GPT Image 1 mini
GPT Image 1 mini by OpenAI: $2 input and $8 output per 1M tokens, — context. Call it through the odnoga LLM gateway.
GPT Image 1 mini is served by OpenAI and called through odnoga with the same OpenAI-compatible request shape as every other model in the catalog. It accepts images alongside text. Cached input is billed at $0.2 per 1M tokens, so repeated prefixes cost less.
Specification and price
| Vendor | OpenAI |
|---|---|
| Model ID | gpt-image-1-mini |
| Context window | — tokens |
| Max output | — |
| Capabilities | Vision |
| Per 1M tokens | Vendor cost | Your price on Free+7% |
|---|---|---|
| Input | $2 | $2.15 |
| Output | $8 | $8.60 |
| Cached input | $0.2 | $0.215 |
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-image-1-mini",
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 Image 1 mini
Derived from the odnoga catalog record for this model.
Best for
- High-volume, latency-sensitive calls: classification, extraction, routing, short rewrites and summaries.
- Work that mixes images with text — screenshots, scanned documents, charts, product photos.
Not the right pick when
- 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.
- 03Keep the stable part of your prompt at the front: cached input is billed at $0.2 per 1M tokens instead of $2.
- 04For repeated identical deterministic calls, odnoga answer reuse returns the stored answer and bills no vendor tokens — turn it off for creative output.
- 05Set 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.
What a month costs
1,000 calls a month, 10,000 input tokens and 2,000 output tokens each, at your Free price (vendor cost +7%):
| Input (10M tokens) | $21.51 |
|---|---|
| Output (2M tokens) | $17.20 |
| Your cost per month on Free | $38.71 |
Vendor list cost $36.00 + odnoga margin $2.71 (+7%). Cached input or answer reuse lowers it; odnoga records both numbers per request.
GPT Image 1 mini compared
| Model | Context | Input / 1M | Output / 1M | Capabilities |
|---|---|---|---|---|
| GPT Image 1 mini | — | $2 | $8 | Vision |
| ChatGPT (chat-latest) | 400K | $5 | $30 | Vision, Tools, JSON mode, Streaming |
| GPT Image 1.5 | — | $5 | $10 | Vision |
Questions
- How much does GPT Image 1 mini cost per 1M tokens?
- OpenAI lists $2 / $8 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 Image 1 mini best for?
- High-volume, latency-sensitive calls: classification, extraction, routing, short rewrites and summaries. Work that mixes images with text — screenshots, scanned documents, charts, product photos.
- Can I switch to GPT Image 1 mini without changing my code?
- Yes. odnoga exposes one OpenAI-compatible endpoint, so switching means sending "gpt-image-1-mini" as the model id — or changing it in the managed prompt version, with no application deploy.
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