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Deepgram Base Video

Up to 30%

Deepgram Base Video brings speech-to-text speech-to-text to LLM.API for voice notes, calls, and caption pipelines.

Start Using API

What is Deepgram Base Video?

With Deepgram Base Video, LLM.API turns audio into text for product voice features. Speech-to-text model by Deepgram. The model id `base-video` keeps STT alongside your other endpoints.

Providers

LLM.API routes Deepgram Base Video to the providers below, with discounted effective rates versus list price.

ProviderPricingContextCapabilities
deepgram30% offin $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 Video right here — free to start.

Deepgram Base Video
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Deepgram Base Video through the OpenAI-compatible API — STT via LLM.API (see docs for audio endpoints).

python
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-video",
    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-video",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Caption generation

    Produces base transcripts for subtitle workflows. Tuned to how teams typically call Deepgram Base Video.

  • Downstream LLM prep

    Feeds transcripts into summarization and action-item extractors. Relevant for `base-video` workloads on LLM.API.

  • Meeting capture

    Fits voice notes, calls, and meeting recording pipelines. Reflects speech-to-text positioning for this endpoint.

  • 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. Tuned to how teams typically call Deepgram Base Video.

6 Most Valuable Use Cases

  • Podcast and video caption drafts with Deepgram Base Video
  • Compliance recording text archives
  • Clinical or field note dictation workflows with Deepgram Base Video
  • Call-center transcription and QA
  • Live captioning prototypes with Deepgram Base Video
  • 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 Base Video on LLM.API?

  • Unified AI Routing

    Reach Deepgram Base Video and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Deepgram Base Video.

  • Reliability Layer

    Production: Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for Deepgram Base Video 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

    Production: Swap Deepgram Base Video 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 Video)
  • Voice is a primary input modality in your product (Deepgram Base Video)
  • You need speech-to-text for batch or streaming audio (Deepgram Base Video)

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 Video.

  • 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

  • Can I use tools or structured outputs with Deepgram Base Video?

    Tooling support varies; for pure stt models, prefer the modalities listed rather than assuming chat tools.

  • What modalities does Deepgram Base Video support?

    Deepgram Base Video accepts audio and produces text according to its architecture metadata on LLM.API.

  • How do I call Deepgram Base Video via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "base-video" and your LLM.API key. See the code snippet on this page.

  • How is Deepgram Base Video 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.

  • Is Deepgram Base Video a chat model?

    No—Deepgram Base Video is categorized as a stt model. Use the matching API surface rather than assuming chat completions.

  • Which providers serve Deepgram Base Video?

    LLM.API currently lists: deepgram. Availability can vary by region and account.

  • What is the context length for Deepgram Base Video?

    Reported context for Deepgram Base Video is See provider specs. Always verify the active provider row if multiple providers are listed.

  • When should I choose Deepgram Base Video?

    You will pipe transcripts into search or LLM summarization — especially when you specifically need Deepgram Base Video.

  • What is Deepgram Base Video?

    Speech-to-text model by Deepgram. On LLM.API it is addressed as `base-video`.

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