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Cerebras vs xAI
xAI sells closed Grok models with live X data. Cerebras sells open-weight speed on a wafer-scale chip. Different purchases, rarely either-or.
By The Subconscious Team · Updated
Cerebras vs xAI: key differences
xAI is a closed lab. Its Grok 4.6 flagship costs $2 in and $6 out with a 500K context window, older Grok 4.20 and 4.3 keep 1M context at $1.25 in and $2.50 out, and server-side X Search pulls live posts from X. Cerebras builds hardware and serves open weights on it, with GPT-OSS 120B near 3,000 tokens per second at $0.35 in and $0.75 out. Its shared catalog is only two models, but it is also the only wafer-scale path to a closed frontier model, through OpenAI's Ultrafast preview of GPT-5.6 Sol at up to 750 output tokens per second.
The decision usually turns on what the model has to know and how fast it must answer. Social listening, news and market agents that need fresh data fit xAI, which also ships separate image, video and audio APIs. Watch prompt size: past 200K tokens, xAI bills the whole request at double. Voice, streaming UIs and long generated outputs on open weights fit Cerebras, though its speed does little when an agent mostly waits on tools or hidden reasoning. Cerebras also reaches buyers through AWS Marketplace, and xAI lists fewer cloud-marketplace options than OpenAI or Anthropic.
What Cerebras and xAI 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 profilexAI
xAI sells the Grok models through its own API. Grok 4.6 is the current flagship and xAI tells developers to use it for everything outside audio, image and video, code included. It has a 500K context window and costs $2 in and $6 out per million tokens under 200K prompt tokens. Older Grok 4.20 and 4.3 models keep a 1M window at $1.25 in and $2.50 out, which is aggressive for that capability class.
Example models: Grok 4.6, Grok 4.20
Full xAI profileShould you choose Cerebras or xAI?
Cerebras
Choose Cerebras for
- Streaming UIs and voice where output speed matters most
- Open-weight GPT-OSS 120B at low per-token cost
- Buying through partners like AWS Marketplace or OpenRouter
xAI
Choose xAI for
- Agents that need live X posts and web search
- Closed reasoning models with 500K to 1M context
- First-party image, video and audio generation next to text
Cerebras vs xAI at a glance
| Attribute | Cerebras | xAI |
|---|---|---|
| Model access | Open weights | Closed |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | Grok 4.6, Grok 4.20, grok-build |
| Speed | ~3,000 tok/s on GPT-OSS 120B | ~54 tok/s on Grok 4.6 |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | $2 in, $6 out (Grok 4.6); 2x past 200K |
| Customization | Unknown | Unknown |
| Deployment | Shared API, dedicated, partners | First-party API |
| Long context | Unknown | 500K (4.6), 1M (4.20, 4.3) |
Frequently asked questions
What is the difference between Cerebras and xAI?
xAI sells closed Grok models with live X data. Cerebras sells open-weight speed on a wafer-scale chip. Different purchases, rarely either-or.
When should I choose Cerebras over xAI?
Streaming UIs and voice where output speed matters most; Open-weight GPT-OSS 120B at low per-token cost; Buying through partners like AWS Marketplace or OpenRouter.
When should I choose xAI over Cerebras?
Agents that need live X posts and web search; Closed reasoning models with 500K to 1M context; First-party image, video and audio generation next to text.
Is Cerebras or xAI cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). xAI: $2 in, $6 out (Grok 4.6); 2x past 200K. The cheaper choice depends on the model and workload.
Related comparisons
Running long-horizon agents?
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.