How to choose the best LLM for your Task?

LLM stands for Large Language Model. It is an AI model trained on a massive amount of text data to interact with human beings in their native language (if supported). LLMs are categorized primarily into these two categories: Reasoning LLM and Standard LLM. Selecting the right one of these two LLMs can be a challenging task for some users. This article shows how to choose the best LLM for your tasks.

How to choose the best LLM for your tasks

LLMs are capable of understanding human requirements based on the text input and generating a response accordingly. Today, multiple large language models (LLMs) are available, including ChatGPT, Gemini, and Copilot, among others. To choose the best LLM for your tasks, it is important to know what the difference between these two models is.

Before starting our discussion, let’s see what Standard and Reasoning Large Language Models are.

Standard LLM

Standard LLMs are also called General Purpose LLMs. They are trained on huge datasets. These LLMs are capable of generating human-like text and performing general-purpose tasks, such as summarizing, translating, and answering user queries and questions. These Large Language Models predict the next word in a sentence by using the data they have been trained on. In this way, they keep predicting the next words in the sentence and generate the desired answer.

Since these LLMs predict the next word in a sentence, their generated answers may have flaws and not always be 100% correct. Hence, it is always recommended to reverify the response.

Reasoning LLMs

The Reasoning Large Language Models are developed to tackle the problems that Standard LLMs cannot handle. These LLMs can effectively handle complex tasks that require logical thinking. They can also generate a detailed step-by-step problem-solving response. Reasoning models are capable of breaking down complex tasks into simple steps and working on each step systematically. In short, these LLMs can mimic the logical thought process of human beings.

The following points will help you understand these two LLMs better:

Capabilities
Advantages
Limitations

Let’s start.

1] Capabilities of Standard LLMs

Since Standard LLMs are the General-purpose LLMs, they are suitable for daily tasks or general-purpose work. You can use them for writing simple articles and blog posts, generating marketing content and social media posts, writing stories and poems, etc.

If you travel to another country or have an online appointment with a foreign client where you interact with the chat process, Standard LLMs can help you eliminate the language barrier. You can use them to translate any language into your native language.

Standard LLMs are basically the Chatbots that answer your queries. Simply type your query and hit Enter. You will get your answer within a few seconds.

Capabilities of Reasoning LLMs

Let’s talk about the capabilities of Reasoning LLMs. Since they are more advanced Large Language Models, they are capable of solving more complex tasks and problems. They can help you solve complex mathematical problems, logical puzzles, generate scientific hypotheses, etc.

Reasoning LLMs are also capable of generating complex codes, identifying errors in a particular code, and suggesting optimizations in a particular code. Though Standard LLMs also solve coding problems but their capability for coding is limited.

In the 21st Century, data is the main fuel. Hence, analyzing data requires a lot of effort. Reasoning-based Large Language Models can perform data analysis and data extraction tasks easily.

Read: Query multiple LLMs at once using LLM Comparison Tool

2] Advantages of Standard LLMS

Let’s talk about the advantages of Standard LLMs.

Speed: Since these are the General-purpose LLMs, users use them only for general-purpose queries. Therefore, the speed of their response generation is fast.
No charge to use them: As Standard LLMs are primarily used for addressing general queries, users are often reluctant to pay for them. That’s why these Standard LLMs are free to use. You can chat with them for an unlimited time.
Broad knowledge: General-purpose LLMs are trained on massive amounts of data. That’s why they are a pool of knowledge. You can ask them anything and they will answer your queries.
Creativity: Standard LLMs are also capable of generating creative content. You just have to give the required prompt.

Advantages of Reasoning LLMs

The following are some of the advantages of Reasoning LLMs.

Can solve complex tasks: Reasoning LLMs are developed to solve complex problems. They can effectively handle complex tasks that require logical thinking and multi-step reasoning.
Analytical capabilities: These LLMs have analytical capabilities. This means they are good at analyzing data and research.
Decision making: These Large Language Models are also good at decision making. You can use their decision-making ability in scenarios that require weighing multiple factors and evaluating possible outcomes.

3] Limitations of Standard LLMs

The following are some of the limitations of Standard or General-purpose LLMs.

Errors: Because Standard LLMs work on predicting the next word in a sentence, their prediction can sometimes be wrong. Therefore, you cannot rely completely on the generated response. It is important to verify the output.
Cannot handle complex problems: These LLMs cannot handle complex problems. They struggle with multi-step reasoning, decision making, complex calculations, and inputs that are beyond the patterns in their training data.
Lack of explainability: They cannot explain the problems that require logical thinking.

Limitations of Reasoning LLMs

Let’s see some of the limitations of Reasoning LLMs.

Speed: Reasoning LLMs have a slower response time. This is because they have to research multiple websites, which takes time.
Cost: Reasoning LLMs have high computational costs. This is the reason why they are not completely free for users.
Prompt engineering: Unlike the Standard LLM, which can work on a simple prompt, Reasoning LLMs require more complex prompts to generate the best results.
Possibilities of errors: Since complex reasoning tasks require applying logic across multiple steps, Reasoning LLMs can sometimes be more prone to errors.

Selecting the right LLM

The following points will help you select the best LLM for your tasks.

Nature of your task: You can choose the best LLM based on the nature of your task. For example, Standard LLMs are best for tasks that do not require deep research. Examples of such tasks are generating SEO descriptions, translating documents, generating creative social media posts, creating quizzes, creating a travel plan, drafting emails, etc. Tasks that require deep research require a Reasoning LLM. Additionally, if you want to use an LLM for decision making, solving complex problems, coding, etc., Reasoning LLMs are the best.
Errors: Both Standard and Reasoning LLMs are prone to errors. You can easily verify the response generated by a Standard LLM rather than a Reasoning LLM.
Latency/speed: Reasoning LLMs have a slow response generation speed. If your tasks are time-bound, use Standard LLM.
Budget: Standard LLMs are free to use, whereas Reasoning LLMs offer limited access to free users. Therefore, if you have a limited budget, use Standard LLM.

I hope this helps.

How do we select the right LLM for each query?

Each query is different. Therefore, it is important to understand the nature of the query to select the right LLM. If the query requires deep research, select the Reasoning LLM. If the query requires immediate response, select the Standard LLM.

What is the best LLM for writing tasks?

It depends on the type of writing task. If the task is simple, such as creative writing, blog writing, creating social media posts, etc., Standard LLM is the best. If the task requires deep research, such as technical writing and academic writing, Reasoning LLM is the best.