Are larger language models always better performers?

Do bigger models score higher on exams?

Yes, larger models consistently outperform smaller ones on standardized tests. In a neurology board-style exam, GPT-4 (the larger model) answered 85% of questions correctly, while GPT-3.5 (the smaller model) got only 66.8% right—a gap of nearly 19 percentage points [6]. On a family medicine in-training exam, GPT-4 scored 86.5%, far above GPT-3.5's 66.3% and Google Bard's 64.2% [4]. Similarly, in oral and maxillofacial surgery questions, GPT-4 led with 76.8% accuracy, followed by Copilot at 72.6%, GPT-3.5 at 62.2%, Gemini at 58.7%, and Llama 2 at 42.5% [3]. These results show a clear pattern: more parameters generally mean higher test scores.

But bigger models can be less reliable—here's the catch

Scaling up has a hidden downside: larger, more instructable models become less reliable. A 2024 Nature study found that as models grow, they shift from avoiding questions (saying 'I don't know') to giving confident but wrong answers, especially on difficult questions that humans often miss [7]. For example, early models would simply refuse to answer, but scaled-up models produce plausible-sounding errors that are harder to spot. The same study showed that while larger models are more stable across different phrasings of the same question, they still have unpredictable 'pockets of variability' where they flip between right and wrong [7]. This means a bigger model might score higher on average but fail unpredictably on individual questions.

Sometimes a smaller, smarter model beats a giant one

In specialized domains, clever architecture can outperform raw scale. A 2025 study on biomolecular language models found that ChaRNABERT, with only 50 million parameters, matched the performance of RiNALMo, a model 13 times larger (650 million parameters) [8]. The key was optimizing tokenization and architectural design rather than just adding more parameters. Similarly, in Chinese medical counseling, a newer AI model (not named) performed markedly better than models evaluated just a year earlier, even though the older models were larger [1]. This shows that algorithmic improvements—not just size—drive real-world gains.

Size alone can't fix deeper problems like human-like understanding

Even the largest models still lack true comprehension. A 2025 study tested GPT-4 (1.5 trillion parameters) against humans on a grammaticality judgment task. GPT-4 was slightly more accurate overall (80% vs. 76%), but it only outperformed humans on grammatical sentences—it was worse on ungrammatical ones [2]. More tellingly, GPT-4 wavered in its answers 12.5% of the time, compared to 9.6% for humans, showing it's less stable [2]. The authors argue that scaling alone is unlikely to fix these issues because models lack 'semantic reference'—they don't connect words to real-world meaning the way humans do [2]. In high-stakes medical settings, none of the models tested on a urogynecology exam achieved the passing score of 80%, with GPT-4 topping out at 61.6% [5]. So while bigger models improve, they don't automatically become trustworthy or human-like.

Sources used in this answer

Related research questions