Deepgram Base Meeting
Up to 30%Deepgram Base Meeting brings speech-to-text speech-to-text to LLM.API for voice notes, calls, and caption pipelines.
What is Deepgram Base Meeting?
With Deepgram Base Meeting, LLM.API turns audio into text for product voice features. Speech-to-text model by Deepgram. The model id `base-meeting` keeps STT alongside your other endpoints.
Providers
LLM.API routes Deepgram Base Meeting to the providers below, with discounted effective rates versus list price.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $0.0125; out — per minute of audio | — | — |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test Deepgram Base Meeting right here — free to start.
Suggestions for your first prompt
Code snippet
Call Deepgram Base Meeting 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="base-meeting",
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": "base-meeting",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Downstream LLM prep
Feeds transcripts into summarization and action-item extractors.
Speech transcription
Converts spoken audio into text for captions, notes, and search. Grounded in the model's stt role rather than generic chat claims.
Caption generation
Produces base transcripts for subtitle workflows. Grounded in the model's stt role rather than generic chat claims.
Compliance archives
Creates text records of spoken interactions for audit trails. Grounded in the model's stt role rather than generic chat claims.
Meeting capture
Fits voice notes, calls, and meeting recording pipelines. Relevant for `base-meeting` workloads on LLM.API.
6 Most Valuable Use Cases
- Voice command capture in mobile apps with Deepgram Base Meeting
- Feeding voice input into LLM agents
- Podcast and video caption drafts with Deepgram Base Meeting
- Call-center transcription and QA
- Live captioning prototypes with Deepgram Base Meeting
- Compliance recording text archives
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 Base Meeting on LLM.API?
Unified AI Routing
Reach Deepgram Base Meeting and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale Deepgram Base Meeting.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Production: Trace prompts, tokens, and errors for Deepgram Base Meeting 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 Deepgram Base Meeting 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 (Deepgram Base Meeting)
- You need speech-to-text for batch or streaming audio (Deepgram Base Meeting)
- Voice is a primary input modality in your product (Deepgram Base Meeting)
Avoid if...
- You require guaranteed perfect transcripts for every accent without evaluation
- 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
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 Base Meeting.
- 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 modalities does Deepgram Base Meeting support?
Deepgram Base Meeting accepts audio and produces text according to its architecture metadata on LLM.API.
What is Deepgram Base Meeting?
Speech-to-text model by Deepgram. On LLM.API it is addressed as `base-meeting`.
How do I call Deepgram Base Meeting via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "base-meeting" and your LLM.API key. See the code snippet on this page.
When should I choose Deepgram Base Meeting?
You need speech-to-text for batch or streaming audio — especially when you specifically need Deepgram Base Meeting.
What are limitations of Deepgram Base Meeting?
Like other API models, Deepgram Base Meeting 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 Deepgram Base Meeting a chat model?
No—Deepgram Base Meeting is categorized as a stt model. Use the matching API surface rather than assuming chat completions.
Does Deepgram Base Meeting support streaming?
Streaming depends on the active provider; check the providers table on this page for flags.
Which providers serve Deepgram Base Meeting?
LLM.API currently lists: deepgram. Availability can vary by region and account.
How is Deepgram Base Meeting priced on LLM.API?
Listed pricing metadata shows: In $12.5 / 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.
Where is the canonical page for Deepgram Base Meeting?
https://llmapi.ai/models/base-meeting/
Does Deepgram Base Meeting 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.
What is the context length for Deepgram Base Meeting?
Reported context for Deepgram Base Meeting is See provider specs. Always verify the active provider row if multiple providers are listed.
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