Urdu Large Language Models in 2026: How Well Does AI Speak Urdu?

In 2026, AI speaks Urdu usefully but unevenly. Frontier models can read and write Urdu and Roman Urdu well enough for many everyday tasks, but research shows they still trail their English performance, especially on multi-step reasoning. A wave of Pakistan-led Urdu LLMs, including Qalb, Alif and UrduLLaMA, report clear gains over their base models on Urdu tasks, and real gaps in datasets, evaluation and speech leave room for builders.

Key takeaways

• Urdu has about 246 million total speakers (Ethnologue estimate cited by Wikipedia), yet it is still treated as a "low-resource" language in AI research.
• General-purpose models handle Urdu, but specialised and fine-tuned models often beat them on specific Urdu tasks.
• Notable local efforts: Qalb (January 2026), Alif 1.0 from Traversaal.ai, and UrduLLaMA 1.1 from the Centre for Language Engineering at UET Lahore.
• Roman Urdu is a challenge of its own: no standard spelling and lots of code-switching with English.
• The biggest opportunities for builders are datasets, evaluation, speech and real products for Urdu-first users.

Why Urdu matters for AI

Urdu is Pakistan's national language and a widely spoken language in South Asia and the diaspora. Wikipedia, citing Ethnologue's 2026 estimates, lists about 78 million first-language speakers and 168 million second-language speakers, roughly 246 million in total, making Urdu one of the ten most widely spoken languages in the world.

Despite that, AI research still often classifies Urdu as low-resource, meaning there is far less high-quality digital text, labelled data and evaluation material than for English. That gap shows up directly in how well models perform.

How frontier models handle Urdu

What works

Modern frontier models from major labs can:

• translate between Urdu and English;
• summarise and draft text in Urdu script;
• understand and reply in Roman Urdu;
• follow instructions given in a mix of Urdu and English.

For many practical uses, such as customer support replies, document summaries or study help, the output is good enough to be useful with human review.

Where they struggle

The research picture is consistent:

• Specialists often beat generalists on Urdu tasks. A study presented in Findings of EMNLP 2024 compared general-purpose models (GPT-4-Turbo and Llama-3-8b) with fine-tuned specialist models across 14 Urdu NLP tasks. Specialists did better on 12 of the 14 tasks by automatic metrics, although human reviewers preferred GPT-4-Turbo's outputs on generation tasks.
• Reasoning is harder in Urdu. The UrduBench team, which adapted reasoning benchmarks such as MGSM and CommonSenseQA into Urdu with human review, found that multi-step and symbolic reasoning tasks are significantly harder in Urdu.
• Performance drops outside English in general. The International AI Safety Report 2026 notes that current AI systems perform worse outside English-language contexts.
• Urdu often costs more tokens. Research presented at NeurIPS 2023 found that tokenizers can produce up to 15 times more tokens for the same text in some languages than others. More tokens per sentence means higher API cost and less room in the context window.

Roman Urdu: the everyday AI challenge

Many Pakistanis type Urdu in Latin letters on WhatsApp, social media and search. Researchers behind the ERUPD English to Roman Urdu dataset describe it as having "extensive phonetic variability" and a "lack of standardized spelling conventions," with frequent code-switching into English.

For a model, "kya", "kia" and "kiya" might all be the same word. For a builder, this means:

• test with real messages your users send, not textbook sentences;
• consider rewriting Roman Urdu queries into Urdu script or English before search (see SI-05);
• reply in the same form the user wrote in, unless they ask otherwise.

Known Urdu LLM efforts

Several Pakistan-linked teams have released Urdu-focused models. The figures below are as reported by the teams themselves.

Some media reports called Qalb "the world's largest" Urdu LLM. That is the team's own description. Self-reported benchmark scores are a useful starting point, but independent testing on your own tasks is what counts.

There are also community resources, such as Traversaal.ai's open list of Urdu datasets, models and tools on GitHub, and new benchmarks such as UrduBench.

