Evidence-first authority page
How AI Agents Handle Hinglish and Code-Switching
What code-switching between Hindi (or another Indic language) and English actually requires of a model, and where it typically still fails.
Direct answer
Handling Hinglish requires a model whose tokenizer and training data actually included code-switched text, not just separate Hindi and English text — a model can score reasonably on Hindi-only and English-only benchmarks while still struggling on a single sentence that mixes both, since that's a distinct pattern from either language alone.
Last reviewed 2026-09-22
Reviewed by Febin Francis, Founder/Editor on 2026-09-22 under the methodology-only publication path: this page makes no externally-verifiable factual or comparative claim, so it is reviewed for originality and disclosed limitations rather than sourced against a primary receipt.
Why code-switching is a distinct problem from bilingual support
"Supports Hindi" and "supports Hinglish" are different claims. Code-switching within a sentence — not alternating between separate Hindi and English messages — requires training data that actually contains that pattern, which is rarer than monolingual data in either language. A model evaluated only on separate Hindi and English test sets gives no evidence about code-switched performance either way.
Romanized Hindi (Hindi written in Latin script, as is common in chat and SMS) is a further variant again — it isn't Hindi script and isn't English, and a model's handling of it has to be checked as its own case rather than assumed from either language's score.
How to actually check this, rather than take a claim at face value
- Test with real code-switched sentences from your own domain, not translated English test cases.
- Include romanized (Latin-script) Hindi or other Indic-language input if that's what your users actually type.
- Check the model card, if one exists, for whether code-switched data was part of training — see model card (glossary) for what that document typically discloses.