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Cerebras vs Nebius
Nebius is a European cloud with 60+ managed models and raw GPUs. Cerebras is a speed specialist with two shared models and a partner network.
By The Subconscious Team · Updated
Cerebras vs Nebius: key differences
Nebius covers a lot of ground. It rents NVIDIA GPUs from H100s at $2.15 an hour preemptible up to GB300 racks, serves 60+ open models through Token Factory from $0.06 per million input tokens, and offers dedicated endpoints with a 99.9% SLA and EU or US placement. Artificial Analysis has measured it among the top hosts on raw throughput. Cerebras covers one thing: the fastest tokens, with GPT-OSS 120B near 3,000 tokens per second on its wafer-scale chip at $0.35 in and $0.75 out. Its shared API holds two models, with more on dedicated endpoints and partners.
Nebius wins on breadth, data residency and the ability to grow from tokens into training on one account, including serving uploaded fine-tunes at the same token price. It asks for a $25 minimum first payment and offers no free trial. Cerebras wins when generation speed is the bottleneck, as in voice or live code autocomplete, and adds a wafer-scale path to OpenAI's Ultrafast GPT-5.6 Sol preview. European enterprises keeping AI in-region should look at Nebius first.
What Cerebras and Nebius 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 profileNebius
Nebius is an Amsterdam-headquartered AI cloud and the strongest European alternative to the US hyperscalers. It sells raw NVIDIA GPU compute, from H100s at $2.15 an hour preemptible up to GB300 NVL72 racks, and it has begun adding Vera Rubin. Hyperscale buyers back it: a Microsoft capacity deal worth about $17.4B in September 2025, then a Meta agreement worth up to about $27B in March 2026.
Example models: DeepSeek V3, GPT-OSS
Full Nebius profileShould you choose Cerebras or Nebius?
Cerebras
Choose Cerebras for
- The fastest available output on GPT-OSS 120B
- Voice and streaming experiences
- Teams following OpenAI's Ultrafast preview on Cerebras
Nebius
Choose Nebius for
- European workloads that need EU placement
- Serving uploaded fine-tunes on dedicated endpoints
- Moving from managed tokens to raw GPU training on one account
Cerebras vs Nebius at a glance
| Attribute | Cerebras | Nebius |
|---|---|---|
| Model access | Open weights | Open weights, 60+ models |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | DeepSeek, Qwen, GLM, Kimi, GPT-OSS |
| Speed | ~3,000 tok/s on GPT-OSS 120B | Among top hosts on throughput |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | From $0.06 per 1M input |
| Customization | Unknown | Serve uploaded fine-tunes |
| Deployment | Shared API, dedicated, partners | Token Factory, dedicated, raw GPUs |
| Long context | Unknown | Varies by model |
Frequently asked questions
What is the difference between Cerebras and Nebius?
Nebius is a European cloud with 60+ managed models and raw GPUs. Cerebras is a speed specialist with two shared models and a partner network.
When should I choose Cerebras over Nebius?
The fastest available output on GPT-OSS 120B; Voice and streaming experiences; Teams following OpenAI's Ultrafast preview on Cerebras.
When should I choose Nebius over Cerebras?
European workloads that need EU placement; Serving uploaded fine-tunes on dedicated endpoints; Moving from managed tokens to raw GPU training on one account.
Is Cerebras or Nebius cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). Nebius: From $0.06 per 1M input. 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.