Gaps in Urdu AI

Datasets

High-quality, openly licensed Urdu text and instruction data is still limited. Most Urdu web text is news, religious or social media content, which skews what models learn.

Evaluation

Until recently there were few standard Urdu benchmarks. Many existing ones are translations of English tests, which can carry translation errors and cultural mismatches. The EMNLP 2024 study also found that using an LLM to grade Urdu outputs aligned less well with humans when the judge model was weaker, which means automated grading in Urdu needs human spot checks.

Speech

Text is only part of the picture. Many users prefer voice notes over typing, and speech recognition and speech synthesis for Urdu, Roman Urdu-style code-switching, and regional accents are areas to test carefully before building on them.

Regional languages

Punjabi, Sindhi, Pashto, Saraiki, Balochi and others are spoken by millions in Pakistan. Support for them in AI tools is generally thinner than for Urdu. Treat any claim of strong support as something to verify.

Opportunities for builders

The gaps are also openings. Practical ideas for Pakistani developers and students:

1. Build Urdu evaluation sets for real tasks. A carefully checked set of 100 questions for a domain (banking FAQs, university admissions, agriculture advice) is valuable to anyone deploying Urdu AI.
2. Bilingual RAG products. Assistants that answer from official documents in Urdu, Roman Urdu and English (tutorial in SI-05).
3. Text normalisation tools. Libraries that clean Urdu Unicode and handle Roman Urdu spelling variants help every Urdu NLP project.
4. Domain fine-tunes. Adapting an open Urdu model like Alif or UrduLLaMA to a narrow domain, with proper evaluation.
5. Voice-first assistants. Carefully tested voice interfaces for users who prefer speaking to typing.
6. Localisation services. Helping global AI products work properly for Pakistani users, including tone, script and cultural context.

How to choose a model for an Urdu product

• List your tasks. Translation, summarisation, Q&A, classification, reasoning?
• Build a small test set in the forms your users write: Urdu script, Roman Urdu, English, mixed.
• Compare two or three options: a frontier API model, an open Urdu model, and possibly a fine-tuned one.
• Measure quality, cost and speed together. Token counts in Urdu may change the cost comparison.
• Review with fluent readers. Automated scores in Urdu need human checks.

At Digital Silk Route (DSR), we see Urdu-first tools as one of the clearest places where local builders can create products that global vendors overlook.

Next step with Digital Silk Route

Urdu AI is a space where Pakistani builders have a natural advantage. The Superintelligence track at the Digital Silk Route (DSR) AI & Crypto Academy runs from Beginner to Advanced, with capstones where you build working AI products, including bilingual Urdu-English tools. Finished products can be listed on the DSR Marketplace. Learn. Build. Earn., in the languages your users actually speak.

Related reading

• Build a RAG Chatbot That Answers in Urdu and English
• How to Test an AI Agent Before You Ship It (Evals for Beginners)
• 12 AI Products Pakistani Developers Can Build and Sell

Sources

• Wikipedia: Urdu (speaker estimates citing Ethnologue; script)
• Qalb paper (arXiv 2601.08141)
• Arab News: Pakistani student launches "Urdu ChatGPT" AI model
• Alif-1.0-8B-Instruct model card (Hugging Face)
• Traversaal.ai: Urdu LLM resources (GitHub)
• Tahir, Shams, Hussain and Butt: Instruction-Tuned Urdu LLMs (UrduLLaMA 1.1), LREC 2026 (PDF)
• Arif et al., Generalists vs. Specialists: Evaluating Large Language Models for Urdu (Findings of EMNLP 2024)
• UrduBench (arXiv 2601.21000)
• ERUPD: English to Roman Urdu Parallel Dataset (arXiv 2412.17562)
• Petrov et al., Language Model Tokenizers Introduce Unfairness Between Languages (NeurIPS 2023)
• International AI Safety Report 2026