GPT Transcribe
GPT 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.
GPT 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-transcribe |
| Context window | — tokens |
| Max output | — |
| Pricing | Per media unit (second / image / minute) — see pricing |
| Capabilities | — |
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-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 Transcribe
Derived from the odnoga catalog record for this model.
Best for
- High-volume, latency-sensitive calls: classification, extraction, routing, short rewrites and summaries.
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.
GPT Transcribe compared
| Model | Context | Input / 1M | Output / 1M | Capabilities |
|---|---|---|---|---|
| GPT Transcribe | — | — | — | — |
| ChatGPT (chat-latest) | 400K | $5 | $30 | Vision, Tools, JSON mode, Streaming |
| GPT Image 1 mini | — | $2 | $8 | Vision |
Questions
- How much does GPT 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 Transcribe best for?
- High-volume, latency-sensitive calls: classification, extraction, routing, short rewrites and summaries.
- Can I switch to GPT Transcribe without changing my code?
- Yes. odnoga exposes one OpenAI-compatible endpoint, so switching means sending "gpt-transcribe" as the model id — or changing it in the managed prompt version, with no application deploy.
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