ElevenLabs: Eleven Flash v2.5 vs Mistral: Voxtral Mini TTS
A side-by-side comparison of ElevenLabs: Eleven Flash v2.5 ($140.00 / 1M characters) and Mistral: Voxtral Mini TTS ($56.00 / 1M characters) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| ElevenLabs: Eleven Flash v2.5 | Mistral: Voxtral Mini TTS | |
|---|---|---|
| Provider | ElevenLabs | Mistral |
| Characters | $140.00 / 1M characters | $56.00 / 1M characters |
| Context window | - | 4,096 |
| Max output | - | 3,276 |
| Input | text | text |
| Output | speech | speech |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Oct 7, 2026 | Apr 19, 2026 |
Key differences
- Mistral: Voxtral Mini TTS is 60% cheaper on input tokens.
In depth
ElevenLabs: Eleven Flash v2.5 is a text to speech model from ElevenLabs, and Mistral: Voxtral Mini TTS is a text to speech model from Mistral. Both sit in the speech category, so neither is a general purpose chat or reasoning model. You give either one text, and you get audio back. Eleven Flash v2.5 carries the provider release date of 2026-10-07, while Voxtral Mini TTS carries 2026-04-19, so the Mistral entry has been around longer on api14. If your application already leans on ElevenLabs voices or on Mistral tooling elsewhere, that existing familiarity is a reasonable tiebreaker, but the models themselves are directly comparable in what they do.
The two models are close in shape. Both accept text as their only input modality and both return speech as their only output modality, so there is no image, audio input or video path on either side. Neither exposes any supported parameters, which means you should not expect to tune sampling, voice or format knobs through the request body in the way you might with a larger generation model. The one concrete difference in the listed facts is the context window: Voxtral Mini TTS lists 4096 tokens, while Eleven Flash v2.5 lists no context length at all. That does not automatically make the Mistral model the bigger context model, since the field is unknown for Eleven Flash rather than small, but it does mean 4096 is the only documented ceiling you can plan against. On price, the qualitative data marks the Mistral model as the cheaper input side, with output cost about the same between them.
For practical selection, start with the length and shape of the text you need spoken. If you are synthesizing short utterances, single sentences, UI prompts, notification reads or conversational turns, either model fits and the cheaper input side on Voxtral Mini TTS is a mild advantage. If you are feeding longer passages and you want a documented token ceiling to validate against before you send the request, the 4096 token context on Voxtral Mini TTS gives you something concrete to check, whereas Eleven Flash v2.5 gives you no published number to reason about. If your product is built around ElevenLabs voice character or you are already routing other ElevenLabs calls through the same gateway, staying with Eleven Flash v2.5 keeps your stack consistent, and the output pricing is about the same either way, so the decision is unlikely to hinge on cost alone.
Calling either model through api14 works the same way, because the gateway exposes an OpenAI compatible API. You point your client at the api14 base URL, send your text in the standard chat or speech request shape, and set the model field to the id you want. For ElevenLabs, that id is elevenlabs/eleven-flash-v2.5. For Mistral, it is mistralai/voxtral-mini-tts-2603. Nothing else in the request has to change to switch between them, so you can keep one code path and swap the model string, or run both side by side behind a flag and compare the audio your users actually hear. Because neither model lists supported parameters, keep your request body minimal and avoid assuming extra tuning fields will be honored.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "elevenlabs/eleven-flash-v2.5" to "mistralai/voxtral-mini-tts-2603" and prompts, streaming and tools keep working.
import { writeFile } from "node:fs/promises";
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api14.com/api/v1",
apiKey: process.env.API14_API_KEY,
});
const audio = await client.audio.speech.create({
"model": "mistralai/voxtral-mini-tts-2603",
"input": "Hello from api14! One API for every model.",
"voice": "alloy",
"response_format": "mp3"
});
await writeFile("speech.mp3", Buffer.from(await audio.arrayBuffer()));
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