Deepgram Whisper Tiny
Up to 30%Transcribe speech with Deepgram Whisper Tiny on LLM.API—batch or streaming audio to text with the same developer surface as your other models.
What is Deepgram Whisper Tiny?
With Deepgram Whisper Tiny, LLM.API turns audio into text for product voice features. Speech-to-text model by Deepgram. The model id `whisper-tiny` keeps STT alongside your other endpoints.
Providers
LLM.API routes Deepgram Whisper Tiny 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 Tiny right here — free to start.
Suggestions for your first prompt
Code snippet
Call Deepgram Whisper Tiny 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-tiny",
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-tiny",
"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. Grounded in the model's stt role rather than generic chat claims.
Multilingual audio
Handles diverse accents and languages depending on the model. Grounded in the model's stt role rather than generic chat claims.
Downstream LLM prep
Feeds transcripts into summarization and action-item extractors. Grounded in the model's stt role rather than generic chat claims.
Voice UX input
Powers voice-driven product interfaces and IVR handoffs. Grounded in the model's stt role rather than generic chat claims.
Compliance archives
Creates text records of spoken interactions for audit trails. Relevant for `whisper-tiny` workloads on LLM.API.
6 Most Valuable Use Cases
- Feeding voice input into LLM agents with Deepgram Whisper Tiny
- Compliance recording text archives
- Podcast and video caption drafts with Deepgram Whisper Tiny
- Live captioning prototypes
- Meeting notes and searchable recordings with Deepgram Whisper Tiny
- 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 Tiny on LLM.API?
Unified AI Routing
Practical: Reach Deepgram Whisper Tiny and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale Deepgram Whisper Tiny.
Reliability Layer
Practical: Retry and route across configured providers when a single upstream blips.
Observability
Production: Trace prompts, tokens, and errors for Deepgram Whisper Tiny 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
Production: Swap Deepgram Whisper Tiny 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 Tiny)
- You will pipe transcripts into search or LLM summarization (Deepgram Whisper Tiny)
- Voice is a primary input modality in your product (Deepgram Whisper Tiny)
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 Tiny.
- 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 Tiny?
Reported context for Deepgram Whisper Tiny is See provider specs. Always verify the active provider row if multiple providers are listed.
How do I call Deepgram Whisper Tiny via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "whisper-tiny" and your LLM.API key. See the code snippet on this page.
Does Deepgram Whisper Tiny 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.
Where is the canonical page for Deepgram Whisper Tiny?
https://llmapi.ai/models/openai-whisper-tiny/
When should I choose Deepgram Whisper Tiny?
Voice is a primary input modality in your product — especially when you specifically need Deepgram Whisper Tiny.
How is Deepgram Whisper Tiny 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.
Which providers serve Deepgram Whisper Tiny?
LLM.API currently lists: deepgram. Availability can vary by region and account.
What is Deepgram Whisper Tiny?
Speech-to-text model by Deepgram. On LLM.API it is addressed as `whisper-tiny`.
Can I use tools or structured outputs with Deepgram Whisper Tiny?
Tooling support varies; for pure stt models, prefer the modalities listed rather than assuming chat tools.
Does Deepgram Whisper Tiny support streaming?
Streaming depends on the active provider; check the providers table on this page for flags.
What are limitations of Deepgram Whisper Tiny?
Like other API models, Deepgram Whisper Tiny can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
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