Cerebras (gateway) runs models on wafer-scale engines, competing with Groq on raw inference speed, and its catalog inside Atlas is small and curated: GPT-OSS 120B, GLM-4.7, and Gemma 4. Context is 131K tokens (131,072), and pricing is GPT-OSS 120B $0.35 / $0.75; GLM-4.7 $2.25 / $2.75; Gemma 4 31B $0.99 / $1.49 per Mtok. Cerebras cannot be your only Atlas provider, because three language models will not cover every task.
What is Cerebras best at inside Atlas?
Cerebras (gateway) is best inside Atlas at keeping the agent loop from feeling like a batch job. Wafer-scale inference produces some of the fastest token rates available, and Cerebras serves GPT-OSS 120B at $0.35 / $0.75 per Mtok with a 131,072 token context window.
An Atlas turn is a chain of generations, not one. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, then computes a unified diff for every file edit and surfaces it for approval before writing. Each of those steps waits on tokens. Cerebras (gateway) attacks exactly that wait by running the model on wafer-scale engines, and the practical result is that a plan appears while you are still reading the prompt you sent. GPT-OSS 120B on Cerebras at $0.35 / $0.75 per Mtok is the best speed-to-price point in the catalog, which is why it is the documented default choice.
How much does Cerebras cost per million tokens?
Cerebras (gateway) prices GPT-OSS 120B at $0.35 / $0.75, GLM-4.7 at $2.25 / $2.75, and Gemma 4 31B at $0.99 / $1.49 per Mtok. GLM-4.7 at $2.25 / $2.75 has an unusually flat input-to-output ratio, so output-heavy work does not get punished.
The GLM-4.7 pricing on Cerebras (gateway) is worth understanding because it is shaped differently from every other provider. Most models charge three to five times more for output than input. GLM-4.7 on Cerebras is $2.25 in and $2.75 out, barely a premium. Inside Atlas that matters because large diffs are output. Atlas computes a unified diff for every file edit, and a big refactor generates a lot of them. On a conventional pricing curve, that refactor is expensive. At $2.25 / $2.75, generating a thousand lines of diff costs almost what reading a thousand lines of source costs.
How small is the Cerebras model catalog?
Cerebras (gateway) serves only three language models: GPT-OSS 120B, GLM-4.7, and Gemma 4. That small curated catalog means less time spent choosing and more time working, but it also means Cerebras cannot be your only Atlas provider in 2026.
Three models is a real constraint and it cuts both ways. The upside of the Cerebras (gateway) catalog is that model selection is not a research project: run `atlas models cerebras`, see GPT-OSS 120B, GLM-4.7, and Gemma 4, and pick the one that matches the job. The downside is coverage. No Claude, no GPT-5, no long-tail specialist model. The documented Atlas guidance is to keep a second provider configured, since three models will not cover every task. Atlas lets you switch the active model and provider on the fly with favorites and recents, so the second provider costs nothing until you need it.
When should you pick a different provider instead of Cerebras?
Pick a different provider than Cerebras (gateway) when the task needs a model outside GPT-OSS 120B, GLM-4.7, and Gemma 4, or when an Atlas session needs more than 131,072 tokens of context. Cerebras also charges a real premium on GLM-4.7 at $2.25 / $2.75 versus $0.60 / $2.20 direct.
The rule for Cerebras (gateway) inside Atlas: use it as the speed provider, not the only provider. Star gpt-oss-120b in `/models` for the best speed-to-price point at $0.35 / $0.75 per Mtok, keep GLM-4.7 for output-heavy diff work where the flat $2.25 / $2.75 ratio pays, and register a frontier provider alongside for everything the three-model catalog cannot reach. Context on Cerebras is 131K tokens (131,072), so when a session needs a million-token window, another provider handles it. Export `CEREBRAS_API_KEY` once and Atlas loads it through `@ai-sdk/cerebras`.
Setup
- 01Export `CEREBRAS_API_KEY=...`. Atlas loads it through `@ai-sdk/cerebras`.
- 02Confirm the catalog: `atlas models cerebras`.
- 03Select gpt-oss-120b from `/models` for the best speed-to-price point.
- 04Keep a second provider configured, since three models will not cover every task.
Frequently asked questions
- how to set up cerebras with atlas
- Export `CEREBRAS_API_KEY=...` and Atlas loads it through `@ai-sdk/cerebras`. Confirm the catalog with `atlas models cerebras`, then select gpt-oss-120b from `/models` for the best speed-to-price point.
- how much does cerebras cost per million tokens
- On Cerebras, GPT-OSS 120B is $0.35 / $0.75, GLM-4.7 is $2.25 / $2.75, and Gemma 4 31B is $0.99 / $1.49 per Mtok. GLM-4.7's flat ratio suits output-heavy work like large diffs.
- what models does cerebras offer
- Cerebras serves three language models: GPT-OSS 120B, GLM-4.7, and Gemma 4. The small curated catalog means less time choosing, but it also means Cerebras cannot be your only Atlas provider.
- what is cerebras's context window
- Cerebras serves 131K tokens (131,072) of context. That applies across its three-model catalog, so an Atlas session needing a million-token window has to use a different provider.
- cerebras vs groq for a coding agent
- Cerebras runs models on wafer-scale engines and competes with Groq on raw inference speed. Cerebras serves GPT-OSS 120B at $0.35 / $0.75 per Mtok with a curated three-model catalog, and both cap context at 131,072 tokens.
- is glm-4.7 cheaper on cerebras or z.ai
- GLM-4.7 costs $2.25 / $2.75 per Mtok on Cerebras versus $0.60 / $2.20 direct from Z.ai, so Cerebras is a real premium. You are paying that premium for wafer-scale inference speed, not for the model.
- why is cerebras fast
- Cerebras runs models on wafer-scale engines rather than conventional GPUs. Wafer-scale inference produces some of the fastest token rates available, which is what keeps an Atlas agent loop from feeling like a batch job.
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