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Deepgram Nova 3 Multilingual

Up to 30%

Deepgram Nova 3 Multilingual brings speech-to-text speech-to-text to LLM.API for voice notes, calls, and caption pipelines.

Start Using API

What is Deepgram Nova 3 Multilingual?

With Deepgram Nova 3 Multilingual, LLM.API turns audio into text for product voice features. Speech-to-text model by Deepgram. The model id `nova-3-multilingual` keeps STT alongside your other endpoints.

Providers

LLM.API routes Deepgram Nova 3 Multilingual to the providers below, with discounted effective rates versus list price.

ProviderPricingContextCapabilities
deepgram30% offin $0.0052; 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 3 Multilingual right here — free to start.

Deepgram Nova 3 Multilingual
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Deepgram Nova 3 Multilingual 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-3-multilingual",
    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-3-multilingual",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Caption generation

    Produces base transcripts for subtitle workflows. Reflects speech-to-text positioning for this endpoint.

  • Speech transcription

    Converts spoken audio into text for captions, notes, and search. Relevant for `nova-3-multilingual` workloads on LLM.API.

  • Voice UX input

    Powers voice-driven product interfaces and IVR handoffs. Tuned to how teams typically call Deepgram Nova 3 Multilingual.

  • Meeting capture

    Fits voice notes, calls, and meeting recording pipelines.

  • Downstream LLM prep

    Feeds transcripts into summarization and action-item extractors. Relevant for `nova-3-multilingual` workloads on LLM.API.

6 Most Valuable Use Cases

  • Meeting notes and searchable recordings with Deepgram Nova 3 Multilingual
  • Feeding voice input into LLM agents
  • Voice command capture in mobile apps with Deepgram Nova 3 Multilingual
  • Call-center transcription and QA
  • Compliance recording text archives with Deepgram Nova 3 Multilingual
  • Live captioning prototypes

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 3 Multilingual on LLM.API?

  • Unified AI Routing

    Practical: Reach Deepgram Nova 3 Multilingual and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale Deepgram Nova 3 Multilingual.

  • Reliability Layer

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

  • Observability

    Production: Trace prompts, tokens, and errors for Deepgram Nova 3 Multilingual alongside the rest of your stack.

  • Drop-in SDKs

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

  • Model Breadth

    Practical: Swap Deepgram Nova 3 Multilingual 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 3 Multilingual)
  • You need speech-to-text for batch or streaming audio (Deepgram Nova 3 Multilingual)
  • Voice is a primary input modality in your product (Deepgram Nova 3 Multilingual)

Avoid if...

  • You require guaranteed perfect transcripts for every accent without evaluation
  • 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

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 3 Multilingual.

  • 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

  • Can I use tools or structured outputs with Deepgram Nova 3 Multilingual?

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

  • Which providers serve Deepgram Nova 3 Multilingual?

    LLM.API currently lists: deepgram. Availability can vary by region and account.

  • What modalities does Deepgram Nova 3 Multilingual support?

    Deepgram Nova 3 Multilingual accepts audio and produces text according to its architecture metadata on LLM.API.

  • Does Deepgram Nova 3 Multilingual support streaming?

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

  • How is Deepgram Nova 3 Multilingual priced on LLM.API?

    Listed pricing metadata shows: In $5.2 / 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.

  • When should I choose Deepgram Nova 3 Multilingual?

    You will pipe transcripts into search or LLM summarization — especially when you specifically need Deepgram Nova 3 Multilingual.

  • What is the context length for Deepgram Nova 3 Multilingual?

    Reported context for Deepgram Nova 3 Multilingual is See provider specs. Always verify the active provider row if multiple providers are listed.

  • Where is the canonical page for Deepgram Nova 3 Multilingual?

    https://llmapi.ai/models/amazon-nova-3-multilingual/

  • Is Deepgram Nova 3 Multilingual a chat model?

    No—Deepgram Nova 3 Multilingual is categorized as a stt model. Use the matching API surface rather than assuming chat completions.

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