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Journals / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 2

Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma

Ömer Aydın(MANİSA CELÂL BAYAR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-7137-4881
Enis Karaarslan(MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ)0000-0002-3595-8783
Fatih Safa Erenay(University of Waterloo)0000-0002-3408-0366
Nebojsa Bacanin(Singidunum University)0000-0002-2062-924X
Pages
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Publication Date
2026-05-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results indicate that paraphrased abstracts showed higher similarity rates, while question-based responses also exceeded acceptable levels.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:DeepSeek v3, developed in China, was released in December 2024, followed by Alibaba’s Qwen 2.5 Max in January 2025 and Qwen3 235B in April 2025.
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Key Findings & Data:These free and open-source models offer significant potential for academic writing and content creation.
💡
Conclusion & Impact:Results indicate that paraphrased abstracts showed higher similarity rates, while question-based responses also exceeded acceptable levels.
🔬Design:Modeling & Simulation
📍Scope:2024 Verileri
⚙️Variables:Generative AI · Academic Writing · Literature Review

Abstract

DeepSeek v3, developed in China, was released in December 2024, followed by Alibaba’s Qwen 2.5 Max in January 2025 and Qwen3 235B in April 2025. These free and open-source models offer significant potential for academic writing and content creation. This study evaluates their academic writing performance by comparing them with ChatGPT, Gemini, Llama, Mistral, and Gemma. There is a critical gap in the literature concerning how extensively these tools can be utilized and their potential to generate original content in terms of quality, readability, and effectiveness. Using 40 papers on Digital Twin and Healthcare, texts were generated through AI tools based on posed questions and paraphrased abstracts. The generated content was analyzed using similarity detection, AI detection, word count comparisons, semantic similarity, and readability assessments. Results indicate that paraphrased abstracts showed higher similarity rates, while question-based responses also exceeded acceptable levels. AI detection tools consistently identified all outputs as AI-generated. Word count analysis revealed that all chatbots produced a sufficient volume of content. Semantic similarity tests showed a strong overlap between generated and original texts. However, readability assessments indicated that the texts were insufficient in terms of clarity and accessibility. This study comparatively highlights the potential and limitations of popular and latest large language models for academic writing. While these models generate substantial and semantically accurate content, concerns regarding plagiarism, AI detection, and readability must be addressed for their effective use in scholarly work.

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Cite This Article

Aydın, Ö., Karaarslan, E., Erenay, F. S., Bacanin, N. (2026). Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma. Turkish Journal of Engineering, Volume: 10 Issue: 2. https://doi.org/10.31127/tuje.1838765

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