Eka-Eval : A Comprehensive Evaluation Framework for Large Language Models in Indian Languages

The rapid advancement of Large Language Models (LLMs) has intensified the need for evaluation frameworks that go beyond English centric benchmarks and address the requirements of linguistically diverse regions such as India. We present EKA-EVAL, a unified and production-ready evaluation framework that integrates over 35 benchmarks, including 10 Indic-specific datasets, spanning categories like reasoning, mathematics, tool use, long-context understanding, and reading comprehension. Compared to existing Indian language evaluation tools, EKA-EVAL offers broader benchmark coverage, with built-in support for distributed inference, quantization, and multi-GPU usage. Our systematic comparison positions EKA-EVAL as the first end-to-end, extensible evaluation suite tailored for both global and Indic LLMs, significantly lowering the barrier to multilingual benchmarking. The framework is open-source and publicly available atthis https URLeka-eval and a part of ongoing EKA initiative (this https URL), which aims to scale up to over 100 benchmarks and establish a robust, multilingual evaluation ecosystem for LLMs.

@article{sinha2025_2507.01853,
  title={ Eka-Eval : A Comprehensive Evaluation Framework for Large Language Models in Indian Languages },
  author={ Samridhi Raj Sinha and Rajvee Sheth and Abhishek Upperwal and Mayank Singh },
  journal={arXiv preprint arXiv:2507.01853},
  year={ 2025 }
}