The performance gap in LLMs between English and other languages is driven not by linguistic complexity, but by technical factors: the distribution of training data, the inefficiency of tokenizers, and a lack of instruction-tuning for underrepresented languages.
What Happened
Technical model limitations create a substantial gap in quality and usage costs. Inefficient tokenization makes working with non-English languages 3–4 times more expensive and reduces the effective context window. Furthermore, the accuracy of information retrieval tasks in these languages can be up to 29% lower compared to English.
Context
The problem is rooted in the imbalance of training datasets and architectural tokenization features that are predominantly optimized for the English language. This creates technical inefficiency when deploying global products that require high accuracy and low operational costs.
Why It Matters for the Industry
The industry needs to rethink its development strategy: instead of creating universal multilingual models, there may be a need to shift toward specialized regional solutions. This will open niches for creating models with optimized tokenization and architectures focused on high-performance serving within specific language groups.
Why It Matters for Users
For users and developers, this means rising API costs and decreased response quality when working with non-English content. A recommended best practice is to use English prompts to drive the model's logic, followed by a request for the response in the target language (the "English logic + Target language output" pattern).
What Is Not Yet Known / Limitations
There is a difference in how the impact of this problem is assessed: technical specialists focus on computational efficiency and context, while business entities emphasize the market opportunities for regional solutions.
Sources
Author
Look at AI, Editorial Team