Benchmark-Based Comparative Assessment of Publicly Benchmarked Indian Foundation Models: A Capability and Evaluation-Maturity Framework

Governments are investing in national foundation models to enhance AI capabilities and digital sovereignty. This paper introduces a structured framework for comparing publicly benchmarked Indian foundation models against global counterparts across eight key capability domains. The assessment addresses challenges posed by inconsistent reporting and proprietary evaluation methods, focusing on areas from general reasoning and coding to cybersecurity and Indic language capabilities.

Why it matters

The development and robust assessment of national foundation models are crucial for countries aiming to secure digital sovereignty and advance their technological independence. This framework offers a standardized approach to evaluate progress, ensuring informed strategic investment and resource allocation in critical AI capabilities.

Key insights

• Governments are increasingly funding indigenous foundation models to boost national AI capability, digital sovereignty, and multilingual computing.
• Assessing national AI ecosystems is challenging due to inconsistent benchmark reporting, proprietary evaluation methodologies, and rapid model evolution.
• The paper proposes a structured, benchmark-based comparative assessment method.
• The assessment compares publicly benchmarked Indian foundation models against global frontier and comparable-scale models.
• Eight specific capability domains are evaluated: general-purpose reasoning, coding/software engineering, agentic AI/computer use, cybersecurity, vision/image understanding, video/multimodal understanding, scientific research, and Indic language capabilities.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.11891