There are numerous LLMs on the market that can process and generate human-like content. They range from paid versions to open source alternatives. While all these models were trained to perform similar core tasks, their proficiency varies, making some of them better at particular tasks than others. Identifying the optimal LLM for your needs involves thorough testing and comparative analysis based on your use case. This overview is meant to give you an idea of what LLMs are available and help you choose the ones to explore further.

Tasks

All LLMs currently available on the market can perform the following tasks:

• text generation (this includes both creative and to-the-point text)
• code generation
• translation
• acting as AI assistants and chatbots

Some LLMs are better at some of these tasks than others. The differences may lie in the level of nuance the LLM can understand, the speed, or the languages it can operate in.

Models Within a Family

Large language models (LLMs) come in families. For example, GPT is a family that includes several models. Within a family, models can vary in size, context window (the number of tokens they can process), and the type of instructions they handle (such as instruct and chat models).

• Larger models usually deliver better performance but run more slowly.
• Models with larger context windows are more expensive but can process more text in a single request.

Model Types

Instruct Models

These models are trained to follow direct instructions or prompts. They're optimized for task completion rather than free-flowing conversations. They work well with specific commands and instructions. These models often include the word "instruct" in their name.

Instruct models follow a prompt and output a string. They're best for:

• Text transformation and summarization
• Question answering and information retrieval
• Generating content based on prompts
• Task-specific assistance

Examples: OpenAI's InstructGPT or Meta's LlaMA

Chat Models

Chat models are optimized for open-ended conversations and generating responses based on prior context in a chat-based format. Unlike instruct models, they process multiple exchanges, preserving context across turns to maintain a coherent dialogue.

These models take in a list of messages, where each message has a role (such as system, user, or assistant) and content. The system message helps set the conversation's overall tone and instructions. The model then generates the next message in the conversation, usually as the assistant.

Tool Calling

Chat models can also interact with external tools or functions. To do this, you provide a tool configuration, and the model generates a JSON schema that can be used to call the tool or function.

They're best for:

• Participating in multi-turn conversations and maintaining the dialogue format
• Systems where you need to call external tools or functions

Examples: Anthropic's Clause, OpenAI's ChatGPT

Models Overview

As the field is rapidly developing and new models with enhanced capabilities keep being released, we recommend checking the following model providers resources to learn about their latest models:

• Anthropic
• Cohere
• DeepSeek
• Google DeepMind
• Meta
• Mistral
• OpenAI