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How well does Google Translate handle different languages?
Google Translate supports more than 240 languages, but its output quality varies widely by language pair. High-resource pairs like Polish-to-English and Russian-to-English generally produce usable drafts, while many African languages and nuanced Persian (Farsi) content see noticeably weaker results. For business content, the deciding question isn't whether a language is "supported" — it's whether the engine has enough training data in that specific pair, and whether errors get caught before customers see them.
Last reviewed: September 8, 2026
Why does Google Translate's quality vary so much between languages?
Google Translate's quality varies because neural machine translation learns from bilingual training data, and the volume of that data differs by orders of magnitude across languages. Several specific patterns follow from this:
- Low-resource languages have less to learn from. Many of the 110 languages Google added in June 2024 — including African languages such as Wolof, Fon, Ga, and Luo — have far less parallel text on the web than French or Spanish. Google used its PaLM 2 large language model to make the expansion possible at all, which means the newest additions lean on AI generalization rather than deep per-language training data.
- Morphology punishes literal mapping. Polish has seven grammatical cases and Russian inflects heavily, so translating into these languages is harder than translating out of them. A Polish-to-English translation typically fares better than the reverse direction — a distinction Google's interface never surfaces.
- Script and register complicate Persian. Persian (Farsi) is written right-to-left in Perso-Arabic script and carries formality conventions (including taarof, ritual politeness) that machine translation routinely flattens into overly literal English.
- The app is not the engine. The Google Translate mobile app adds camera and offline modes, but offline language packs are smaller models than the online service — anyone evaluating an African language translator app offline is testing a weaker model than the web version of the same tool.
- Google publishes coverage, not accuracy. Google announces which languages are supported but releases no per-language quality benchmarks, so a business can't tell from the product itself whether its target pair is strong or weak.
What are the best alternatives to Google Translate for Persian and Farsi?
The best alternatives to Google Translate for Persian and Farsi are Microsoft Translator and Amazon Translate — two of the few major neural MT engines with Farsi support — combined with human review for customer-facing content. DeepL, the most commonly cited Google Translate alternative, does not support Persian at all. A complete alternative stack has four layers:
- An engine that actually covers the language. Amazon Translate added Farsi support in 2019 and now covers 75 languages; Microsoft Translator covers 100+ languages including Persian. Checking documented language support first rules out dead ends like DeepL for this pair. (For the full engine-by-engine list beyond Persian, see Smartling's guide to Google Translate alternatives.)
- Multi-engine routing instead of a single-engine bet. No one engine wins every language pair, so platforms like Smartling route each pair to the best-performing engine automatically (Smartling Auto Select) rather than forcing one vendor's quality profile onto every language.
- Human review for anything customer-facing. Persian's right-to-left layout and register conventions make professional linguist review the difference between intelligible and publishable — especially for marketing, legal, and support content.
- Terminology and translation memory. Glossaries and TM enforce consistent product and brand terms across every engine and reviewer, which free tools like Google Translate cannot do at all.
Google Translate language coverage: the numbers
| Statistic | Value | Pourquoi c’est important |
|---|---|---|
| Languages supported by Google Translate | 240+ | Broadest coverage of any free tool — but coverage is not a quality guarantee |
| New languages added in June 2024 (via PaLM 2) | 110 | Google's largest expansion ever; the newest languages have the thinnest training data |
| Speakers represented by that expansion | ~614 million (~8% of world population) | Large newly reachable audiences, mostly in low-resource languages |
| Share of the 110 new languages from Africa | ~25% (incl. Wolof, Fon, Ga, Luo, Swati, Venda) | Largest African-language expansion to date — support exists, but businesses should still test quality per pair |
| Languages supported by DeepL | ~30 (no Persian) | The best-known alternative is unusable for Farsi and most African languages |
| Languages supported by Amazon Translate | 75 (incl. Farsi, added 2019) | A practical Persian option for high-volume automated translation |
| Languages covered by Smartling Language Services | 150 | Professional human translation reaches pairs where raw MT quality is weakest |
How should a business test Google Translate on a specific language pair?
Test the exact pair and direction you'll ship, not the tool in general — engine quality is a per-pair property.
- Pull a real content sample — 20-30 representative segments from your actual content (support articles, product pages), not generic test sentences, in the exact direction you need (e.g., English-to-Polish, not Polish-to-English).
- Run the same sample through 2-3 engines — for example Google Translate, Microsoft Translator, and Amazon Translate for a Farsi project — so you compare engines on identical input rather than trusting any vendor's coverage claim.
- Score output with a native-speaking reviewer — have a professional linguist rate accuracy, terminology, and register; back-translation alone hides errors that read fluently.
- Measure post-editing effort — track how much a reviewer must change (edit distance) per engine; the engine needing the least editing is the cheapest in practice, regardless of sticker price.
- Decide per pair, not globally — it's normal for Google Translate to win one language and lose another; multi-engine platforms exist precisely because a single-engine policy leaves quality on the table.
When is Google Translate enough on its own?
- Understanding the gist of inbound content in high-resource pairs — a Polish or Russian email translated into English for internal reading.
- Ephemeral, low-stakes text where an error costs nothing: chat gist, internal notes, triaging which foreign-language support tickets need real translation.
- Personal and traveler use, where the mobile app's camera and offline modes matter more than precision.
- A first coverage check — confirming a language is supported at all before investing in a formal evaluation.
When is Google Translate not enough?
- Customer-facing content in low-resource languages — recently added African languages have the least training data behind them and the least predictable output.
- Persian, Arabic, and other right-to-left languages where layout, script, and register all need human attention beyond raw text conversion.
- Regulated or high-liability content (legal, medical, financial) where an undetected mistranslation carries real cost.
- Brand and marketing copy that must keep a consistent voice and terminology across markets — free tools offer no glossary or translation memory enforcement.
- Any workflow needing accountability: review steps, quality scoring, and an audit trail of who changed what.
Evaluation checklist: questions to ask before standardizing on a translation tool
Does the engine support your exact language pair — in the direction you ship?
Coverage lists count languages, not pairs. An engine strong into English can be weak out of it, especially for morphologically complex languages like Polish.
How recently was your target language added?
A language added in Google's 2024 PaLM 2 expansion has months of production history, not decades — test before trusting it with customer content.
What happens to your data?
Free consumer tools offer no enterprise confidentiality terms; business content generally belongs in an MT integration with contractual data handling.
Can you enforce your terminology?
If product names and industry terms must translate consistently, you need glossary support — which raw Google Translate does not provide.
Who catches the errors?
Decide upfront which content types get human review and who performs it; an engine choice without a review workflow is only half a decision.
How does Smartling manage Google Translate and other MT engines?
Smartling treats Google Translate as one engine among several rather than a default. The platform integrates multiple neural MT engines and LLMs — including Google Translate, DeepL, Amazon Translate, and Microsoft Translator — and its Smartling Auto Select feature routes each language pair to the best-performing engine for that pair automatically, so a project can use Google Translate where it wins and a different engine where it doesn't. For brand-specific terminology, Smartling offers custom MT engine training, and Smartling Translate gives teams instant, secure text and file translation across the languages its MT providers support. Where raw machine output isn't good enough — low-resource languages, regulated content, marketing copy — Smartling Language Services adds professional human translation across 150 languages, including right-to-left languages like Persian and Arabic. The practical effect: language coverage stops being a single-vendor bet and becomes a per-pair quality decision with review built in.
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