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AssemblyAI U3 RT Pro

Up to 30%

Transcribe speech with AssemblyAI U3 RT Pro on LLM.API—batch or streaming audio to text with the same developer surface as your other models.

Start Using API

What is AssemblyAI U3 RT Pro?

AssemblyAI U3 RT Pro is a speech-to-text model on LLM.API (`u3-rt-pro`). Streaming speech-to-text model by Assemblyai. Feed audio and receive text transcripts for captions, agents, and searchable archives.

Providers

LLM.API routes AssemblyAI U3 RT Pro to the providers below, with discounted effective rates versus list price.

ProviderPricingContextCapabilities
assemblyai30% offin $0.0075; out — per minute of audio——

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test AssemblyAI U3 RT Pro right here — free to start.

AssemblyAI U3 RT Pro
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call AssemblyAI U3 RT Pro 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="u3-rt-pro",
    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": "u3-rt-pro",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Multilingual audio

    Handles diverse accents and languages depending on the model. Relevant for `u3-rt-pro` workloads on LLM.API.

  • Voice UX input

    Powers voice-driven product interfaces and IVR handoffs. Reflects streaming speech-to-text positioning for this endpoint.

  • 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. Reflects streaming speech-to-text positioning for this endpoint.

  • Speech transcription

    Converts spoken audio into text for captions, notes, and search. Reflects streaming speech-to-text positioning for this endpoint.

6 Most Valuable Use Cases

  • Podcast and video caption drafts with AssemblyAI U3 RT Pro
  • Live captioning prototypes
  • Call-center transcription and QA with AssemblyAI U3 RT Pro
  • Clinical or field note dictation workflows
  • Voice command capture in mobile apps with AssemblyAI U3 RT Pro
  • 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 AssemblyAI U3 RT Pro on LLM.API?

  • Unified AI Routing

    Production: Reach AssemblyAI U3 RT Pro and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale AssemblyAI U3 RT Pro.

  • Reliability Layer

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

  • Observability

    Trace prompts, tokens, and errors for AssemblyAI U3 RT Pro 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 AssemblyAI U3 RT Pro 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 (AssemblyAI U3 RT Pro)
  • You will pipe transcripts into search or LLM summarization (AssemblyAI U3 RT Pro)
  • Voice is a primary input modality in your product (AssemblyAI U3 RT Pro)

Avoid if...

  • Your audio cannot leave your compliance boundary and you lack an approved provider path
  • You require guaranteed perfect transcripts for every accent without evaluation
  • 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 AssemblyAI U3 RT Pro.

  • 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

  • Does AssemblyAI U3 RT Pro 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.

  • Which providers serve AssemblyAI U3 RT Pro?

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

  • How is AssemblyAI U3 RT Pro priced on LLM.API?

    Listed pricing metadata shows: In $7.5 / 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 AssemblyAI U3 RT Pro?

    Streaming speech-to-text model by Assemblyai. On LLM.API it is addressed as `u3-rt-pro`.

  • How do I call AssemblyAI U3 RT Pro via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "u3-rt-pro" and your LLM.API key. See the code snippet on this page.

  • Does AssemblyAI U3 RT Pro support streaming?

    Streaming depends on the active provider; check the providers table on this page for flags.

  • What is the context length for AssemblyAI U3 RT Pro?

    Reported context for AssemblyAI U3 RT Pro is See provider specs. Always verify the active provider row if multiple providers are listed.

  • Where is the canonical page for AssemblyAI U3 RT Pro?

    https://llmapi.ai/models/u3-rt-pro/

  • What modalities does AssemblyAI U3 RT Pro support?

    AssemblyAI U3 RT Pro accepts audio and produces text according to its architecture metadata on LLM.API.

  • Is AssemblyAI U3 RT Pro a chat model?

    No—AssemblyAI U3 RT Pro is categorized as a stt model. Use the matching API surface rather than assuming chat completions.

  • What are limitations of AssemblyAI U3 RT Pro?

    Like other API models, AssemblyAI U3 RT Pro 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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