Azure Whisper
Up to 30%Transcribe speech with Azure Whisper on LLM.API—batch or streaming audio to text with the same developer surface as your other models.
What is Azure Whisper?
Azure Whisper is a speech-to-text model on LLM.API (`whisper`). Speech-to-text model by Azure. Feed audio and receive text transcripts for captions, agents, and searchable archives.
Providers
LLM.API routes Azure Whisper to the providers below, with discounted effective rates versus list price.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| See LLM.API pricing | See provider specs | — |
Try this model
Test Azure Whisper right here — free to start.
Suggestions for your first prompt
Code snippet
Call Azure Whisper 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",
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",
"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` workloads on LLM.API.
Voice UX input
Powers voice-driven product interfaces and IVR handoffs. Relevant for `whisper` workloads on LLM.API.
Speech transcription
Converts spoken audio into text for captions, notes, and search.
Meeting capture
Fits voice notes, calls, and meeting recording pipelines.
Multilingual audio
Handles diverse accents and languages depending on the model. Tuned to how teams typically call Azure Whisper.
6 Most Valuable Use Cases
- Live captioning prototypes with Azure Whisper
- Voice command capture in mobile apps
- Feeding voice input into LLM agents with Azure Whisper
- Compliance recording text archives
- Podcast and video caption drafts with Azure Whisper
- 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 Azure Whisper on LLM.API?
Unified AI Routing
Reach Azure Whisper and sibling models through one OpenAI-compatible endpoint.
Cost Control
Compare provider price points and keep spend visible as you scale Azure Whisper.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Production: Trace prompts, tokens, and errors for Azure Whisper 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
Practical: Swap Azure Whisper for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You will pipe transcripts into search or LLM summarization (Azure Whisper)
- You need speech-to-text for batch or streaming audio (Azure Whisper)
- Voice is a primary input modality in your product (Azure Whisper)
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 Azure Whisper.
- 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 Azure Whisper?
Reported context for Azure Whisper is See provider specs. Always verify the active provider row if multiple providers are listed.
What are limitations of Azure Whisper?
Like other API models, Azure Whisper can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
Is Azure Whisper a chat model?
No—Azure Whisper is categorized as a stt model. Use the matching API surface rather than assuming chat completions.
When should I choose Azure Whisper?
You will pipe transcripts into search or LLM summarization — especially when you specifically need Azure Whisper.
What is Azure Whisper?
Speech-to-text model by Azure. On LLM.API it is addressed as `whisper`.
Where is the canonical page for Azure Whisper?
https://llmapi.ai/models/openai-whisper/
Which providers serve Azure Whisper?
LLM.API currently lists: azure. Availability can vary by region and account.
Does Azure Whisper support streaming?
Streaming depends on the active provider; check the providers table on this page for flags.
How do I call Azure Whisper via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "whisper" and your LLM.API key. See the code snippet on this page.
What modalities does Azure Whisper support?
Azure Whisper accepts audio and produces text according to its architecture metadata on LLM.API.
Can I use tools or structured outputs with Azure Whisper?
Tooling support varies; for pure stt models, prefer the modalities listed rather than assuming chat tools.
How is Azure Whisper priced on LLM.API?
Listed pricing metadata shows: In $6.00 / 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.
COMPARE
Competitive Models
Deepgram Base Finance
Another stt option from the speech-to-text lineup on LLM.API.
Deepgram Base Conversational AI
Sibling-style choice: Deepgram Base Conversational AI (base-conversationalai) for comparable stt workloads.
Deepgram Whisper Medium
Sibling-style choice: Deepgram Whisper Medium (whisper-medium) for comparable stt workloads.
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