Groq

Groq — Custom LPU silicon serving open-weight models at extreme speed. Groq runs a curated set of open-weight models on its own language processing units, delivering token rates that general-purpose GPU hosts do not approach. The API is OpenAI-compatible and cheap. The constraint is the menu: you get the models Groq has chosen to deploy, models rotate off with limited notice, and there is no path to bring your own weights. Founded 2016. 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 gives ground.

• API since — 2024, 16th of 18. OpenAI records 2020.
• Effort control — No. Anthropic records Yes.

None of these disqualify it on their own. They are the fields to check against your own requirements before you commit, because they are the ones where a competitor genuinely does better.

Provenance. 18 of 27 tracked fields carry a value for Groq, and 2 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. Groq is ahead on Open weights (Yes against Partial). Qwen takes Trains on your data (Partial against No) and Effort control (Partial against No). That pattern repeats across the rest of the roster — see Groq alternatives for the other rivals, each compared the same way.

Founded

2016

Funding

Late-stage private

Fields we track

18 of 27

Last verified

2026-01-15

When the response appearing instantly is the product, Groq changes what you can build — voice, live search and inline completion feel different at these token rates. It is not a general-purpose platform: the menu is short, models rotate off with little warning, and there is no path to bring your own weights.

TTFT p50

250 ms2nd of 2

Community-reportedverified 2026-01-15

API since

202416th of 18

Inferred

Output tok/s

400 tok/s2nd of 2

Community-reportedverified 2026-01-15

Trains on your data

○No7th of 18

Inferredverified 2026-01-15

Effort control

○No12th of 18

Inferredverified 2026-01-15

OpenAI-compat API

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

MCP support

○No6th of 18

Inferredverified 2026-01-15

Open weights

●Yes1st of 18

Inferredverified 2026-01-15

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

• Voice agents and any interface where perceived latency is the feature.
• High-volume cheap inference on well-known open-weight models.
• Fast transcription alongside text generation on one key.

Reach for something else when any of the following is a requirement rather than a nice-to-have:

• API since. Groq records 2024, 16th of 18 in the LLM APIs roster. OpenAI records 2020 on the same field. If that is a hard requirement rather than a preference, start elsewhere.
• Effort control. Groq records No on the LLM APIs table. Anthropic records Yes on the same field. If that is a hard requirement rather than a preference, start elsewhere.

We publish this block because a comparison that only lists what a tool is good at is marketing. Every figure above sits on the same page as its source, and the field definitions are on the category tables if you want to check how we measured them.

What we don't know. 9 of the 27 fields we track for Groq are still blank: Context window, Max output, $/M input, $/M output, $/M cache read, Batch discount, Cache TTL and Notice period, and 1 more. 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.