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Deepgram Whisper Small

Up to 30%

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

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What is Deepgram Whisper Small?

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

Providers

LLM.API routes Deepgram Whisper Small 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 Small right here — free to start.

Deepgram Whisper Small
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Deepgram Whisper Small 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-small",
    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-small",
  "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. Tuned to how teams typically call Deepgram Whisper Small.

  • Voice UX input

    Powers voice-driven product interfaces and IVR handoffs. Reflects speech-to-text positioning for this endpoint.

  • Caption generation

    Produces base transcripts for subtitle workflows. Relevant for `whisper-small` workloads on LLM.API.

  • Multilingual audio

    Handles diverse accents and languages depending on the model. Grounded in the model's stt role rather than generic chat claims.

  • Compliance archives

    Creates text records of spoken interactions for audit trails. Grounded in the model's stt role rather than generic chat claims.

6 Most Valuable Use Cases

  • Call-center transcription and QA with Deepgram Whisper Small
  • Compliance recording text archives
  • Podcast and video caption drafts with Deepgram Whisper Small
  • Live captioning prototypes
  • Meeting notes and searchable recordings with Deepgram Whisper Small
  • 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 Whisper Small on LLM.API?

  • Unified AI Routing

    Production: Reach Deepgram Whisper Small and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale Deepgram Whisper Small.

  • Reliability Layer

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

  • Observability

    Production: Trace prompts, tokens, and errors for Deepgram Whisper Small alongside the rest of your stack.

  • Drop-in SDKs

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

  • Model Breadth

    Production: Swap Deepgram Whisper Small for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

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

Avoid if...

  • You need text-to-speech or chat generation instead of transcription
  • You require guaranteed perfect transcripts for every accent without evaluation
  • 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 Small.

  • 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 are limitations of Deepgram Whisper Small?

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

  • Does Deepgram Whisper Small support streaming?

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

  • Which providers serve Deepgram Whisper Small?

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

  • Does Deepgram Whisper Small 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 Small support?

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

  • Can I use tools or structured outputs with Deepgram Whisper Small?

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

  • What is Deepgram Whisper Small?

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

  • Is Deepgram Whisper Small a chat model?

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

  • When should I choose Deepgram Whisper Small?

    You need speech-to-text for batch or streaming audio — especially when you specifically need Deepgram Whisper Small.

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