COMPARISON OF TELEGRAM BOT FOR TEXT GENERATION BUILT ON OPENAI AND GEMINIAI
DOI:
https://doi.org/10.28925/2663-4023.2026.32.1063Keywords:
chatbot, generative models, artificial intelligence, OpenAI GPT, Gemini 2.5 Pro;, transformer, Telegram bot, ; text generation, natural language processing, comparative analysisAbstract
The study examines the problem of developing and conducting a comparative analysis of modern generative language models, using OpenAI GPT and Gemini 2.5 Pro integrated into a Telegram bot for automated text generation. The relevance of the research is driven by the increasing demand for fast, flexible, and high-quality content creation tools capable of operating in real time. Within the work, the requirements for the software tool were defined, the architecture of the Telegram bot was developed, the principles of its operation were determined, and the interaction with the APIs of both models was implemented. Special attention is given to testing the quality of generated texts, response speed, model stability, and their behavior under load. Experimental results indicate that Gemini 2.5 Pro demonstrates significantly higher generation speed and more precise interpretation of user queries, whereas OpenAI GPT excels in creative tasks, stylistic naturalness, and flexibility of formulations. Both models show strong performance across multiple text genres, though they differ in structural coherence, logic, and context retention.The results of the study show that neither model has an absolute advantage; the choice depends on specific tasks and requirements for style, accuracy, or speed of response. The developed Telegram bot confirms the effectiveness of integrating generative models into practical systems and demonstrates adaptability to various usage scenarios. The findings can be applied to further optimization of generative systems, model selection for specific applications, and the development of intelligent AI-based services.
Downloads
References
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS 2017).
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., et al. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv. https://arxiv.org/abs/1910.10683
Min, B., Ross, H., Sulem, E., Pouran Ben Veyseh, A., Nguyen, T. H., Sainz, O., Agirre, E., Heinz, I., & Roth, D. (2021). Recent advances in natural language processing via large pre-trained language models: A survey. arXiv. https://arxiv.org/abs/2111.01243
Kalyan, S. S. (2023). A survey of GPT-3 family large language models including ChatGPT and GPT-4. arXiv. https://arxiv.org/abs/2301.06627
Amvera. (2024). Integration of GPT-4 Omni model into a Telegram bot in Python. Habr. https://habr.com
TeLLMgramBot. (n.d.). Library for building Telegram bots with AI model support. PyPI. https://pypi.org
Speka Media. (2023). How artificial intelligence is used in SEO and content marketing. https://speka.media
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”. (2024). Context-aware text transformation using ChatGPT [Bachelor’s/Master’s thesis]. Electronic Library KPI. Zaporizhzhia National University. (n.d.). Machine learning course materials. https://moodle.znu.edu.ua
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Оксана Онищук

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.