deepgram
Deepgram: Aura-2
deepgram/aura-2
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
According to the OpenRouter catalog, deepgram/aura-2 is Deepgram’s multilingual text-to-speech model. The catalog describes it as accepting text, producing speech, and supporting Deepgram’s canonical Aura-2 voice catalog across multiple languages. These are source-reported capabilities rather than independently measured performance. No benchmark entries, knowledge cutoff, Hugging Face identifier, configurable parameters, or independent model card were included in this source pack, so suitability for a particular voice, language, latency, or production workload should be validated directly.[1]
Strengths
Limitations
- The evidence is catalog-only; an independent model card was not available in this source pack, and no measured performance results were supplied.[1]
- The source does not enumerate supported languages or voices, nor does it provide speech quality, latency, reliability, or workload-specific validation.[1]
Where to use it
Sources
- [1] OpenRouter catalog · Deepgram: Aura-2Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Aura-2 is a multilingual text-to-speech model from Deepgram. It supports Deepgram’s canonical Aura-2 voice catalog for speech synthesis across multiple languages.
Specifications
- Model ID
- deepgram/aura-2
- Canonical ID
- deepgram/aura-2-20260716
- Creator
- deepgram
- Added to OpenRouter
- 16.07.2026
- Context length
- 0
- Modality
- text->speech
- Input modalities
- text
- Output modalities
- speech
- Token pricing
- No per-token price in the source snapshot — see OpenRouter for the billing unit
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | — |
| Coding | No measurement | — |
| Agents | No measurement | — |
| Writing | No measurement | — |
| Reasoning | No measurement | — |
| Document analysis | No measurement | — |
| Vision | No measurement | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
No evaluations are supplied for this model.
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| Deepgram | 0 | — | — | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.