Does using different AI models makes any difference?
I often see these infographics where users are pointing out preferred AI model for certain tasks. And while there are AI specialized for certain type of activity, like coding or video generation, there are so many that are characterized as generalist, but still somehow fall into deepsearch category, or good for social media and so on.
And so with new, powerful models dropping every few months, each promising the next leap in performance—a critical question emerges for the efficiency-minded professional: Should I stick with the one I know, or is it time to start mixing my models?
If you’re a Product Manager (or anyone driving outcomes with AI), this decision isn't just about curiosity. It is about optimal resource allocation, output quality, and maintaining your competitive edge.
How many AI models are users running daily?
I don't have a single, definitive number. The data sources do not specify the exact number of distinct AI models an individual runs daily (you won't find 2.7 models per user). However, we can track usage frequency and task diversity, which serves as a powerful proxy for multi-model engagement. However, the data strongly indicates high-frequency use and a high diversity of tasks, which serves as a powerful proxy for multi-model engagement.
Studies show that a significant portion of active GenAI users in the professional services market report using an AI utility at least daily (often over 40% of respondents).
The tools commonly used daily include large language models (LLMs) and systems like ChatGPT, Microsoft Copilot, and Google Gemini. When you use one of these interfaces, you are typically interacting with a single, massive core neural network model hosted remotely. But the average professional's daily workflow is now a stack of tools, each potentially powered by a different model family (e.g., a chatbot, a code assistant, an image generator).
High-frequency users tend to engage these tools for complex needs. Daily users of GenAI tools select on average 4.4 different uses within their personal life and 4.7 different uses within their professional life. This range covers everything from clarifying search results and summarizing research to drafting important text.
Here is the link where I’m basing most of my numbers: https://www.drcf.org.uk/siteassets/drcf/pdf-files/drcf_understanding-consumer-use-of-generative-ai.pdf?v=394341
What is the Value of Sticking to One and What are the Benefits of Mixing?
Choosing a single, robust model and committing to it offers clear efficiency gains:
Output bias, tone, and format are predictable, which is crucial for brand and voice alignment.
Your mastery of a single prompting style means less friction and faster iteration.
Easier to track and manage API costs or subscription tiers for a single vendor.
The multi-model approach is the domain of the power user and the strategic thinker.
Utilize models that excel in narrow domains (e.g., code, logic, creativity) for significantly higher quality and accuracy in critical outputs.
Use a second model to cross-check the output of the first (the Two-Model Sanity Check), minimizing the risk of "hallucinations" in high-stakes reports.
Maintain workflow continuity by pivoting instantly to a backup tool when a primary model is down, rate-limited, or experiencing quality issues.
Do Skills Differ Per AI Models, and How?
For every model, the fundamental skill is the same: Prompt Engineering or the ability to articulate a clear, constrained, and contextual instruction.
However, the subtle differences in the underlying architecture (e.g., the size of the training corpus, the fine-tuning methods) mean that an excellent prompt for Model X might only be a good prompt for Model Y.
So to reply to yes, the skills differ. and here is how I see they do.
The biggest mistake is the monogamous prompting style or better known using the exact same prompt structure across several different models and expecting the same quality. The sophisticated user understands that each model requires a slight dialect shift. So several examples:
For Code/Technical LLMs:
Best-Fit Skillset: Debugging & Syntax Analysis.
Why it Matters: These models are often better at understanding error codes and generating specific API calls. You need to verify the code logic, not just the language, meaning a higher emphasis on technical correctness over flowery prose.
For Creative/Art Models:
Best-Fit Skillset: Visual Literacy & Curation.
Why it Matters: Success is less about the text and more about understanding constraints like aspect ratios, artistic styles, and using negative prompting to steer the output toward a professional aesthetic.
For Logic/Reasoning LLMs:
Best-Fit Skillset: Constraint Definition & Chain-of-Thought.
Why it Matters: You need to be a master of setting guardrails ("Only use facts from Source A") and directing multi-step reasoning ("Think step-by-step before answering"). This focuses the model's energy on analytical depth rather than speed.
Does this mean that users with different specialization and experience can get more out of different models?
A user's existing specialization acts as a powerful amplifier when paired with the right model.
For example, a veteran financial analyst can extract maximum value from a model designed for complex data parsing and structural reasoning because their domain knowledge allows them to spot subtle inaccuracies and define precise parameters.
Conversely, a skilled graphic designer will outperform a non-specialist when using an image generation model because they understand the vocabulary of composition, lighting, and style required for optimal prompting. The model provides the raw intelligence, but the specialist provides the refined steering capability. Leveraging models that complement your deep experience is the most efficient path to maximizing AI output quality.
Final thoughts
In Product Management, you don't use a single tool for everything. You use Jira for tracking, Figma for design, and Amplitude for analytics. Each tool is the "best-of-breed" for its specific job. so why would you use one AI model?
ersonally, I use a combo of five or more models daily. It's my default Modular AI Stack. Today, that might be Google Gemini, NotebookLM, Grok, Leonardo AI, and Kapwing. Sometimes I use them to test performance, other times I set up AI agents to run parallel research and compare results across different vendors. I’m even working on an AI agent to play a turn-based video game.
The difference between using one model and strategically mixing models is the difference between generating content and generating a superior, verifiable outcome. Your goal isn't just to be fast; it's to be right, creative, and resilient. And for that, a multi-model approach is quickly becoming the ultimate competitive advantage.
