GPT / OpenAI specialists 18 services

This category centres on the OpenAI ecosystem. Users come from Codex CLI and from a large body of existing code already written against the OpenAI spec. Swapping base_url and the key is usually all it takes, so migration cost is minimal — which is why this is where many people meet their first relay service.

When choosing, distinguish between "Chat Completions compatible" and "Responses API supported". The former is the industry-wide standard and nearly every relay implements it; the latter is OpenAI's newer shape and is what newer tools such as Codex CLI depend on. A service that only implements the former will fail outright with those tools.

The GPT line iterates quickly, and a service's model list tends to lag. A practical signal: if /v1/models returns the newest versions, the service is probably keeping up with its upstream; if it only lists model names from a couple of years ago, it may have gone stale.

Also note how the o-series differs from the GPT line on billing. Reasoning models emit significantly more output tokens, so the same number of calls produces a noticeably larger bill. Do not compare rates alone — estimate against your own token mix.

• Most stable：Highest current uptime. Fewer samples means more distortion, so we state the source (public dataset / our own measurement) and you can check it on the detail page.
• Fastest：Ordered by the home-page response time of the most recent probe. A single measurement is noisy, so treat it as a range rather than an absolute.

After switching base_url to a relay, does my code need changes?

In most cases you only change base_url and api_key; SDK code stays as it is — that is the value of OpenAI-protocol compatibility. But if you rely on newer endpoints (Responses API, Realtime), confirm the service implements them first, or you will get "basic chat works, this one feature 404s".

Are GPT-5 models downgraded on relay services?

It happens, though less often than with the Claude line. The checks are similar to Opus: look at what /v1/models returns, compare answer quality, and see whether response latency matches the tier. Our API probe records the model list, which you can use as a cross-reference.

Why does the same model respond so much faster on one service than another?

Two things dominate: the network path from the service to its upstream, and whether it queues requests. We collect latency daily, so the distribution is visible on the comparison board. Single measurements fluctuate a lot — read a period's median rather than one reading.

Are reasoning models (o-series) a good fit for relay services?

They can be, but watch the billing. A reasoning model's thinking also counts as output tokens and often far exceeds the final answer, so the bill runs well above what a per-conversation estimate suggests. Run a week of real tasks after signing up, then re-evaluate from actual token usage.