What is GPT-4o Transcribe best used for?

GPT-4o Transcribe is best for real-time or batch transcription of meetings, calls, podcasts, and other audio into structured text.

What modalities does GPT-4o Transcribe support?

GPT-4o Transcribe accepts audio input and returns text output; it is not intended for direct image or video understanding.

How is GPT-4o Transcribe priced on LLM.API?

GPT-4o Transcribe is billed on LLM.API per unit of audio processed, typically metered in minutes or seconds rather than tokens.

What is the context window of GPT-4o Transcribe?

GPT-4o Transcribe effectively handles long audio segments, but downstream text usage is constrained by the GPT-4o text context window.

How fast is GPT-4o Transcribe in terms of latency?

GPT-4o Transcribe is optimized for low latency and can stream partial transcriptions for near real-time use cases.

How do I call GPT-4o Transcribe through LLM.API?

You invoke GPT-4o Transcribe by specifying the model name in LLM.API audio endpoints and sending your audio file or stream payload.

How does GPT-4o Transcribe compare to general GPT-4o chat models?

GPT-4o Transcribe focuses on audio-to-text accuracy and efficiency, while general GPT-4o chat models focus on multi-turn natural language reasoning.

Does GPT-4o Transcribe support multiple languages?

GPT-4o Transcribe supports multilingual transcription, but accuracy can vary by language and audio quality.

What are the main limitations of GPT-4o Transcribe?

GPT-4o Transcribe may struggle with heavy background noise, overlapping speakers, very low-quality audio, or highly domain-specific jargon.

Which endpoint do I call for GPT-4o Transcribe?

POST https://api.llmapi.ai/v1/chat/completions with model "gpt-4o-transcribe" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to GPT-4o Transcribe on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does GPT-4o Transcribe support function calling and structured outputs?

Function calling: not supported. Structured outputs: not supported.

GPT-4o Transcribe API: Endpoint & Pricing

GPT-4o Transcribe is an OpenAI model specialized for converting audio into accurate, time-aligned text transcripts.

Start Using API

What is GPT-4o Transcribe?

GPT-4o Transcribe is a transcription-focused variant of OpenAI’s GPT-4o model designed to turn spoken audio into structured text. It is mainly used for tasks like meeting notes, call and podcast transcription, caption generation, and transforming voice recordings into searchable documents. It also supports workflows that combine transcription with light understanding, such as summarizing or tagging segments of speech. It belongs to the GPT-4o family of multimodal OpenAI models adapted for speech-to-text transcription workloads.

Can you self-host GPT-4o Transcribe?

No. We found no public weights for GPT-4o Transcribe on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API key See API code ↓

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

List price by provider

InputOutput

openai$0 in

$0 out

Provider list prices; the LLM.API discount applies on top.

Provider	Input	Output	Cache read /M	Latency	Throughput	Uptime
openai	in $0; out — per minute of audio	—	—

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.

Try this model

Test GPT-4o Transcribe right here — free to start.

GPT-4o Transcribe

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to gpt-4o-transcribe and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-transcribe",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="gpt-4o-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)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "gpt-4o-transcribe",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="gpt-4o-transcribe",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "gpt-4o-transcribe",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID gpt-4o-transcribe · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Not supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Not supported	`response_format: {"type": "json_object"}`
Structured outputs	Not supported	`response_format: {"type": "json_schema", …}`
Streaming	Not supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Uptime

30-Day Uptime

96.70%

Past Incidents (30d)

Error rate (24h)

9.27%

Last 30 days

26/30 days operational | 96.70% uptime

30 days ago Today

Operational Degraded Outage Maintenance

See All Incidents

GPT-4o Transcribe in production

Best workloadsLLM.API features

What the model is good at.

Speech Transcription

Converts spoken audio into accurate, punctuated text transcripts, handling diverse speakers, accents, and recording conditions in real time.
Multilingual Transcription

Transcribes speech from multiple languages into text, preserving language-specific characters, names, and terminology where supported.
Conversation Transcripts

Generates structured transcripts for dialogues, meetings, and interviews, distinguishing speakers when metadata or channel separation is available.
Media Captioning

Produces text captions from audio tracks in videos or podcasts, supporting workflows for accessibility, search, and content indexing.
Streaming Monitoring

Supports near real-time transcription for live audio streams, enabling monitoring, compliance checks, and rapid downstream processing.

What you get when you call GPT-4o Transcribe through LLM.API.

Intelligent AI Routing

Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or retraining clients.
One endpoint, any model
Cost-Aware Orchestration

Use pricing-aware routing, quotas, and policies to keep spend predictable while still hitting quality and latency targets across models and clouds.
Control spend by design
Automatic Fallback Flows

Define failover rules once and let LLM.API retry on alternate providers or models when timeouts, rate limits, or provider outages occur.
Resilience built in
Full-Stack Observability

Get traces, metrics, and structured logs for every request so you can debug prompts, compare providers, and tune performance in production.
See every token
Task-Level Abstractions

Describe tasks like chat, extraction, or tooling once and let LLM.API pick and tune models behind the scenes, simplifying integration and future upgrades.
Code to tasks, not models
High-Throughput Batch

Submit large jobs as batches with automatic chunking, retries, and aggregation to slash costs and saturate throughput without writing glue code.
Scale jobs, not code

When to Use — When NOT to Use

Use it if...

