Deepgram Whisper Medium API: Endpoint & 30% Discount
Transcribe speech with Deepgram Whisper Medium on LLM.API—batch or streaming audio to text with the same developer surface as your other models.
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.
Can you self-host Deepgram Whisper Medium?
No. We found no public weights for Deepgram Whisper Medium on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Providers
LLM.API routes Deepgram Whisper Medium to the providers below, with discounted effective rates versus list price.
List price by provider
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $0; out — per minute of audio | — | — |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 24 Sept 2026.
Try this model
Test Deepgram Whisper Medium right here — free to start.
Suggestions for your first prompt
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
Browse available modelsDeepgram Whisper Medium in production
What the model is good at.
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.
What you get when you call Deepgram Whisper Medium through LLM.API.
Unified AI Routing
Reach Deepgram Whisper Medium and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.Cost Control
Compare provider price points and keep spend visible as you scale Deepgram Whisper Medium.
More performance, less spend.Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.Observability
Trace prompts, tokens, and errors for Deepgram Whisper Medium alongside the rest of your stack.
See every token, everywhere.Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.Model Breadth
Swap Deepgram Whisper Medium for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.
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.
COMPARE
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