Cerebras

Cerebras — Wafer-scale inference — the fastest tokens per second available. Cerebras serves open-weight models from wafer-scale engines and consistently posts the highest single-stream throughput measured in this category, often several thousand tokens per second on models where GPU hosts manage a few hundred. Like Groq it is a latency instrument rather than a general platform: a short model list, and an architecture decision you make deliberately. 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.

• Image input — No. Anthropic records Yes.
• 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 Cerebras, 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. Cerebras 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 Cerebras 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

Consistently the fastest single-stream generation you can buy, by a margin that is qualitative rather than incremental — reasoning models that take half a minute elsewhere return in a couple of seconds. The catch is the same as Groq's: a short menu, no bring-your-own-weights, and no continuity guarantees at all.

Image input

○No16th of 18

Inferredverified 2026-01-15

Output tok/s

2,000 tok/s1st of 2

Community-reportedverified 2026-01-15

API since

202416th of 18

Inferred

TTFT p50

200 ms1st 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

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

• Reasoning models where thinking-token latency is the bottleneck.
• Interactive tools that feel broken at 50 tokens per second.
• Benchmarking how much of your UX problem is actually latency.

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

• Image input. Cerebras 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.
• API since. Cerebras 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. Cerebras 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 Cerebras 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.