The Model You Love Is Probably Just the One You Use

2026-04-01 · Source: AI & ML – Radar · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

Developers often favor the Large Language Models (LLMs) they are accustomed to using, rather than those objectively superior, due to practical integration, learned quirks, and established workflows. This phenomenon, dubbed "the model you love is probably just the one you use," creates a "local maximum" where productivity is high with a chosen model but potentially misses significant improvements from others. Objective comparison is challenging due to numerous variables like model size, architecture, training data, fine-tuning, prompting strategies, and evaluation metrics. Factors such as access limitations, cost considerations for proprietary versus open-source models, and ease of integration further influence model adoption. The "best" model is highly context-dependent, varying by use case, performance requirements, budget, and latency constraints.

Key takeaway

For AI Engineers evaluating LLMs, acknowledge your inherent bias towards familiar tools. Instead of defaulting to what you know, define clear project criteria, conduct rigorous benchmarking, and actively experiment with alternative models. This approach helps avoid local maxima and ensures you select the most effective and cost-efficient LLM for your specific application, rather than just the easiest to integrate.

Key insights

Developer preference for LLMs is often driven by familiarity and integration ease, not objective superiority.

Principles

• Familiarity creates a "local maximum" in tool adoption.
• "Best" LLM is context-dependent, not universal.
• Bias awareness is the first step to objective evaluation.

Method

To objectively select LLMs, define criteria, benchmark rigorously, experiment with diverse models, consider fine-tuning, and stay informed on new releases and research.

In practice

• Define specific performance and budget requirements.
• Use standardized benchmarks and custom evaluation metrics.
• Allocate resources for experimenting with new models.

Topics

• LLM Selection Bias
• Objective LLM Evaluation
• Local Maxima Problem
• LLM Integration Challenges
• Fine-tuning Strategies

Best for: AI Engineer, Machine Learning Engineer, Director of AI/ML

Related on AIssential

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• The signal is the ceiling: Measurement limits of LLM-predicted experience ratings from open-ended survey text — Input text characteristics limit LLM prediction accuracy more than prompt or model choice.
• Shubhamsaboo / awesome-llm-apps — This repository provides a comprehensive collection of LLM applications and tutorials.
• How I Choose Between GPT, Claude, Gemini and Open Source Models for Every Task — Optimizing LLM usage requires a decision framework matching tasks to models for cost and quality.
• To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling — LLMs often misjudge when to use external tools, leading to suboptimal performance and inefficient resource use.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI & ML – Radar.