评估印度大模型能力与评测成熟度，揭示真实差距与评测生态短板。

Purpose: Governments increasingly fund indigenous foundation models to strengthen national AI capability, digital sovereignty, and multilingual computing. This paper assesses India's foundation-model ecosystem and examines whether apparent capability gaps in public benchmark evidence may also reflect gaps in evaluation maturity. Approach: The paper presents a structured, benchmark-based comparative assessment of Indian foundation models against global frontier and comparable-scale models across eight capability domains: general-purpose reasoning, coding and software engineering, agentic AI and computer use, cybersecurity, vision and image understanding, video and multimodal understanding, scientific research, and Indic language capability. Using only publicly reported results, it proposes an exploratory four-dimension Benchmark Maturity Index (BMI), scoring each domain on standardization, participation, independent verification, and national Findings: Indian models achieve strong scores on established benchmarks such as MMLU and MATH-500. However, these are now widely regarded as saturated, and frontier developers no longer report them. Indian models participate far less frequently in newer, agentic, and domain-specialized evaluations, and participation is highly uneven across organizations. Sarvam AI reports the broadest coverage by a substantial margin. The BMI refines, and in some cases revises, the maturity judgments a purely descriptive review would produce. Practical implications: Many apparent capability gaps cannot be distinguished, on available evidence, from evaluation-ecosystem gaps, with direct implications for how national AI programs should design monitoring and funding criteria. Originality: The paper proposes BMI as a reusable instrument for scoring evaluation-ecosystem maturity at the domain level and demonstrates its application to the Indian foundation-model ecosystem.