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Deepgram Whisper Medium

Up to 30%

Transcribe speech with Deepgram Whisper Medium 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 Medium?

Deepgram Whisper Medium is a speech-to-text model on LLM.API (`whisper-medium`). Speech-to-text model by Deepgram. Feed audio and receive text transcripts for captions, agents, and searchable archives.

Providers

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

Deepgram Whisper Medium
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

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

5 Core Capabilities

  • Meeting capture

    Fits voice notes, calls, and meeting recording pipelines. Tuned to how teams typically call Deepgram Whisper Medium.

  • 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. Tuned to how teams typically call Deepgram Whisper Medium.

  • Multilingual audio

    Handles diverse accents and languages depending on the model. Relevant for `whisper-medium` workloads on LLM.API.

  • Compliance archives

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

6 Most Valuable Use Cases

  • Compliance recording text archives with Deepgram Whisper Medium
  • Meeting notes and searchable recordings
  • Voice command capture in mobile apps with Deepgram Whisper Medium
  • Live captioning prototypes
  • Clinical or field note dictation workflows with Deepgram Whisper Medium
  • 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 Medium on LLM.API?

  • Unified AI Routing

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

  • Cost Control

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

  • Reliability Layer

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

  • Observability

    Trace prompts, tokens, and errors for Deepgram Whisper Medium 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

    Swap Deepgram Whisper Medium 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 Medium)
  • You will pipe transcripts into search or LLM summarization (Deepgram Whisper Medium)
  • Voice is a primary input modality in your product (Deepgram Whisper Medium)

Avoid if...

  • Your audio cannot leave your compliance boundary and you lack an approved provider path
  • You require guaranteed perfect transcripts for every accent without evaluation
  • 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 Whisper Medium.

  • 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

  • When should I choose Deepgram Whisper Medium?

    You will pipe transcripts into search or LLM summarization — especially when you specifically need Deepgram Whisper Medium.

  • What are limitations of Deepgram Whisper Medium?

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

  • Where is the canonical page for Deepgram Whisper Medium?

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

  • Does Deepgram Whisper Medium support streaming?

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

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

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

  • What is Deepgram Whisper Medium?

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

  • What modalities does Deepgram Whisper Medium support?

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

  • What is the context length for Deepgram Whisper Medium?

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

  • How do I call Deepgram Whisper Medium via API?

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

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