ElevenLabs: Eleven Flash v2.5 vs Qwen: Qwen-Audio-3.0-TTS Plus
A side-by-side comparison of ElevenLabs: Eleven Flash v2.5 ($140.00 / 1M characters) and Qwen: Qwen-Audio-3.0-TTS Plus ($70.00 / 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 | Qwen: Qwen-Audio-3.0-TTS Plus | |
|---|---|---|
| Provider | ElevenLabs | Qwen |
| Characters | $140.00 / 1M characters | $70.00 / 1M characters |
| Context window | - | - |
| Max output | - | - |
| Input | text | text |
| Output | speech | speech |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Oct 7, 2026 | Jul 23, 2026 |
Key differences
- Qwen: Qwen-Audio-3.0-TTS Plus is 50% cheaper on input tokens.
In depth
ElevenLabs: Eleven Flash v2.5 is a text to speech model from ElevenLabs, listed under the speech category on api14. Qwen: Qwen-Audio-3.0-TTS Plus is a text to speech model from Qwen, also listed under the speech category. Both are served through the same gateway, so from a developer's point of view they sit side by side in the same catalogue and are addressed the same way. Neither listing records a provider release date difference that matters for integration, and both are positioned as production speech synthesis endpoints rather than general purpose chat models.
On the facts available, the two models look very similar in shape. Both take text as input and return speech as output, and neither exposes a context length in the listing, so there is no documented context window to compare. Neither model advertises tool calling, structured output or reasoning as a distinguishing capability, and the comparison data marks those fields as not meaningfully different. In other words, there is no capability axis here where one model clearly beats the other. The one qualitative difference given is on input price: Qwen: Qwen-Audio-3.0-TTS Plus is the cheaper of the two on input. Output pricing is marked as about the same, so the cost gap, such as it is, sits on the input side only.
For picking between them, the practical split is narrow. If your workload is dominated by input volume, for example long scripts, batch narration or large volumes of text pushed through synthesis, the Qwen model is the cheaper input option and is a reasonable default on that basis alone. If your workload is output heavy, or if you simply prefer the ElevenLabs voice character and delivery, Eleven Flash v2.5 is a fine choice, since output cost is about the same and there is no capability penalty either way. Because neither model exposes tools, structured output or reasoning, you should not choose between them on those grounds. Choose on voice quality preference and on which side of the input and output cost split your traffic falls.
Calling either model through api14 uses the same OpenAI compatible interface. You send a request to the speech endpoint with your text, and the only thing that changes between the two is the model id string. Use elevenlabs/eleven-flash-v2.5 to route to ElevenLabs, or qwen/qwen-audio-3.0-tts-plus to route to Qwen. Your authentication, request shape and response handling stay identical, so switching is a one line change and you can A/B the two voices in the same code path without rewriting anything.
There is one case where the more expensive input model is still the better pick. If you have already tuned your pipeline around ElevenLabs output, or your product depends on a specific ElevenLabs voice, the input price difference is unlikely to justify a migration, especially since output pricing is about the same. In that situation, keep elevenlabs/eleven-flash-v2.5 and treat the Qwen model as the option to reach for when input volume grows enough that the cheaper input rate starts to matter.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "elevenlabs/eleven-flash-v2.5" to "qwen/qwen-audio-3.0-tts-plus" 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": "qwen/qwen-audio-3.0-tts-plus",
"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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