ElevenLabs: Eleven v3 Conversational

Eleven v3 Conversational is a text-to-speech model from ElevenLabs. It is a variant of Eleven v3 that the provider has tuned specifically for natural dialogue in conversational agents, rather than for one-off narration. If you are building a voice agent, an interactive assistant, or any product where the spoken output has to sound like one side of a back-and-forth exchange, this is the model ElevenLabs positions for that job.

The model takes text as its only input and returns speech as its only output. It is a speech model, so it does not generate text, reason over documents, or call tools; it turns the text you send it into audio. ElevenLabs states that it supports more than 70 languages and that a request is limited to 5,000 characters. The model exposes a fixed set of 21 voices: george, sarah, adam, alice, bella, bill, brian, callum, charlie, chris, daniel, eric, harry, jessica, laura, liam, lily, matilda, river, roger, and will. No context length in tokens is published for this model, and no request parameters are listed as supported.

The practical uses follow directly from that shape. Conversational agents and voice assistants are the obvious fit, since the model is tuned for dialogue rather than long-form reading. The same properties suit interactive characters in games or apps, spoken responses in customer support flows, and any interface where a user types or speaks and expects a spoken reply. The 70+ language support makes it usable for multilingual products, and the 5,000 character request limit is generous for the short turns that conversation typically involves. Because the output is speech only, you would pair it with a separate text model if you also need the agent to generate or reason about the reply text.

You call it through api14's OpenAI-compatible API using the model id elevenlabs/eleven-v3-conversational. Because the endpoint follows the OpenAI format, you can point an existing OpenAI client at api14 and send a request the same way you would for any other model, selecting this id and providing the text to be spoken plus the voice you want. The model lists no supported request parameters, so treat the request as text in and audio out, with the voice selection drawn from the 21 available voices, and avoid assuming tuning knobs such as temperature or top_p will have an effect.

The main limits to keep in mind are the ones stated above: text in, speech out, a 5,000 character cap per request, and a fixed voice list. If your task is transcription, translation of audio, or anything that needs a text response, this model is not the right tool and a text or audio-understanding model would fit better. Within its lane, though, it is a straightforward choice for developers who want conversational speech without managing the underlying voice stack themselves.