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Deepgram Whisper Large

Up to 30%

Transcribe speech with Deepgram Whisper Large on LLM.API—batch or streaming audio to text with the same developer surface as your other models.

Start Using API

What is Deepgram Whisper Large?

With Deepgram Whisper Large, LLM.API turns audio into text for product voice features. Speech-to-text model by Deepgram. The model id `whisper-large` keeps STT alongside your other endpoints.

Providers

LLM.API routes Deepgram Whisper Large to the providers below, with discounted effective rates versus list price.

ProviderPricingContextCapabilities
deepgram30% offin $0.0048; out — per minute of audio——

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

Try this model

Test Deepgram Whisper Large right here — free to start.

Deepgram Whisper Large
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Deepgram Whisper Large 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="whisper-large",
    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": "whisper-large",
  "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. Grounded in the model's stt role rather than generic chat claims.

  • Caption generation

    Produces base transcripts for subtitle workflows. Grounded in the model's stt role rather than generic chat claims.

  • Multilingual audio

    Handles diverse accents and languages depending on the model.

  • Compliance archives

    Creates text records of spoken interactions for audit trails. Tuned to how teams typically call Deepgram Whisper Large.

  • Voice UX input

    Powers voice-driven product interfaces and IVR handoffs.

6 Most Valuable Use Cases

  • Live captioning prototypes with Deepgram Whisper Large
  • Meeting notes and searchable recordings
  • Voice command capture in mobile apps with Deepgram Whisper Large
  • Call-center transcription and QA
  • Podcast and video caption drafts with Deepgram Whisper Large
  • Feeding voice input into LLM agents

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 Whisper Large on LLM.API?

  • Unified AI Routing

    Reach Deepgram Whisper Large and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Deepgram Whisper Large.

  • Reliability Layer

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

  • Observability

    Production: Trace prompts, tokens, and errors for Deepgram Whisper Large 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 Whisper Large 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 Whisper Large)
  • You need speech-to-text for batch or streaming audio (Deepgram Whisper Large)
  • Voice is a primary input modality in your product (Deepgram Whisper Large)

Avoid if...

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

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 Whisper Large.

  • 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

  • What is the context length for Deepgram Whisper Large?

    Reported context for Deepgram Whisper Large is See provider specs. Always verify the active provider row if multiple providers are listed.

  • Is Deepgram Whisper Large a chat model?

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

  • Where is the canonical page for Deepgram Whisper Large?

    https://llmapi.ai/models/openai-whisper-large/

  • Does Deepgram Whisper Large 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 modalities does Deepgram Whisper Large support?

    Deepgram Whisper Large accepts audio and produces text according to its architecture metadata on LLM.API.

  • What is Deepgram Whisper Large?

    Speech-to-text model by Deepgram. On LLM.API it is addressed as `whisper-large`.

  • How do I call Deepgram Whisper Large via API?

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

  • Which providers serve Deepgram Whisper Large?

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

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