Araştırma Makalesi

A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability

Cilt: 10 Sayı: 1 31 Ağustos 2026
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A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability

Öz

Large language model (LLM) provider selection has become a multi-objective infrastructure decision rather than a simple benchmark ranking exercise. This paper compares major proprietary, open-weight, and hyperscaler-mediated LLM options across four dimensions: performance, scalability, reliability, and sustainability. It synthesizes benchmark evidence, systems literature, provider disclosures, outage data, and environmental studies to show three cross-cutting patterns: benchmark leadership is increasingly fragile under contamination-free and real-world evaluation; sparse and optimized serving architectures are compressing the cost gap between frontier and open-weight systems; and reliability and sustainability disclosures lag behind enterprise adoption. The resulting framework emphasizes task-specific evaluation, tiered model routing, explicit fallback design, and per-query resource accounting as prerequisites for responsible production deployment.

Anahtar Kelimeler

Teşekkür

Generative AI tools were used to support literature search, reference checking, grammar refinement, and figure revision. All AI-assisted outputs were reviewed, verified, and edited by the authors, who remain fully responsible for the content of the manuscript.

Kaynakça

  1. [1] OpenAI, "The next phase of the Microsoft OpenAI partnership," Apr. 27, 2026. https://openai.com/index/next-phase-of-microsoft-partnership/ (accessed May 25, 2026).
  2. [2] DeepSeek-AI, "DeepSeek-V3 technical report," arXiv preprint arXiv:2412.19437, 2024. https://doi.org/10.48550/arXiv.2412.19437
  3. [3] W. Kwon et al., "Efficient memory management for large language model serving with PagedAttention," in Proc. SOSP, 2023, pp. 611-626. https://doi.org/10.1145/3600006.3613165
  4. [4] L. Zheng et al., "SGLang: Efficient execution of structured language model programs," in Proc. NeurIPS, 2024. https://doi.org/10.48550/arXiv.2312.07104
  5. [5] N. Jegham, M. Abdelatti, C. Y. Koh, L. Elmoubarki, and A. Hendawi, "How hungry is AI? Benchmarking energy, water, and carbon footprint of LLM inference," arXiv preprint arXiv:2505.09598, 2025. https://doi.org/10.48550/arXiv.2505.09598
  6. [6] OpenAI, "Why SWE-bench Verified no longer measures frontier coding capabilities," OpenAI Blog, Feb. 23, 2026. https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/ (accessed May 25, 2026).
  7. [7] Q. Zhao, Y. Huang, T. Lv, L. Cui, et al., "MMLU-CF: A contamination-free multi-task language understanding benchmark," in Proc. ACL, 2025, pp. 13371-13391. https://doi.org/10.18653/v1/2025.acl-long.656
  8. [8] F. Yao, Y. Zhuang, Z. Sun, S. Xu, A. Kumar, and J. Shang, "Data contamination can cross language barriers," in Proc. EMNLP, 2024, pp. 17864-17875. https://doi.org/10.18653/v1/2024.emnlp-main.990

Ayrıntılar

Birincil Dil

İngilizce

Konular

Doğal Dil İşleme

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

13 Temmuz 2026

Yayımlanma Tarihi

31 Ağustos 2026

Gönderilme Tarihi

1 Haziran 2026

Kabul Tarihi

13 Temmuz 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 10 Sayı: 1

Kaynak Göster

APA
Hejja, K., & Kurban, R. (2026). A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability. International Journal of Multidisciplinary Studies and Innovative Technologies, 10(1), 46-56. https://doi.org/10.36287/ijmsit.10.1.5
AMA
1.Hejja K, Kurban R. A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability. IJMSIT. 2026;10(1):46-56. doi:10.36287/ijmsit.10.1.5
Chicago
Hejja, Khaled, ve Rifat Kurban. 2026. “A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability”. International Journal of Multidisciplinary Studies and Innovative Technologies 10 (1): 46-56. https://doi.org/10.36287/ijmsit.10.1.5.
EndNote
Hejja K, Kurban R (01 Ağustos 2026) A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability. International Journal of Multidisciplinary Studies and Innovative Technologies 10 1 46–56.
IEEE
[1]K. Hejja ve R. Kurban, “A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability”, IJMSIT, c. 10, sy 1, ss. 46–56, Ağu. 2026, doi: 10.36287/ijmsit.10.1.5.
ISNAD
Hejja, Khaled - Kurban, Rifat. “A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability”. International Journal of Multidisciplinary Studies and Innovative Technologies 10/1 (01 Ağustos 2026): 46-56. https://doi.org/10.36287/ijmsit.10.1.5.
JAMA
1.Hejja K, Kurban R. A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability. IJMSIT. 2026;10:46–56.
MLA
Hejja, Khaled, ve Rifat Kurban. “A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability”. International Journal of Multidisciplinary Studies and Innovative Technologies, c. 10, sy 1, Ağustos 2026, ss. 46-56, doi:10.36287/ijmsit.10.1.5.
Vancouver
1.Khaled Hejja, Rifat Kurban. A Comparative Analysis of Large Language Model Providers: Performance, Scalability, Reliability, and Sustainability. IJMSIT. 01 Ağustos 2026;10(1):46-5. doi:10.36287/ijmsit.10.1.5