Using languages other than English for technical prompts is inefficient because it forces the AI to perform an intermediate translation. This translation step consumes valuable tokens from the context window, leaving less capacity for detailed instructions and increasing the risk of misinterpretation, which results in weaker solutions.

Newer AI models may have low per-token prices but are often "token hungry," requiring more tokens to complete a task. This can make them more expensive overall. The true measure of economic viability is the final cost-per-task, not the misleading per-token price.

While text-based AI models struggle with non-English languages, the problem is exponentially worse for audio models. The lack of diverse, high-quality audio training data (across ages, genders, topics) in various languages is a critical bottleneck for companies aiming for global adoption of audio-first AI.

Beyond the obvious lack of non-English training data, Large Language Models are architecturally biased. Their tokenization process, designed for English, inefficiently breaks down other languages into more fragments. This increases operational costs and reduces comprehension, creating a structural disadvantage.

Models built for multilingual use, like Meta's LLaMA, don't necessarily "think" in multiple languages. They often retrieve answers internally in English and then translate back to the source language. This extra step introduces significant opportunities for error, undermining their multilingual promise and losing knowledge in translation.

Technical terms like "callback" often lack a precise one-to-one translation in other languages. When a non-English prompt is used, the AI may misinterpret these crucial terms, leading it to misunderstand the user's intent, waste context tokens trying to disambiguate the instruction, and ultimately generate incorrect or suboptimal code.

The binary distinction between "reasoning" and "non-reasoning" models is becoming obsolete. The more critical metric is now "token efficiency"—a model's ability to use more tokens only when a task's difficulty requires it. This dynamic token usage is a key differentiator for cost and performance.

The primary reason AI models generate better code from English prompts is their training data composition. Over 90% of AI training sets, along with most technical libraries and documentation, are in English. This means the models' core reasoning pathways for code-related tasks are fundamentally optimized for English.

AI vendors can increase effective costs without altering their price-per-token sheets. When Anthropic updated its model, a new tokenizer produced ~30% more tokens for the same text. While the sticker price was unchanged, real-world bills grew 12-27%—a classic case of 'shrinkflation' applied to AI.