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GPT Transcribe

Up to 30%

GPT Transcribe brings speech-to-text speech-to-text to LLM.API for voice notes, calls, and caption pipelines.

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What is GPT Transcribe?

GPT Transcribe is a speech-to-text model on LLM.API (`gpt-transcribe`). Speech-to-text model by Azure. Feed audio and receive text transcripts for captions, agents, and searchable archives.

Providers

LLM.API routes GPT Transcribe to the providers below, with discounted effective rates versus list price.

ProviderPricingContextCapabilities
azure30% offin $0.0045; out — per minute of audio128K tokens—

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

Try this model

Test GPT Transcribe right here — free to start.

GPT Transcribe
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call GPT Transcribe 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="gpt-transcribe",
    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": "gpt-transcribe",
  "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. Reflects 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.

  • Multilingual audio

    Handles diverse accents and languages depending on the model. Relevant for `gpt-transcribe` workloads on LLM.API.

  • Speech transcription

    Converts spoken audio into text for captions, notes, and search. Grounded in the model's stt role rather than generic chat claims.

  • Meeting capture

    Fits voice notes, calls, and meeting recording pipelines. Relevant for `gpt-transcribe` workloads on LLM.API.

6 Most Valuable Use Cases

  • Voice command capture in mobile apps with GPT Transcribe
  • Podcast and video caption drafts
  • Live captioning prototypes with GPT Transcribe
  • Meeting notes and searchable recordings
  • Call-center transcription and QA with GPT Transcribe
  • 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 GPT Transcribe on LLM.API?

  • Unified AI Routing

    Reach GPT Transcribe and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale GPT Transcribe.

  • Reliability Layer

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

  • Observability

    Practical: Trace prompts, tokens, and errors for GPT Transcribe 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 GPT Transcribe 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 (GPT Transcribe)
  • You will pipe transcripts into search or LLM summarization (GPT Transcribe)
  • Voice is a primary input modality in your product (GPT Transcribe)

Avoid if...

  • You need text-to-speech or chat generation instead of transcription
  • Your audio cannot leave your compliance boundary and you lack an approved provider path
  • 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 GPT Transcribe.

  • 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 GPT Transcribe?

    Reported context for GPT Transcribe is 128K tokens. Always verify the active provider row if multiple providers are listed.

  • When should I choose GPT Transcribe?

    Voice is a primary input modality in your product — especially when you specifically need GPT Transcribe.

  • Does GPT Transcribe support streaming?

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

  • How is GPT Transcribe priced on LLM.API?

    Listed pricing metadata shows: In $4.5 / 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 GPT Transcribe a chat model?

    No—GPT Transcribe is categorized as a stt model. Use the matching API surface rather than assuming chat completions.

  • What modalities does GPT Transcribe support?

    GPT Transcribe accepts audio and produces text according to its architecture metadata on LLM.API.

  • What are limitations of GPT Transcribe?

    Like other API models, GPT Transcribe can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • Which providers serve GPT Transcribe?

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

  • How do I call GPT Transcribe via API?

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

  • Where is the canonical page for GPT Transcribe?

    https://llmapi.ai/models/openai-gpt-transcribe/

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