Chew Loong Nian Finds Llama 3.2 Token Costs Soar for Bengali, YAML Configuration

The Bengali word for human, “মানুষ,” is five characters and fifteen bytes. When Chew Loong Nian, an AI Engineer, ran it through Gemma 3’s tokenizer, it returned a single token. Llama 3.2’s tokenizer, however, produced eight tokens for the exact same word, a stark difference in processing cost and efficiency for multilingual applications.

This specific finding, first reported by Towards AI, highlights a critical, often hidden, variable in the operational economics of large language models: tokenization. Nian’s research examined how seven popular tokenizer vocabularies consumed tokens for identical content across 21 languages. While tokenizers largely agreed on English content, typically within a four percent variance, their efficiency diverged by up to 4.95 times when processing non-English languages.

This is not an isolated observation in Nian’s recent work. Earlier research, also published on Towards AI, explored tokenization efficiency for structured data formats. Nian compared YAML and JSON representations of a Kubernetes-shaped configuration object. He found that across a hundred records, YAML was three percent smaller in bytes than minified JSON. Despite its smaller byte footprint, YAML consistently consumed 21 percent more tokens when processed by tokenizers from GPT-5, Llama, and Qwen. An unmentioned alternative format, which Nian described as “nobody suggests,” managed to halve the token count again.

Chew Loong Nian’s investigations consistently point to a fundamental challenge for engineers building on LLMs: the disconnect between raw data size and the actual computational cost determined by tokenization. Whether it is the character set of a spoken language or the syntax of a configuration file, the choice of tokenizer and the underlying model’s vocabulary significantly impact how much “work” an LLM perceives. This directly translates into higher API costs, reduced effective context windows, and potentially slower inference for applications that deal with diverse inputs.

The implication is clear for any organization deploying AI models. Relying solely on byte size as a proxy for input cost is a mistake. A single Bengali word costing eight tokens on one model versus one token on another can drastically alter the economics of a global customer support bot or a translation service. Similarly, choosing YAML over JSON for internal configuration management, while potentially offering readability benefits, incurs a measurable token penalty. This kind of hidden inefficiency forces engineers to choose between developer convenience, data representation, and operational expense.

Nian’s work provides concrete, benchmarked data that moves beyond theoretical discussions of tokenizer design. It offers a practical guide for developers and product managers to understand where their LLM costs truly originate. The divergence in token efficiency, particularly for non-English content and structured data, suggests that a “one-size-fits-all” approach to LLM integration is financially inefficient. Instead, careful consideration of the specific data types an application handles, and a corresponding choice of model and tokenizer, becomes essential for managing operational budgets and maximizing model utility.

The findings from “Llama 3.2 Needs Eight Tokens For One Bengali Word. Gemma 3 Needs One” and “GPT-5, Llama And Qwen Agree: YAML Is Smaller Than JSON And Costs More Tokens” together paint a picture of an AI engineer focused on the practical, often overlooked, mechanics of LLM interaction. Nian’s research shows that tokenization is not a solved problem, but an active area where significant cost and performance differences still exist across leading models and data types. For developers building systems today, these are not academic curiosities but direct inputs into their architecture decisions. The 4.95x token cost difference for non-English content, as reported today, is a concrete example of this engineering challenge.

How many tokens did Llama 3.2 use for the Bengali word “মানুষ”?

Llama 3.2’s tokenizer used eight tokens for the Bengali word “মানুষ,” while Gemma 3’s tokenizer used one token for the same word.

What is the token cost difference between YAML and JSON for configuration objects?

YAML, despite being three percent fewer bytes than minified JSON, consumed 21 percent more tokens across a hundred records when processed by models like GPT-5, Llama, and Qwen.

Compiled by Launch91 Desk from the sources linked above. More about Launch91.