Choosing the Right AI Model

What is the best model for my prompt?

This is a very legitimate and frequent question among our users. Our answer is as follows:

➡️ There is no universally "superior" AI model for what you want to do.

Therefore, the best approach is to put models in competition:

• Send your request to a first model
• Reprompt with a second model
• Choose between the two proposals or iterate with a 3rd model

Having multiple options is currently the most reliable way to obtain an excellent quality result.

🧾 Demonstration

To understand this, you need to look at how reference benchmarks like lmsys.org or livebench.ai work. These sites compare AIs on thousands of questions (reasoning, math, code, writing, etc.) to establish statistics. But be careful when interpreting these scores:

• they are probabilities, not guarantees: a model ranked #1 simply has a statistically higher chance of giving a better answer than #5, but for a specific question, #5 may very well be better.
• the gaps are minimal: today, the pure performance differences between market leaders have become tiny, often indistinguishable for everyday use.

We are therefore observing a paradigm shift as AIs progress.

• before: we chose the model that gave the correct answer.
• today: several models give objectively correct answers, so the choice becomes more subjective: writing style, tone of communication, presentation structure, etc...

In summary: the best answer increasingly depends on your personal preference, and no one can predict with certainty which model will best respond to your request before comparing them.

Mammouth's model categories and their associated uses

In Mammouth, you have access to different model categories. To get more relevant answers, select the category suited to your use case.

	Model Category	Recommended Uses
	Text Generation	Writing and communication: emails, articles, reports, marketing content Document analysis and synthesis Translation, correction, and brainstorming
	Image Generation	Visual creation and design: illustrations, mockups, marketing materials Photo editing: background removal, resolution enhancement, format modification
	Web Search	Monitoring and research of recent information Market analysis and competitive intelligence Fact-checking
	Reasoning	Complex problem-solving and logical analysis Advanced code and debugging Strategy development and decision support
	Lightweight Generation	Quick tasks and simple iterations Drafts and basic visual creations

Reprompting: what Mammouth usage data reveals

generation
Our users' statistics confirm that reprompting is a high-value practice: the more complex or creative the request, the more essential comparison between models becomes.

34% of text requests are reprompted

• 24% with another model → the user validates the result by cross-checking 2 AIs
• 12% with 2+ other models → the user challenges 3 or more AIs to obtain the ultimate answer

What this means: as soon as the stakes increase, our users adopt a comparative strategy:

• 🎯 Technical complexity → cross-verification
• ✨ Creative excellence → exploring different tones and angles
• 💎 Optimal formulation → comparison to find the ideal answer

For image generation:

The phenomenon is even more pronounced:

• 41% of requests are reprompted (vs 34% for text)
• 19% with 2+ other models

Why? Images are inherently subjective: visual style, composition, artistic interpretation vary greatly from one model to another. Comparison becomes almost indispensable.

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What this proves

✅ Reprompting is not a waste of time, it's a quality signal: users adopt it spontaneously for their most demanding tasks

✅ 1 in 3 times users consider that a single answer is not enough for important requests

✅ Mammouth's value lies precisely in this ability to compare without friction: reprompting means upgrading quality