New speech model from OpenAI (gpt-audio-1.5) does not understand other languages except English
By Holidays in Europe / March 11, 2026 / No Comments / Uncategorized
OpenAI Introduces GPT-Audio-1.5: Powerful English Speech Model with Language Limitations
OpenAI has announced the release of their latest speech models, GPT-Realtime 1.5 and GPT Audio 1.5, marking significant advancements in natural language processing capabilities. These new models are designed to enhance real-time voice interactions and audio understanding, promising improved speed and accuracy.
Having previously utilized the GPT Audio model for a voice note-taking application, I was eager to test the new GPT Audio 1.5 to evaluate its performance. Preliminary tests revealed that, when operating in English, the model performs remarkably well. In fact, it matches or even surpasses the speed of leading models like Google’s Gemini, exhibiting near-instantaneous response times comparable to the Gemini Flash architecture. This efficiency underscores OpenAI’s ongoing commitment to delivering high-performance AI solutions for speech processing tasks.
However, a notable limitation became immediately apparent during my testing: GPT-Audio-1.5’s language comprehension is exclusively effective for English. Despite its impressive capabilities in English, it fails to understand or process other languages at all. This stark language restriction was a surprise, especially given the increasingly multilingual demands of modern AI applications.
In summary, OpenAI’s GPT-Audio-1.5 demonstrates impressive speed and accuracy within English speech recognition and processing. Nevertheless, its lack of multilingual support significantly limits its versatility for global applications. Developers and users should consider this language constraint when integrating or deploying the model in multilingual contexts.
As OpenAI continues to refine their speech models, future updates may address these language limitations. For now, GPT-Audio-1.5 stands out as an advanced tool for English-centric speech recognition endeavors, but alternative solutions will be necessary for multi-language requirements.