Bonus: Top up now and we'll double your first deposit — get x2 credits instantly.
Get Started
Sign Up
Sign Up

Deepgram Nova 2 Conversational AI

Up to 30%

Transcribe speech with Deepgram Nova 2 Conversational AI on LLM.API—batch or streaming audio to text with the same developer surface as your other models.

Start Using API

What is Deepgram Nova 2 Conversational AI?

Deepgram Nova 2 Conversational AI is a speech-to-text model on LLM.API (`nova-2-conversationalai`). Speech-to-text model by Deepgram. Feed audio and receive text transcripts for captions, agents, and searchable archives.

Providers

LLM.API routes Deepgram Nova 2 Conversational AI to the providers below, with discounted effective rates versus list price.

ProviderPricingContextCapabilities
deepgram30% offin $0.0043; out — per minute of audio——

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test Deepgram Nova 2 Conversational AI right here — free to start.

Deepgram Nova 2 Conversational AI
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Deepgram Nova 2 Conversational AI through the OpenAI-compatible API — STT via LLM.API (see docs for audio endpoints).

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="nova-2-conversationalai",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)
{
  "model": "nova-2-conversationalai",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Speech transcription

    Converts spoken audio into text for captions, notes, and search. Reflects speech-to-text positioning for this endpoint.

  • Voice UX input

    Powers voice-driven product interfaces and IVR handoffs. Grounded in the model's stt role rather than generic chat claims.

  • Multilingual audio

    Handles diverse accents and languages depending on the model.

  • Meeting capture

    Fits voice notes, calls, and meeting recording pipelines. Reflects speech-to-text positioning for this endpoint.

  • Downstream LLM prep

    Feeds transcripts into summarization and action-item extractors. Grounded in the model's stt role rather than generic chat claims.

6 Most Valuable Use Cases

  • Live captioning prototypes with Deepgram Nova 2 Conversational AI
  • Call-center transcription and QA
  • Podcast and video caption drafts with Deepgram Nova 2 Conversational AI
  • Clinical or field note dictation workflows
  • Compliance recording text archives with Deepgram Nova 2 Conversational AI
  • Voice command capture in mobile apps

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Deepgram Nova 2 Conversational AI on LLM.API?

  • Unified AI Routing

    Reach Deepgram Nova 2 Conversational AI and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Practical: Compare provider price points and keep spend visible as you scale Deepgram Nova 2 Conversational AI.

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for Deepgram Nova 2 Conversational AI alongside the rest of your stack.

  • Drop-in SDKs

    Practical: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap Deepgram Nova 2 Conversational AI for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You will pipe transcripts into search or LLM summarization (Deepgram Nova 2 Conversational AI)
  • Voice is a primary input modality in your product (Deepgram Nova 2 Conversational AI)
  • You need speech-to-text for batch or streaming audio (Deepgram Nova 2 Conversational AI)

Avoid if...

  • Your audio cannot leave your compliance boundary and you lack an approved provider path
  • You need text-to-speech or chat generation instead of transcription
  • You require guaranteed perfect transcripts for every accent without evaluation

COMMUNITY

What developers say about speech-to-text models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Deepgram Nova 2 Conversational AI.

  • Head-to-head tests of the leading APIs land within a point or two of each other; the choice usually comes down to language coverage, streaming latency and batch pricing.
  • Voice-agent developers report streaming latency and endpointing quality matter more in production than headline word-error rates.
  • Whisper-family models remain the default open baseline, with hosted providers winning on real-time features and diarization.

SOURCES

Frequently Asked Questions

  • Where is the canonical page for Deepgram Nova 2 Conversational AI?

    https://llmapi.ai/models/amazon-nova-2-conversationalai/

  • How is Deepgram Nova 2 Conversational AI priced on LLM.API?

    Listed pricing metadata shows: In $4.3 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • Is Deepgram Nova 2 Conversational AI a chat model?

    No—Deepgram Nova 2 Conversational AI is categorized as a stt model. Use the matching API surface rather than assuming chat completions.

  • Does Deepgram Nova 2 Conversational AI support streaming?

    Streaming depends on the active provider; check the providers table on this page for flags.

  • How do I call Deepgram Nova 2 Conversational AI via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "nova-2-conversationalai" and your LLM.API key. See the code snippet on this page.

  • When should I choose Deepgram Nova 2 Conversational AI?

    You need speech-to-text for batch or streaming audio — especially when you specifically need Deepgram Nova 2 Conversational AI.

  • Does Deepgram Nova 2 Conversational AI translate speech?

    Primary behavior is transcription to text. Translation—if needed—should be a separate step with a chat model unless the provider explicitly offers it.

  • What is Deepgram Nova 2 Conversational AI?

    Speech-to-text model by Deepgram. On LLM.API it is addressed as `nova-2-conversationalai`.

  • Can I use tools or structured outputs with Deepgram Nova 2 Conversational AI?

    Tooling support varies; for pure stt models, prefer the modalities listed rather than assuming chat tools.

  • What are limitations of Deepgram Nova 2 Conversational AI?

    Like other API models, Deepgram Nova 2 Conversational AI can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

COMPARE

Competitive Models

Get one key to every model

Swap your API key. Keep your code.

Start for free Talk to sales