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Model Types

Overview

This section gives an overview of different model types.

Introduction

Different types of text models are designed with varying architectures, training data, and optimization goals, leading to distinct capabilities and best-fit use cases.

While many models focus primarily on text, an increasing number are becoming multimodal, capable of processing and generating information across different types of data, such as text, images, audio, and even video.

Today, these different types of models are generally accessed by selecting the specific model or API endpoint provided by developers (versus the user accessing the same interface that figures out the best type of model).

Deep Research

Models in this category are typically designed for in-depth information retrieval, synthesis, and report generation from vast amounts of data, often involving Browse and analyzing multiple sources. They aim to provide comprehensive and well-supported answers to complex queries.

Key Insights:

  • Extensive Information Gathering: Excel at searching and processing information from large and diverse datasets, including the web or private document repositories.
  • Synthesis and Structuring: Capable of synthesizing information from various sources into coherent and structured reports or summaries.
  • Handling Complexity: Designed to tackle complex, multi-faceted research questions that require connecting information across different domains.
  • Citation and Verification: Often include features for citing sources, allowing users to verify the information presented.

Use Cases:

  • Generating detailed reports on niche or complex topics.
  • Performing market research by analyzing industry trends and competitor information.
  • Assisting academics and researchers in literature reviews and synthesizing findings.
  • Providing comprehensive answers to complex legal, medical, or scientific questions.
  • Analyzing large volumes of internal documents to extract insights.

Examples: Perplexity Deep Research, ChatGPT Deep Research, Gemini Deep Research, HuggingFace Open Deep Research, Claude 3 Opus

Reasoning

Reasoning-focused models are optimized to perform complex logical deductions, solve problems requiring multiple steps, and understand intricate relationships between concepts. They are built to "think" through problems rather than just retrieving information or generating text based on patterns.

Key Insights:

  • Logical Deduction: Strong capabilities in applying logical rules and deriving conclusions from given premises.
  • Multi-Step Problem Solving: Can break down complex problems into smaller, manageable steps and follow a chain of thought to reach a solution.
  • Mathematical and Scientific Reasoning: Often perform well on mathematical problems, coding challenges, and scientific inquiries that require step-by-step analysis.
  • Reduced Hallucination in Complex Tasks: While still a challenge for all LLMs, models focused on reasoning aim to reduce the likelihood of generating false or inconsistent information in complex scenarios by showing their work or using techniques like self-correction.

Use Cases:

  • Solving complex mathematical equations or proofs.
  • Debugging code and suggesting logical fixes.
  • Analyzing data and drawing reasoned conclusions.
  • Assisting in strategic planning by evaluating scenarios and predicting outcomes.
  • Providing step-by-step explanations for complex concepts or solutions.
  • Excelling at benchmarks requiring logical inference and problem-solving.

Examples: DeepSeek-R1, OpenAI’s GPT-4, Google’s Gemini Ultra, Anthropic’s Claude 3 Sonnet, Meta’s Llama 3

General Purpose

General-purpose LLMs are designed to be versatile and handle a wide array of natural language tasks. They are trained on broad datasets to provide a good balance of capabilities across different domains without being specifically optimized for one.

Key Insights:

  • Versatility: Capable of performing a wide range of tasks, including text generation, summarization, translation, question answering, and creative writing.
  • Broad Knowledge: Possess a vast amount of general knowledge from their diverse training data.
  • Adaptability: Can often adapt to different styles and formats based on the prompt.
  • Accessibility: Typically the most widely available and accessible models for everyday use.

Use Cases:

  • Drafting emails, articles, and other written content.
  • Summarizing documents or long texts.
  • Translating text between languages.
  • Answering general knowledge questions.
  • Brainstorming ideas and assisting in creative writing.
  • Powering chatbots and virtual assistants for a variety of inquiries.

Examples: GPT-4 Turbo, Gemini Pro, Claude 3 Haiku, Mistral Large, Cohere Command R+

Code

Code-focused models are specifically trained on large datasets of code from various programming languages and sources. They are designed to understand, generate, and assist with programming tasks.

Key Insights:

  • Code Generation: Can generate code snippets, functions, or even entire programs based on natural language descriptions or prompts.
  • Code Completion: Provide intelligent suggestions for completing code as developers type.
  • Code Explanation and Documentation: Can explain how code works and generate documentation.
  • Debugging and Error Detection: Assist in identifying potential errors and suggesting fixes in code.
  • Code Translation: Translate code between different programming languages.
  • Support for Multiple Languages: Trained on a wide variety of programming languages.

Use Cases:

  • Speeding up software development by generating boilerplate code.
  • Assisting developers in learning new programming languages or frameworks.
  • Automating repetitive coding tasks.
  • Improving code quality through suggestions and error detection.
  • Generating test cases for software.
  • Helping non-programmers understand or modify code.

Examples: CodeLlama (Meta), StarCoder (ServiceNow), Codex (OpenAI), DeepSeek Coder, Google’s Codey

Tip

This blog gives a good overview of different models' capabilities for code and echoes my experience.

Tip

Code Generation is a small part of Software Engineering.

Different model types are suitable for different Software Engineering Artifacts