideal. If you’re aiming to work in international law, consider pursuing programs focused on international relations and human rights.
4. Which is better: a 1-year LLM or a 2-year LLM?
A 1-year LLM is best suited for those who already have substantial experience and want to specialize or enhance their qualifications quickly. A 2-year LLM, on the other hand, offers more time for in-depth study, research, and a broader understanding of legal fields. Choose based on your career goals and whether you prefer intensive learning or a more flexible, thorough approach.
5. Which LLM program is the best?
The best LLM program depends on your career aspirations and area of interest. For example, Harvard Law School, Yale Law School, and Stanford Law School are highly regarded globally. However, your ideal program should align with your specialization, career goals, and budget.
6. Which subject is best in LLM?
The best subject for an LLM largely depends on your interests and career trajectory. Popular subjects include international law, corporate law, human rights law, tax law, and intellectual property law. If you’re focused on technology, cybersecurity law or AI law may be promising fields. Ultimately, choose a subject that aligns with your passions and the type of legal work you want to pursue.
