Deepgram Base Conversational AI
Up to 30%Transcribe speech with Deepgram Base Conversational AI on LLM.API—batch or streaming audio to text with the same developer surface as your other models.
What is Deepgram Base Conversational AI?
Deepgram Base Conversational AI is a speech-to-text model on LLM.API (`base-conversationalai`). Speech-to-text model by Deepgram. Feed audio and receive text transcripts for captions, agents, and searchable archives.
Providers
LLM.API routes Deepgram Base Conversational AI 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 Conversational AI right here — free to start.
Suggestions for your first prompt
Code snippet
Call Deepgram Base Conversational AI 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-conversationalai",
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-conversationalai",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Compliance archives
Creates text records of spoken interactions for audit trails. Reflects speech-to-text positioning for this endpoint.
Caption generation
Produces base transcripts for subtitle workflows. Relevant for `base-conversationalai` workloads on LLM.API.
Downstream LLM prep
Feeds transcripts into summarization and action-item extractors. 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-conversationalai` workloads on LLM.API.
Streaming recognition
Supports low-latency partial transcripts when the provider offers streaming STT. Tuned to how teams typically call Deepgram Base Conversational AI.
6 Most Valuable Use Cases
- Feeding voice input into LLM agents with Deepgram Base Conversational AI
- Clinical or field note dictation workflows
- Call-center transcription and QA with Deepgram Base Conversational AI
- Compliance recording text archives
- Meeting notes and searchable recordings with Deepgram Base Conversational AI
- Live captioning prototypes
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 Conversational AI on LLM.API?
Unified AI Routing
Reach Deepgram Base Conversational AI and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale Deepgram Base Conversational AI.
Reliability Layer
Production: Retry and route across configured providers when a single upstream blips.
Observability
Production: Trace prompts, tokens, and errors for Deepgram Base Conversational AI 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
Swap Deepgram Base Conversational AI 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 Base Conversational AI)
- You will pipe transcripts into search or LLM summarization (Deepgram Base Conversational AI)
- Voice is a primary input modality in your product (Deepgram Base Conversational AI)
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 Base Conversational AI.
- 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 are limitations of Deepgram Base Conversational AI?
Like other API models, Deepgram Base Conversational AI 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 Base Conversational AI?
Tooling support varies; for pure stt models, prefer the modalities listed rather than assuming chat tools.
When should I choose Deepgram Base Conversational AI?
Voice is a primary input modality in your product — especially when you specifically need Deepgram Base Conversational AI.
Which providers serve Deepgram Base Conversational AI?
LLM.API currently lists: deepgram. Availability can vary by region and account.
Is Deepgram Base Conversational AI a chat model?
No—Deepgram Base Conversational AI is categorized as a stt model. Use the matching API surface rather than assuming chat completions.
How is Deepgram Base Conversational AI 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.
What is Deepgram Base Conversational AI?
Speech-to-text model by Deepgram. On LLM.API it is addressed as `base-conversationalai`.
What modalities does Deepgram Base Conversational AI support?
Deepgram Base Conversational AI accepts audio and produces text according to its architecture metadata on LLM.API.
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