India’s linguistic diversity key in US-China AI race

The battle between American and Chinese AI models for India’s enterprise market may ultimately be decided not by benchmark scores or token costs, but by how well they understand India’s linguistic diversity. As Chinese models rapidly close the gap with their US rivals in reasoning and coding, both camps will need to prove they can handle code-switching, transliteration and domain-specific Indian languages to win enterprise adoption.

Chinese models excel in low-cost inference and are also becoming increasingly capable at coding. For instance, Z.ai’s GLM 5.2 outperforms most state-of-the-art models smaller than Opus 4.7, meaning it can handle around 90 per cent of the tasks people typically use AI for.

“Chinese models have reached roughly 90-95 per cent parity with their American counterparts in reasoning, coding, and multilingual capabilities. While they still have some ground to cover in areas like safety and enterprise readiness, their total cost of ownership is about one-tenth that of competing US models. As a result, they are already well positioned to deliver disproportionate value to enterprises that choose to adopt them,” said Praveer Kochhar, CPO & Co-founder of KOGO AI.

Narrow gap
According to Greyhound Research, DeepSeek’s V4 Flash sits at $0.14 and $0.28 per million tokens, V4 Pro at $1.74 and $3.48 with a promotional rate of $0.435 and $0.87 currently posted, and OpenAI’s newest flagship tier, GPT-5.6 Sol, at $5 and $30.

Meanwhile, Jaspreet Bindra, Co-founder & CEO, AI & Beyond, highlighted that Chinese models now match frontier American systems across reasoning, mathematics, coding and multilingual benchmarks. Stanford’s 2026 AI Index estimates that the performance gap between the leading US and Chinese models has narrowed to just 2.7 percentage points.

However, American models retain advantages in complex agentic workflows, reliability, advanced research, safety tooling, enterprise integrations, and the surrounding cloud, developer, and application ecosystems.

“While China can challenge the United States at the model level, particularly through efficient open-source innovation, America retains advantages in advanced chips, cloud infrastructure, capital, global developer ecosystems, and the number of frontier models. The competition will remain closely contested,” Bindra said.

China’s progress has come from constraint meeting competence. Companies have reduced computing costs through sparse mixture-of-experts models, lower-precision computing and more efficient attention mechanisms, while using synthetic data, model distillation and smarter inference to make better use of limited data and computing power. They are also increasingly designing AI models to work efficiently with domestically-developed chips.

No monopoly
“The model layer is becoming less scarce, more contestable and easier to substitute, which erodes the pricing power of premium providers and gives CIOs a credible basis for multi-model architecture, even as dependence on cloud capacity, accelerators and integration ecosystems remains. Intelligence is becoming portable faster than the systems needed to operate it safely. China has not caught the entire AI stack, but has made the model layer far harder for anyone to monopolise,” Sanchit Vir Gogia, Chief Analyst at Greyhound Research, explained.

However, he observed, multilingual capability is routinely oversold. Qwen’s third-generation family was trained across 119 languages and dialects, including Hindi, Tamil, and Bengali, and the leading US models carry broad global coverage with larger international product and support ecosystems. But neither origin guarantees dependable Indian-language performance. Currently, the strongest published score on Indian financial and regulatory language belongs to a US flagship rather than any Chinese contender.

“A model that converses in Hindi does not necessarily reason, retrieve, or comply in Hindi. Indian buyers should test transliteration, code-switching and domain terminology directly, compare tokenisation efficiency because inefficient tokenisation inflates both latency and cost, and re-test safety behaviour in every required language, since guardrails built in English weaken when the script changes,” Gogia noted.

American bias
Greyhound Research finds that Indian enterprises are actively evaluating Chinese models while committing to almost none in production. Evidence shows a pronounced skew towards US-managed platforms in named enterprise commitments, sitting alongside vigorous developer and start-up interest in Chinese open weights.

US AI models enjoy a distribution advantage in India, with enterprises already relying on American hyperscalers for cloud infrastructure, security and procurement. Partnerships between AI providers and IT majors such as TCS and Infosys have further cemented their position.

“Language will become a decisive selection factor rather than a marketing line. India’s language market will punish anyone who confuses vocabulary with understanding,” Gogia shared.

Meanwhile, India has a third strategic option. Homegrown models like Sarvam-105B, trained across 22 Indian languages, combined with the IndiaAI compute build-out, offer greater data residency and sovereign AI capabilities. However, these models alone are not enough—India must also build the infrastructure and operational ecosystem to deploy them at scale.

Indian control
Greyhound Research expects Indian enterprises to initially use Chinese AI models primarily for evaluations, developer tools, and private pilots, before adoption gradually expands through Indian-hosted deployments and approved marketplaces. Wider use in regulated sectors is likely only as local hosting, governance, and enterprise support mature. Ultimately, while Indian enterprises will increasingly use foreign AI models, they will insist on Indian control through local infrastructure, compliance and deployment.

Ritwik Batabyal, Chief Technology and Innovation Officer, Mastek, echoed this, adding that for enterprises, factors such as regulatory compliance, data sovereignty requirements, deployment flexibility, cost, model performance, security standards, and ecosystem compatibility are often more important than the country of origin.

“Some organisations may prefer open-source Chinese models for on-premise deployments, customisation requirements, or cost optimisation. Others may choose US providers because of stronger governance frameworks, enterprise support capabilities, and established cloud partnerships. Increasingly, we are seeing enterprises adopt hybrid AI strategies where multiple models coexist within the same architecture, with workloads routed dynamically to the model best suited for a particular task,” he said. The Hindu BusinessLine