Deepgram Whisper Base
Up to 30%Transcribe speech with Deepgram Whisper Base on LLM.API—batch or streaming audio to text with the same developer surface as your other models.
What is Deepgram Whisper Base?
Deepgram Whisper Base is a speech-to-text model on LLM.API (`whisper-base`). Speech-to-text model by Deepgram. Feed audio and receive text transcripts for captions, agents, and searchable archives.
Providers
LLM.API routes Deepgram Whisper Base to the providers below, with discounted effective rates versus list price.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $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 Base right here — free to start.
Suggestions for your first prompt
Code snippet
Call Deepgram Whisper Base through the OpenAI-compatible API — STT via LLM.API (see docs for audio endpoints).
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-base",
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-base",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Streaming recognition
Supports low-latency partial transcripts when the provider offers streaming STT. Relevant for `whisper-base` workloads on LLM.API.
Voice UX input
Powers voice-driven product interfaces and IVR handoffs. Grounded in the model's stt role rather than generic chat claims.
Multilingual audio
Handles diverse accents and languages depending on the model. Reflects speech-to-text positioning for this endpoint.
Meeting capture
Fits voice notes, calls, and meeting recording pipelines.
Compliance archives
Creates text records of spoken interactions for audit trails. Tuned to how teams typically call Deepgram Whisper Base.
6 Most Valuable Use Cases
- Clinical or field note dictation workflows with Deepgram Whisper Base
- Voice command capture in mobile apps
- Feeding voice input into LLM agents with Deepgram Whisper Base
- Podcast and video caption drafts
- Compliance recording text archives with Deepgram Whisper Base
- Meeting notes and searchable recordings
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 Base on LLM.API?
Unified AI Routing
Practical: Reach Deepgram Whisper Base and sibling models through one OpenAI-compatible endpoint.
Cost Control
Practical: Compare provider price points and keep spend visible as you scale Deepgram Whisper Base.
Reliability Layer
Practical: Retry and route across configured providers when a single upstream blips.
Observability
Trace prompts, tokens, and errors for Deepgram Whisper Base 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
Practical: Swap Deepgram Whisper Base 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 Base)
- You will pipe transcripts into search or LLM summarization (Deepgram Whisper Base)
- Voice is a primary input modality in your product (Deepgram Whisper Base)
Avoid if...
- 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
- You require guaranteed perfect transcripts for every accent without evaluation
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 Base.
- 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
Does Deepgram Whisper Base 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.
Is Deepgram Whisper Base a chat model?
No—Deepgram Whisper Base is categorized as a stt model. Use the matching API surface rather than assuming chat completions.
Which providers serve Deepgram Whisper Base?
LLM.API currently lists: deepgram. Availability can vary by region and account.
Does Deepgram Whisper Base support streaming?
Streaming depends on the active provider; check the providers table on this page for flags.
What is Deepgram Whisper Base?
Speech-to-text model by Deepgram. On LLM.API it is addressed as `whisper-base`.
What is the context length for Deepgram Whisper Base?
Reported context for Deepgram Whisper Base is See provider specs. Always verify the active provider row if multiple providers are listed.
Where is the canonical page for Deepgram Whisper Base?
https://llmapi.ai/models/openai-whisper-base/
What modalities does Deepgram Whisper Base support?
Deepgram Whisper Base accepts audio and produces text according to its architecture metadata on LLM.API.
What are limitations of Deepgram Whisper Base?
Like other API models, Deepgram Whisper Base can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
Can I use tools or structured outputs with Deepgram Whisper Base?
Tooling support varies; for pure stt models, prefer the modalities listed rather than assuming chat tools.
How is Deepgram Whisper Base priced on LLM.API?
Listed pricing metadata shows: In $4.8 / 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.
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