Choose AI Model Based on Your Task
The world of AI is evolving rapidly, and the wide variety of AI models available can sometimes feel overwhelming. The best way to navigate this landscape is to start by focusing on your specific tasks-- what you need to accomplish, and then select the AI model that best supports those tasks. This method is known as the Task-First Approach to Choosing AI Models.
1. Start with What You Need to Do
Before diving into AI model names or technologies, take a moment to look closely at your workplace activities:
What routine or complex tasks take up your time?
Where do you need faster content creation, deeper analysis, or smarter problem-solving?
Which processes involve language, numbers, images, or data review?
Answering these questions helps pinpoint where AI can accelerate your work or add value-- and where it might be less effective.
By grounding your AI choices in actual tasks, you’ll focus your efforts and make smarter decisions.
2. Define the Types of AI Models You Need
Navigating the AI ecosystem effectively starts with understanding that AI models generally fall into three main categories: Generative Models, Reasoning Models and Deep Research Models.
Each type has distinct characteristics, strengths, and ideal use cases based on the kinds of tasks it supports best.
Generative Models
What are they?
A generative model is a pre-trained model designed primarily for generating new content. Trained on massive datasets, they can produce human-like text, images, or other media formats.
Strengths: Fast and efficient for common or repetitive tasks, with strong capabilities in natural language generation. However, they may occasionally sacrifice precision for speed.
Best For:
Writing and editing content
Summarizing information
Answering straightforward questions
Speedy content generation and creative tasks
They work well for repetitive, common tasks requiring quick outputs, although sometimes accuracy might be limited compared to more specialized models.
Use Cases: Email drafting, summarizing lengthy documents, brainstorming ideas, creating marketing copy, and generating conversational responses, e.g.
Available Generative AI Models in Doraverse:
ChatGPT: GPT-4o, GPT-4.1, GPT-4.1-mini
Llama: Llama 4 Maverick, Llama 4 Scout
Claude: Claude 3.5 Haiku
Perplexity: Sonar, Sonar Pro
SuperGrok: Grok 3
DeepSeek: Deepseek Chat (DeepSeek-V3)
Reasoning Models
What Are They?
A reasoning model is a type of model that can perform complex reasoning tasks. Instead of quickly generating output based solely on a statistical guess of what the next word should be in an answer, as an LLM typically does, a reasoning model will take time to break a question down into individual steps and work through a “Chain of Thought” (CoT) process to come up with a more accurate answer. In that manner, a reasoning model is much more human-like in its approach.
Strengths: Provide explainable outputs that help users understand how conclusions are reached. This elevates confidence in AI suggestions, especially for strategic or technical tasks.
Best For: Complex problem-solving, code debugging, detailed data analysis, fact-checking, e.g.
Use Cases: Debugging software code, extracting insights from large datasets, evaluating AI-generated content authenticity, and making high-stakes decisions based on AI analysis.
Available Reasoning AI Models in Doraverse:
ChatGPT: o4-mini, o3-mnini
Gemini Advanced: Gemini 2.5 Flash, Gemini 2.5 Pro
Claude: Claude 3.7 Sonnet
Perplexity: Sonar Reasoning, Sonar Reasoning Pro
SuperGrok: Grok 3 Mini (Thinking)
DeepSeek: Deepseek Reasoner (DeepSeek-R1)
Deep Research AI
What Are They? A deep research AI is an advanced AI systems integrated with extensive external data sources that conduct in-depth research and synthesis. They crawl across diverse information pools to compile comprehensive reports.
Strengths: Highly thorough and accurate due to cross-referencing multiple sources. Tend to have slower response times because of the intensive data retrieval and processing.
Best For: Market research, scientific and technical analysis, PhD-level work, competitive intelligence gathering.
Use Cases: Generating detailed market analysis reports, conducting academic or scientific literature reviews, trend forecasting, and compiling exhaustive competitive intelligence.
Available Deep Research AI in Doraverse:
Perplexity: Sonar Deep Research
After understanding the importance of starting with your specific tasks, the next step is understanding AI models’ capabilities. For a deeper dive, check out our detailed guide on choosing AI models based on capabilities.
Last updated