Can OpenAI’s new “IndQA” benchmark help close the gap between Indic LLMs?

OpenAI’s ‘IndQA’ Benchmark: A Step Toward Closing the Gap for Indic LLMs?

India’s AI landscape is currently at a turning point. The country, with its 22 recognized languages and numerous dialects, is a double-edged sword for language models in terms of opportunity and difficulty. While the English-centric global LLMs like GPT-4, Claude, and Gemini are at the forefront, their effectiveness in Indian languages remains significantly behind.

To bridge this gap, OpenAI has released a new evaluation standard called ‘IndQA’ — which is basically a dataset created to measure how accurately LLMs interpret and reply in Indic languages. IndQA has the potential to be a watershed moment for the country’s AI residing in different languages, particularly as regional developers are busy building indigenous models that can integrate local context, syntax, and cultural subtleties perfectly.

Still, the main issue persists: Is it possible that a standard such as IndQA would really enable Indian LLMs to compete with global ones in terms of performance? Let us delve into this issue thoroughly.

What Is IndQA?

IndQA, the full name of which is Indic Question Answering Benchmark, is an evaluation dataset that is structured to assess the ability of an AI model to comprehend, reason, and generate correct answers in Indian languages.

In other words, IndQA is asking — “Does an AI really understand and answer like a human speaker if it claims to know Hindi or Tamil?”

The dataset has thousands of question-answer pairs that are divided among the most spoken Indian languages like Hindi, Bengali, Tamil, Telugu, Marathi, Kannada, and Malayalam, etc. Each pair is selected with linguistic and cultural considerations to make sure that AI systems are tested on the basis of both language fluency and cultural accuracy.

IndQA, in contrast to other general translation datasets, is more about measuring actual comprehension. It is not simply about the AI model’s converting local idioms, metaphors, and expressions to English equivalents; it rather checks whether the model really grasps the meaning of those.

Why IndQA Matters for India’s AI Ecosystem

India’s digital transformation has opened up massive possibilities for AI to be implemented. The annual increase in the number of internet users who are not English speaking is in millions nowadays. However, most of the already available AI tools are still operating at their best only in English.

This situation affects particularly the areas where regional languages are the most spoken as it is mainly in these areas where the use of AI is the most promising, e.g., education, healthcare, agriculture, and governance.

IndQA is the solution that offers a unified and clear approach to assessing Indian languages models. It allows the tracking of native language comprehension by researchers, developers, and policymakers.

Moreover, IndQA also plays a role in expanding the boundaries of AI development. It provides small startups, research centers, and universities with a common yardstick to find out how their Indic models measure against world LLMs. This implies that the Hindi-first AI made in Delhi or the Tamil voice assistant developed in Chennai will now be assessed based on measurable parameters rather than on assumptions.

How IndQA Works

The IndQA benchmark follows a simple but powerful structure — the question-answer format. Each question is written in a native language and tests comprehension across different knowledge areas, including:

General Knowledge: Questions rooted in Indian geography, history, and culture.
Reasoning: Logical and contextual problem-solving in regional syntax.
Conversational Contexts: Everyday communication patterns that reveal linguistic fluency.
Local Expressions: Phrases and idioms that carry cultural weight.

AI models are then evaluated based on four primary criteria:

Accuracy: How correct is the answer?
Fluency: Does it sound natural in the native language?
Relevance: Does it directly respond to the intent of the question?
Cultural Understanding: Does it grasp local context, idioms, or tone?

This format pushes LLMs beyond rote translation. It demands real linguistic intelligence — the ability to think and respond like a native speaker, not about a native speaker.

The Unique Challenge of Indic Languages

Indian languages present an entirely different level of complexity for AI systems. Each language has its own script, syntax, grammatical structure, and rhythm.

For example:

Hindi and Urdu share vocabulary roots but use different scripts.
Tamil and Telugu have complex morphology that challenges token-based AI systems.
Bengali and Assamese share phonetics but differ semantically.

