🏴‍☠️ Which AI for Which Task

Stop blindly guessing which AI model fits your current project. Using the wrong large language model for a specific task isn’t just inefficient; it yields subpar results that require heavy editing.

I recently stumbled across a comprehensive analysis by a LinkedIn creator who decided to benchmark the industry giants against one another. The expert looked at ChatGPT, Grok, Mistral Le Chat, Claude, and Meta AI to determine where each one truly shines. Instead of a generic overview, this breakdown assesses them across thirteen distinct categories, including text generation, code generation, data analysis, and even video creation capabilities.

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Mapping Capabilities to Tasks

The core mechanism of this analysis is recognizing that “AI” is not a monolith. The author’s framework suggests that we need to move away from brand loyalty and toward task-based selection. By evaluating models on specific features like file inputs, presentation creation, and website building, the creator provides a matrix for decision-making.

If you need deep research, one model takes the lead; if you need to visualize data or generate an app, another might be the superior choice. This approach turns your workflow into a specialized assembly line rather than a one-stop-shop that does everything merely “okay.”

Specialization is the New Standard

One of the most critical takeaways from this industry pro’s comparison is the stark divide between creative and technical capabilities. While many users default to a single model for everything, the analysis suggests that code generation and app creation require different underlying strengths than document creation or learning assistance.

For instance, a model optimized for logic and syntax will save you hours of debugging, whereas a model fine-tuned for natural language nuances will produce writing that sounds far less robotic. The creator implies that saving hours of trial and error comes down to knowing these “superpowers” in advance.

Beyond Text: The Multimodal Frontier

The comparison highlights how far we have moved past simple text-based chat interfaces. The expert includes categories like data visualization, image generation, and video generation, signaling a shift in what we should expect from our tools.

The ability to handle file inputs is a massive differentiator mentioned in the post. This feature allows users to upload spreadsheets or heavy documentation for analysis, transforming the AI from a writer into an analyst. By listing these advanced features, the author encourages us to push these tools to their limits rather than just using them as glorified search engines.

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The Operational Safety Protocol

Perhaps the most valuable part of the post is the strict set of “Do’s and Don’ts” the original poster outlines for safe usage. It is easy to get swept up in the magic of automation, but the author reminds us to verify every single output against trusted sources.

The guidance is clear: never feed confidential or regulated data into generic tools, and strictly avoid relying on AI for high-risk decisions without human oversight. This insight serves as a necessary grounding wire, ensuring that while we leverage these powerful engines for speed, we maintain ethical standards and data privacy.

The Hallucination Hurdle

Despite the power of these models, the creator flags a persistent challenge: the reliability of the information. The warning against treating hallucinations or fabricated details as facts is a crucial reminder. Even the most advanced model in the comparison list can confidently generate incorrect information.

You really need to see the full breakdown to know which specific model won each category.

Check the link for the full comparison.

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