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Cerebras vs Sail Research

Cerebras sells the fastest tokens; Sail sells the cheapest waits. One serves live users, the other serves agents that can pause for minutes.

By The Subconscious Team · Updated

Cerebras vs Sail Research: key differences

Cerebras and Sail Research sit at the two poles of inference. Cerebras runs GPT-OSS 120B near 3,000 tokens per second on its wafer-scale chip and targets voice, live autocomplete and streaming UIs. Sail packs work into GPUs and prices by patience: a one-minute priority window for 30 to 50% off, a five-minute standard window for 45 to 65% off, and an off-peak flex window for 60 to 80% off. Sail's own profile calls it unsuited to voice, live chat or any interactive UI, which is Cerebras' home ground.

Sail's shared catalog is broader, with Kimi K2.6, GLM-5, GPT-OSS 120B, Qwen 3.6, Gemma 4 and customer LoRA fine-tunes over OpenAI and Anthropic-compatible APIs. Its Sailboxes give background agents persistent compute that can run for hours. Cerebras' own caveat applies here: its speed does little when an agent mostly waits on tools or hidden reasoning. For hours-long code scans or evals, Sail is the rational buy. For a human waiting on each token, Cerebras is.

What Cerebras and Sail Research do

Cerebras

Cerebras builds a single chip the size of a silicon wafer, and its inference cloud is the fastest public host on the models it serves. Its developer table lists GPT-OSS 120B near 3,000 tokens per second, about six times Groq on the same weights, at $0.35 in and $0.75 out per million. The public shared catalog is thin, just GPT-OSS 120B and Gemma 4 31B as of August 2026. More model families live on dedicated endpoints and through partners like OpenRouter, Hugging Face, Vercel and AWS Marketplace.

Example models: GPT-OSS 120B, Gemma 4 31B

Full Cerebras profile

Sail Research

Sail Research sells throughput over latency. Founders Neil Movva and Samir Menon built a serving stack that packs as much work as possible into every GPU, and customers state how long they can wait through completion windows. The priority window targets about a one-minute turn for roughly 30 to 50% off the immediate asap price. The default standard window targets about five minutes for 45 to 65% off. The flex window runs off-peak for 60 to 80% off.

Example models: Kimi K2.6, GLM-5

Full Sail Research profile

Should you choose Cerebras or Sail Research?

Cerebras

Choose Cerebras for

  • Voice agents and live chat
  • Streaming code autocomplete
  • Any interactive UI where generation time is the wait

Sail Research

Choose Sail Research for

  • Background agents running for hours unattended
  • Evals and offline research at 30 to 80% off
  • LoRA fine-tunes on open models without real-time needs

Cerebras vs Sail Research at a glance

AttributeCerebrasSail Research
Model accessOpen weightsOpen weights
Flagship modelsGPT-OSS 120B, Gemma 4 31BKimi K2.6, GLM-5, GPT-OSS 120B
Speed~3,000 tok/s on GPT-OSS 120BMinutes per turn by design
Price$0.35 in, $0.75 out (GPT-OSS 120B)30–80% off by completion window
CustomizationUnknownCustomer LoRA fine-tunes
DeploymentShared API, dedicated, partnersAPI plus Sailboxes
Long contextUnknownVaries by model

Frequently asked questions

What is the difference between Cerebras and Sail Research?

Cerebras sells the fastest tokens; Sail sells the cheapest waits. One serves live users, the other serves agents that can pause for minutes.

When should I choose Cerebras over Sail Research?

Voice agents and live chat; Streaming code autocomplete; Any interactive UI where generation time is the wait.

When should I choose Sail Research over Cerebras?

Background agents running for hours unattended; Evals and offline research at 30 to 80% off; LoRA fine-tunes on open models without real-time needs.

Is Cerebras or Sail Research cheaper?

Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). Sail Research: 30–80% off by completion window. The cheaper choice depends on the model and workload.

Related comparisons

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If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.