You need accurate transcription of short English audio clips into text for downstream processing.
Your use case involves batch-transcribing meeting recordings or interviews for search and summarization.
You need to convert user-uploaded voice notes into text for productivity or messaging apps.
Your use case involves generating captions or subtitles from prerecorded video or podcast audio.
You need a reliable OpenAI-native transcription model that integrates cleanly with other GPT-4o workflows.
Your use case involves prototyping speech-to-text features without managing separate ASR infrastructure.

Avoid if...

You need advanced spoken-language understanding, reasoning, or dialog beyond simple transcription of audio.
Your workload requires ultra-low-latency streaming ASR for live captioning or voice assistants.
You need high-quality transcription for many low-resource languages not well-covered by OpenAI models.
Your workload requires detailed diarization, speaker identification, or complex audio event classification.
You need offline, on-device transcription where sending audio to cloud services is impossible.
Your workload requires tightly controlled, fully open-source ASR components for strict compliance constraints.

LLM API rating for GPT-4o Transcribe

★ 3.7/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

5.0Production adoptionTop 1% of production API volume

2.8Benchmark qualityNo public Arena rank — scored on declared reasoning support

1.9Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.7Coding 3.5Voice & audio 3.8

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about GPT-4o Transcribe

LMArena—Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

Developers on the OpenAI community report significant issues with GPT-4o-transcribe truncating or omitting words, particularly at the beginning and end of short messages, often making it less reliable than Whisper-1.
Developers have shared mixed results regarding accuracy, with some reporting high-quality transcripts while others experienced total nonsense or random text generation, especially when approaching the 1,500-second maximum length limit.
Users on Hacker News and OpenAI forums noted that while the model handles background noise better than Whisper, it has higher latency and may smooth transcripts, resulting in lower word-for-word precision.

SOURCES

Frequently Asked Questions

What is GPT-4o Transcribe?

GPT-4o Transcribe is an OpenAI GPT-4o-based model on LLM.API specialized for accurate, low-latency speech-to-text transcription.
What is GPT-4o Transcribe best used for?

GPT-4o Transcribe is best for real-time or batch transcription of meetings, calls, podcasts, and other audio into structured text.
What modalities does GPT-4o Transcribe support?

GPT-4o Transcribe accepts audio input and returns text output; it is not intended for direct image or video understanding.
How is GPT-4o Transcribe priced on LLM.API?

GPT-4o Transcribe is billed on LLM.API per unit of audio processed, typically metered in minutes or seconds rather than tokens.
What is the context window of GPT-4o Transcribe?

GPT-4o Transcribe effectively handles long audio segments, but downstream text usage is constrained by the GPT-4o text context window.
How fast is GPT-4o Transcribe in terms of latency?

GPT-4o Transcribe is optimized for low latency and can stream partial transcriptions for near real-time use cases.
How do I call GPT-4o Transcribe through LLM.API?

You invoke GPT-4o Transcribe by specifying the model name in LLM.API audio endpoints and sending your audio file or stream payload.
How does GPT-4o Transcribe compare to general GPT-4o chat models?

GPT-4o Transcribe focuses on audio-to-text accuracy and efficiency, while general GPT-4o chat models focus on multi-turn natural language reasoning.
Does GPT-4o Transcribe support multiple languages?

GPT-4o Transcribe supports multilingual transcription, but accuracy can vary by language and audio quality.
What are the main limitations of GPT-4o Transcribe?

GPT-4o Transcribe may struggle with heavy background noise, overlapping speakers, very low-quality audio, or highly domain-specific jargon.

COMPARE

Competitive Models

GPT-4o Mini Transcribe is an OpenAI model specialized for converting spoken language in audio into accurate text. It is optimized for lightweight, fast transcription while maintaining good recognition quality across common speech scenarios.

GPT-4o Mini TTS

Text-to-Speech

GPT-4o Mini TTS is a text-to-speech variant of OpenAI’s lightweight GPT-4o Mini model, designed to generate natural-sounding spoken audio from text with low latency and efficient resource usage.

GPT-5.1

Up to 30%

Instruction Following

GPT-5.1 is an OpenAI language model; as of mid-2026, OpenAI has not publicly released technical details or documentation about it.

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GPT-4o Transcribe API: Endpoint & Pricing

What is GPT-4o Transcribe?

Can you self-host GPT-4o Transcribe?

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Uptime

GPT-4o Transcribe in production

Speech Transcription

Multilingual Transcription

Conversation Transcripts

Media Captioning

Streaming Monitoring

Intelligent AI Routing

Cost-Aware Orchestration

Automatic Fallback Flows

Full-Stack Observability

Task-Level Abstractions

High-Throughput Batch