Moreover, cultural context varies significantly across regions. An idiom in Marathi might sound confusing when translated into Malayalam, even if both languages belong to the Indo-Aryan family.

This diversity makes it difficult for global LLMs trained on mostly English datasets to perform well in Indic contexts. IndQA steps in as a reality check, offering a granular way to see where models fail — and why.

Why IndQA Could Be a Game-Changer

IndQA may not train models directly, but it empowers developers to improve them faster. By providing consistent feedback, it helps AI teams identify where their LLMs struggle — whether in understanding grammar, tone, or cultural nuance.

Developers can then fine-tune their datasets, retrain their models, and benchmark results again. Over time, this iterative process can drastically enhance the overall quality of Indic AI systems.

In other words, IndQA acts as a mirror. It reflects both the strengths and the blind spots of language models, enabling India’s AI community to make data-driven improvements rather than rely on guesswork.

The Role of OpenAI in Indic AI Development

OpenAI’s involvement in creating IndQA signals a broader commitment to linguistic diversity. Historically, global AI research has leaned heavily toward Western languages, leading to what experts call the Anglocentric bias in machine learning.

By introducing IndQA, OpenAI acknowledges India’s multi-lingual digital potential. It also opens the door for collaboration between global research bodies and Indian institutions, ensuring that future models are trained, tested, and optimized for India’s linguistic reality.

This could accelerate the growth of multilingual digital ecosystems, where AI doesn’t just translate — it understands.

Advantages of IndQA

Unified Standard: IndQA provides a single, transparent way to compare Indic LLMs across languages.
Encourages Research: Universities, startups, and open-source AI projects now have a reliable benchmark for evaluation.
Boosts Inclusivity: It ensures linguistic representation for regional languages often ignored in mainstream AI development.
Accelerates Innovation: Developers can use IndQA feedback loops to improve translation tools, chatbots, and education platforms.
Strengthens Localization: By testing cultural understanding, IndQA helps models adapt to real-world Indian use cases — from customer service to healthcare.

What IndQA Doesn’t Solve Yet

While IndQA lays a strong foundation, some challenges persist.

Limited Dialect Coverage: Many Indian dialects remain underrepresented.
Bias in Dataset Creation: Curated data can still reflect unconscious cultural or gender biases.
Low Adoption: Unless major tech firms integrate IndQA testing, its impact may stay limited to research.
Contextual Depth: Cultural idioms and slang evolve quickly, making static datasets less adaptive over time.

For IndQA to achieve its full potential, Indian AI developers must actively contribute datasets, share results, and push adoption across public and private sectors.

How Indian Developers Can Leverage IndQA

For Indian startups and researchers, IndQA is not just a tool — it’s a competitive advantage.

Developers can integrate the benchmark into their LLM pipelines to identify weaknesses early and train smarter. It allows them to create transparent performance reports that attract investors, users, and collaborations.

Moreover, it encourages data diversity. Teams can collect region-specific examples, fine-tune models for dialects, and test them against IndQA’s framework. Over time, this can create an open ecosystem of truly multilingual AI technologies.

The Future of Indic LLMs

India’s AI community is already innovating with models like BharatGPT, Krutrim, and Hanooman. These efforts aim to build localized intelligence that understands the cultural and emotional depth of Indian languages.

However, without standardized evaluation, measuring their success remains subjective. IndQA fills that gap by providing measurable quality assurance.

In the next few years, India could see LLMs that not only translate but also reason in Hindi, narrate in Bengali, and debate in Tamil — all with native fluency. IndQA could be the catalyst that brings this transformation to life.

Conclusion

OpenAI’s IndQA benchmark is more than a technical project — it’s a vision for inclusive AI. It recognizes that intelligence isn’t just about understanding English; it’s about understanding people in their native voices.

By offering a transparent evaluation framework, IndQA gives India’s AI ecosystem a fair chance to rise to global standards. It won’t close the language gap overnight, but it lays the foundation for sustainable progress — one that celebrates linguistic diversity as a strength, not a limitation.

The future of AI in India depends on tools like IndQA — because true intelligence speaks every language, not just one.