How much does GPT Live Transcribe cost per 1M tokens?

OpenAI lists — / — per 1M input / output tokens in the odnoga catalog. Through odnoga you pay that vendor price plus your plan margin, and every request is recorded with both numbers.

What is GPT Live Transcribe best for?

High-volume, latency-sensitive calls: classification, extraction, routing, short rewrites and summaries. User-facing chat where partial output should appear while the model is still writing.

Can I switch to GPT Live Transcribe without changing my code?

Yes. odnoga exposes one OpenAI-compatible endpoint, so switching means sending "gpt-live-transcribe" as the model id — or changing it in the managed prompt version, with no application deploy.

GPT Live Transcribe

GPT Live Transcribe by OpenAI: billed per media unit (second, image or minute of audio), — context. Call it through the odnoga LLM gateway; see the pricing page for the media rate.

Streaming

GPT Live Transcribe is served by OpenAI and called through odnoga with the same OpenAI-compatible request shape as every other model in the catalog.

Specification and price

Vendor	OpenAI
Model ID	gpt-live-transcribe
Context window	— tokens
Max output	—
Pricing	Per media unit (second / image / minute) — see pricing
Capabilities	Streaming

Vendor cost is the list price per million tokens as recorded in the odnoga catalog; your price applies your plan margin with the same formula that bills every request — see pricing. Pricing

Call it through odnoga

const res = await fetch("https://api.odnoga.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.ODNOGA_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-live-transcribe",
    messages: [{ role: "user", content: "Hello" }],
  }),
});

Same request shape for every vendor in the catalog — swap the model id and odnoga handles keys, routing, limits and cost accounting.

How to use GPT Live Transcribe

Derived from the odnoga catalog record for this model.

Best for

High-volume, latency-sensitive calls: classification, extraction, routing, short rewrites and summaries.
User-facing chat where partial output should appear while the model is still writing.

Not the right pick when

Anything that needs to read an image — this model takes text only.
Agent loops that must call your functions — tool calling is not available here.
Pipelines that require guaranteed JSON — parse defensively or pick a model with enforced JSON.
Anything where a wrong answer is costly without a human check — no model in the catalog removes that requirement.

Practical tips through odnoga

01Pin the model id in a managed prompt version, so a model swap is a version change you can compare and roll back, not an edit in application code.
02Compare it against 2–8 other models on the same frozen test cases in the evaluation laboratory before you make it the production default.
03For repeated identical deterministic calls, odnoga answer reuse returns the stored answer and bills no vendor tokens — turn it off for creative output.
04Set a fallback model on the route so a vendor incident degrades quality instead of returning an error, and a per-tenant budget so one caller cannot spend the month.