TTS Language Coverage Compared: Five Published Counts and What They Hide

The short answer

If the decision is driven by a published number, the order is Google Cloud TTS at 75+ languages and variants, ElevenLabs at 74 languages, Speechify at 60+ languages, Amazon Polly at 40+ languages and variants, and Murf at 35+ languages. The gap between the first two is one language, which is not a meaningful difference. The more important finding is that five of the ten tools in this site’s 2026 run publish no language total at all, so a buyer cannot rank them on this field.

The published counts

Every number below is quoted from the vendor’s own page, with the wording the vendor uses. A plus sign is the vendor’s, not an estimate from this desk.

What a count actually measures

A language count is a catalogue statement. It usually means the vendor has at least one voice that can speak the language, not that every voice can, and not that the result is equally good across languages. Google’s product page says 380+ voices across 75+ languages and variants; that ratio alone tells you the catalogue is unevenly distributed, as every catalogue is (Google).

The wording matters. “Languages and variants” folds regional forms into the total. English alone can appear as US, UK, Australian, Indian, and other variants. A vendor that counts variants will look larger than a vendor that counts languages, even if their actual coverage is similar. This is why the table records the wording instead of normalising it; normalising would require a definition the vendors do not share.

ElevenLabs’ 74 is tied to a specific model row on its pricing page, the Eleven v3 row (ElevenLabs pricing). That is a more precise claim than a site-wide “supports 70+ languages” banner, because it tells you which model was measured. If you switch to a different model, the count may not apply.

What a count hides

Four things matter more than the total for most projects. The first is voice depth per language: a language with one voice gives you no gender, age, or accent choice. The second is script handling: names, numbers, currency, dates, and abbreviations are where a synthesiser sounds foreign even when the language is supported. The third is code-switching: a sentence that mixes two languages is common in product names and place names, and not every model handles it.

The fourth is the difference between a language and a locale. Portuguese in Brazil and Portugal, Spanish in Mexico and Spain, and French in France and Canada are not interchangeable for a local audience. A vendor can support the language and still miss the locale. The count will not tell you which.

A five-step audit for your locale

1. Write a short script in the target language with a local name, a phone number, a price with the local currency, a date, and one brand name that is usually left in English.
2. Generate it with the default voice and with one alternative voice, using default settings.
3. Have a native speaker listen without seeing the vendor name and mark pronunciation errors, unnatural stress, and wrong numbers.
4. Repeat with a second script that mixes the target language with an English product name.
5. Record the model name, voice name, and date, because vendors update models and a voice that passed last quarter may change.

The audit takes about an hour per vendor and produces a result you can defend. A catalogue count takes a minute and produces a number that may not apply to your script.

Who should care about which count

Global products should start with Google or ElevenLabs, because both publish a large count and a model-level basis. European or Asian single-market products may be better served by a smaller catalogue with deeper voice choice in that language; the count will not tell you, so run the audit. English-only products can ignore the field almost entirely; WellSaid’s English-first plans and Speechify’s reading product are both reasonable, and neither needs a 75-language catalogue.

Teams that need a published number for procurement should note that five vendors in this run publish none. That is not a claim that they support fewer languages; it is a claim that the field is unavailable from the page a buyer can cite. If your procurement process requires a citable count, those five will fail the requirement before the audio is ever tested.

The takeaway

The language count is a useful first filter and a poor final one. Google’s 75+ and ElevenLabs’ 74 are close enough to be a tie for practical purposes; Speechify, Polly, and Murf sit in a lower band; the other five are unranked on this field. Use the count to build a shortlist, then run the five-step audit on the one language that decides the project.