Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps

Researchers conducted the first systematic performance benchmark of AI video chat systems across six mainstream applications, measuring quality, latency, internal mechanisms, and system overhead. The study reveals that network latency impacts AI video calls less significantly than human video calls, while AI agent capabilities emerge as the primary driver of user experience.

The emergence of AI video chat represents a meaningful evolution in human-AI interaction, moving beyond text-based interfaces to real-time multimodal communication. This research addresses a critical gap in the field by establishing the first comprehensive benchmark framework for evaluating these systems. The timing is significant as LLM providers race to integrate video calling capabilities into their platforms, creating competitive pressure to optimize user experience without systematic performance baselines.

The findings challenge conventional assumptions about video communication design inherited from human-to-human video call optimization. The discovery that network latency matters less for AI interactions suggests that current network infrastructure may be less of a constraint than previously assumed, potentially lowering deployment barriers for AI video services. Conversely, the emphasis on AI agent capabilities as the primary experience driver indicates that competitive differentiation will hinge on model quality, reasoning speed, and contextual understanding rather than infrastructure investments.

For the developer and investor ecosystem, this benchmark framework provides critical performance metrics that could influence product development priorities and infrastructure spending decisions. Companies optimizing AI video chat systems can now allocate resources more effectively toward capability improvements rather than pursuing marginal latency gains. The open-sourced dataset and online evaluation platform democratize access to rigorous benchmarking, potentially accelerating industry-wide improvements. Moving forward, the research landscape will likely shift toward understanding how multimodal AI capabilities translate to real-world user satisfaction, with particular focus on balancing response quality against processing latency tradeoffs.

• →Network latency has less impact on AI video chat experience than on human-to-human video calls, challenging traditional optimization assumptions.
• →AI agent capabilities and model quality are the primary determinants of user experience in video chat applications.
• →The first comprehensive benchmark framework for AI video chat systems spans quality, latency, mechanisms, and system overhead across six platforms.
• →Open-sourced benchmarking tools and datasets enable broader industry optimization and reduce barriers to performance evaluation.
• →Future AI video chat development should prioritize capability improvements over infrastructure optimization based on measurement findings.