Deepgram: Aura-2 vs ElevenLabs: Eleven Flash v2.5
A side-by-side comparison of Deepgram: Aura-2 ($105.00 / 1M characters) and ElevenLabs: Eleven Flash v2.5 ($140.00 / 1M characters) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| Deepgram: Aura-2 | ElevenLabs: Eleven Flash v2.5 | |
|---|---|---|
| Provider | Deepgram | ElevenLabs |
| Characters | $105.00 / 1M characters | $140.00 / 1M characters |
| Context window | - | - |
| Max output | - | - |
| Input | text | text |
| Output | speech | speech |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Jul 16, 2026 | Oct 7, 2026 |
Key differences
- Deepgram: Aura-2 is 25% cheaper on input tokens.
In depth
Deepgram: Aura-2 is a speech model from Deepgram, and ElevenLabs: Eleven Flash v2.5 is a speech model from ElevenLabs. Both sit in the speech category, and both take text as input and produce speech as output. Neither is a general text model, so the comparison here is between two text to speech engines rather than between two chat assistants. If you are building something that needs to turn written content into audio, these are the two candidates on api14 that fit that description.
On capabilities, the two are closer than they look. Both list text as the only input modality and speech as the only output modality, and neither exposes a context length in tokens, which is expected for a speech model where the relevant limit is usually the length of the text you send rather than a token window. Neither model advertises tool calling, structured output or reasoning as a distinguishing feature, so there is no capability gap to exploit there. The one qualitative difference given is on price: Aura-2 is the cheaper model on input. Output pricing is not flagged as different, so treat the two as about the same on that side. In short, the meaningful difference is cost on input, not features.
That makes the choice fairly practical. If your workload is dominated by input volume, for example long scripts, articles or documents that you convert to audio at scale, Aura-2 is the cheaper option on input and is the natural default. If you are already invested in ElevenLabs voices or have a reason to prefer its output character, Eleven Flash v2.5 is the pick, and the input price difference is the trade you accept for it. Because neither model offers tools, structured output or reasoning, you should not choose between them on those grounds; there is nothing there to separate them. Decide on voice preference and on how much input text you are pushing through.
Calling either model through api14 works the same way, because the API is OpenAI compatible. You send a request to the same endpoint with the same request shape, and the only thing that changes is the model id. Use "deepgram/aura-2" for Deepgram and "elevenlabs/eleven-flash-v2.5" for ElevenLabs. Your text goes in as the input, and the response comes back as speech. That means you can build one code path, keep the model id in configuration, and switch providers without rewriting the integration. It also makes A/B testing straightforward: run the same text through both ids and compare the audio.
There is a case where the other model is still the better pick even though Aura-2 is cheaper on input. Because output pricing is not flagged as different and no capability difference is listed, Eleven Flash v2.5 remains a reasonable choice whenever the deciding factor is the voice or the provider rather than the input cost. If your input volume is modest, the input price gap matters less, and the reason to pick ElevenLabs can stand on its own. Neither model is strictly better across the board; the facts given support a cost advantage for Aura-2 on input and nothing more.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "deepgram/aura-2" to "elevenlabs/eleven-flash-v2.5" 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": "elevenlabs/eleven-flash-v2.5",
"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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