# From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini

arXiv: https://arxiv.org/abs/2510.11269 · HTML: https://mlsanity.com/paper/2510.11269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [NetLLM: Adapting Large Language Models for Networking](https://mlsanity.com/paper/2402.02338/) (2024)
- [Large Language Models for Networking: Workflow, Advances and Challenges](https://mlsanity.com/paper/2404.12901/) (2024)
- [Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects](https://mlsanity.com/paper/2503.04783/) (2025)
- [Journey of Hallucination-minimized Generative AI Solutions for Financial Decision Makers](https://mlsanity.com/paper/2311.10961/) (2023)
- [Cloud-gaming:Analysis of Google Stadia traffic](https://mlsanity.com/paper/2009.09786/) (2020)
- [Characterizing User Platforms for Video Streaming in Broadband Networks](https://mlsanity.com/paper/2408.16995/) (2024)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking](https://mlsanity.com/paper/1804.09914/) (2018)
- [Network Anatomy and Real-Time Measurement of Nvidia GeForce NOW Cloud Gaming](https://mlsanity.com/paper/2401.06366/) (2024)
- [MetaVRadar: Measuring Metaverse Virtual Reality Network Activity](https://mlsanity.com/paper/2402.08286/) (2024)
- [Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation](https://mlsanity.com/paper/2112.02637/) (2021)

## Abstract (arXiv)

GenAI chatbots are now pervasive in digital ecosystems, fundamentally reshaping user interactions over the Internet. Their reliance on an always-online, cloud-centric operating model introduces novel traffic dynamics that challenge practical network management. Despite the critical need to anticipate these changes in network demand, the traffic characterization of these chatbots remains largely underexplored. To fill this gap, this study presents an in-depth traffic analysis of ChatGPT, Copilot, and Gemini used via Android mobile apps. Using a dedicated capture architecture, we collect two complementary datasets, combining unconstrained user interactions with a controlled workload of selected prompts for both text and image generation. This dual design allows us to address practical research questions on the distinctiveness of chatbot traffic, its divergence from that of conventional messaging apps, and its novel implications for network usage. To this end, we provide a multi-granular traffic characterization and model packet-sequence dynamics to uncover the underlying transmission mechanisms. Our analysis reveals app-/content-specific traffic patterns and distinctive protocol footprints. We highlight the predominance of TLS, with Gemini extensively leveraging QUIC, ChatGPT exclusively using TLS 1.3, and characteristic Server Name Indication (SNI) values. Through occlusion analysis, we quantify the reliance on SNI for traffic visibility, demonstrating that masking this field reduces classification performance by up to 20 percentage points. Finally, the comparison with conventional messaging apps confirms that GenAI workloads introduce novel stress factors, such as sustained upstream activity and high-rate bursts, with direct implications for capacity planning and network management. We publicly release the datasets to support reproducibility and foster extensions to other use cases.