OpenAI

OpenAI — GPT models plus audio, images and embeddings on one bill. The broadest first-party surface in the category: text, reasoning, realtime audio, image generation, embeddings, moderation and a hosted agent runtime, all under one key. The Responses API is the current front door and carries most of the agentic features. OpenAI's request format is also the category's lingua franca — almost every competitor and router ships a compatibility endpoint that imitates it. Founded 2015. Late-stage private.

We track it in 1 comparison — LLM APIs — so every claim below is a cell in a table you can open and check rather than an impression. Across those rosters it sits against 17 other tools, and what follows is where it visibly separates from them.

Where it wins.

• Effort control — Yes, which only 4 of 18 tools here manage.
• MCP support — Yes, which only 2 of 18 tools here manage.
• $/M cache read — $0.125 /M tok, 2nd of 5.
• Audio in/out — Yes, which only 2 of 18 tools here manage.
• API since — 2020, the best figure of the 18 here.

Each of those is ranked against the whole roster on its category page, not against a hand-picked subset, so a first place here means first of everything we list.

Provenance. 24 of 27 tracked fields carry a value for OpenAI, and 13 of those cite a document you can open. Last verified 2026-01-15. Every figure keeps its own provenance — measured by us, claimed by the vendor, inferred, or community-reported — and we would rather print a dash than a guess.

Its nearest neighbour in our data is Qwen. OpenAI is ahead on API since (2020 against 2023) and Computer use (Partial against No). Qwen takes Trains on your data (Partial against No) and $/M output ($6 /M tok against $10 /M tok). That pattern repeats across the rest of the roster — see OpenAI alternatives for the other rivals, each compared the same way.

Founded

2015

Funding

Late-stage private

Fields we track

24 of 27

Last verified

2026-01-15

The widest surface area in the category, and the only one where audio, images and text share a bill and a mental model. Dated snapshots make routine version management painless — the strongest routine protection here. On notice periods, be careful how much weight you put on the comparison: the retirements we have observed have run shorter than Anthropic's, but neither provider publishes a guaranteed minimum, so both numbers in that column are inference from past behaviour rather than a commitment either vendor has made. Pin, and keep an eye on the deprecations page.

Effort control

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

$/M input

$1.25 /M tok5th of 8

Vendor-claimedverified 2026-01-15source

MCP support

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

$/M output

$10 /M tok5th of 8

Vendor-claimedverified 2026-01-15source

$/M cache read

$0.125 /M tok2nd of 5

Inferredverified 2026-01-15

Audio in/out

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

Context window

272,000 tokens5th of 10

Inferredverified 2026-01-15

API since

20201st of 18

Inferred

OpenAI is the right call in these situations, each one drawn from a field we actually record:

• Multimodal products that need speech and image generation alongside text.
• Teams standardising on one vendor for everything rather than best-of-breed.
• Anyone who wants a dated snapshot they can actually pin.
• Effort control is your binding constraint. OpenAI records Yes, which only 4 of the 18 tools in the LLM APIs roster do. We define that field as a request-level parameter that trades reasoning depth against cost and latency;.
• MCP support is your binding constraint. OpenAI records Yes, which only 2 of the 18 tools in the LLM APIs roster do. We define that field as whether the API itself can connect to Model Context Protocol servers, rather than leaving MCP entirely to your client;.
• $/M cache read is your binding constraint. OpenAI records $0.125 /M tok, 2nd of 5 in the LLM APIs roster.
• Audio in/out is your binding constraint. OpenAI records Yes, which only 2 of the 18 tools in the LLM APIs roster do.

Nothing in our data disqualifies OpenAI on a field we score — it is not bottom of its roster on anything weighted. That does not make it universally correct. The honest failure modes for a tool in this position are commercial rather than technical: pricing that changes under you, a licence that changes under you, or a roadmap that drifts away from your use case. Check the timeline for changes we have logged, and OpenAI alternatives for what teams move to when it stops fitting.

What we don't know. 3 of the 27 fields we track for OpenAI are still blank: TTFT p50, Output tok/s and AU region. Those render as dashes rather than as zeroes or assumptions, because an empty cell and a bad cell are not the same thing and only one of them is honest. If you know any of these figures and can point at a document, tell us.