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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Beyond the model: Key differentiators in large language models and multi-agent services

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  • Beyond the model: Key differentiators in large language models and multi-agent services

Muskaan Goyal 1, * and Pranav Bhasin 2

1 Department of Computer Science, University of California, Berkeley, United States.
2 Department of Electrical Engineering, University of California, Berkeley, United States.

Review Article

World Journal of Advanced Research and Reviews, 2025, 26(01), 2703-2706

Article DOI: 10.30574/wjarr.2025.26.1.1295

DOI url: https://doi.org/10.30574/wjarr.2025.26.1.1295

Received on 07 March 2025; revised on 14 April 2025; accepted on 16 April 2025

With the launch of foundation models like DeepSeek, Manus AI, and Llama 4, it has become evident that large language models (LLMs) are no longer the sole defining factor in generative AI. As many now operate at comparable levels of capability, the real race is not about having the biggest model but optimizing the surrounding ecosystem, including data quality and management, computational efficiency, latency, and evaluation frameworks. This review article delves into these critical differentiators that ensure modern AI services are efficient and profitable.

LLM Ecosystem Optimization; Multi-Agent Systems; Computational Efficiency; Evaluation Framework; Data Management; Latency and Cost Reduction

https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-1295.pdf

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Muskaan Goyal and Pranav Bhasin. Beyond the model: Key differentiators in large language models and multi-agent services. World Journal of Advanced Research and Reviews, 2025, 26(1), 2703-2706. Article DOI: https://doi.org/10.30574/wjarr.2025.26.1.1295

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


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