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Cerebras vs TypeSafe AI
TypeSafe AI's Jev returns typed decisions in about 100ms. Cerebras returns generated text at about 3,000 tokens per second. Different outputs, both built for speed.
By The Subconscious Team · Updated
Cerebras vs TypeSafe AI: key differences
Both companies attack latency, but they return different things. Cerebras speeds up generation, serving GPT-OSS 120B near 3,000 tokens per second so long answers stream fast. TypeSafe AI skips generation. Its Jev model takes an answer space defined with primitives like Choice and Score, evaluates every option in one pass, and returns a typed answer with calibrated confidence, usually in about 100ms. TypeSafe says that is roughly 40 to 200x faster than an LLM on decision-shaped queries. Jev cannot write text or code.
In practice they sit in different parts of the same system. Jev handles routing, intent classification, jailbreak detection and tool-call grading, then passes real generation to an LLM host like Cerebras. For a voice agent, that means a fast decision on what to do followed by fast generation of what to say. Jev is in early access and text-only, with a new programming model to learn. Cerebras is in production, with a two-model shared catalog.
What Cerebras and TypeSafe AI 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
TypeSafe AI
TypeSafe AI builds decision models instead of text generators. Founder Diogo Almeida co-invented RLHF and InstructGPT at OpenAI and later worked at Google Brain. After two years in stealth the company released its first System One Model, Jev, in early access. The name nods to Kahneman's fast System 1 thinking, and the model is built for machines to call, not people to chat with.
Example models: Jev, jev-1.13
Full TypeSafe AI profileShould you choose Cerebras or TypeSafe AI?
Cerebras
Choose Cerebras for
- Fast text generation for voice and streaming UIs
- Long outputs on GPT-OSS 120B
- The generation step after a router decides
TypeSafe AI
Choose TypeSafe AI for
- Routing and intent classification in about 100ms
- Guardrails with calibrated confidence and escalation
- Schema-bound decisions that cannot fall outside the options
Cerebras vs TypeSafe AI at a glance
| Attribute | Cerebras |  TypeSafe AI |
|---|---|---|
| Model access | Open weights | Decision models |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | Jev, jev-1.13 |
| Speed | ~3,000 tok/s on GPT-OSS 120B | ~100ms per call |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | A fraction of an LLM call |
| Customization | Unknown | Unknown |
| Deployment | Shared API, dedicated, partners | Early-access API |
| Long context | Unknown | Unknown |
Frequently asked questions
What is the difference between Cerebras and TypeSafe AI?
TypeSafe AI's Jev returns typed decisions in about 100ms. Cerebras returns generated text at about 3,000 tokens per second. Different outputs, both built for speed.
When should I choose Cerebras over TypeSafe AI?
Fast text generation for voice and streaming UIs; Long outputs on GPT-OSS 120B; The generation step after a router decides.
When should I choose TypeSafe AI over Cerebras?
Routing and intent classification in about 100ms; Guardrails with calibrated confidence and escalation; Schema-bound decisions that cannot fall outside the options.
Is Cerebras or TypeSafe AI cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). TypeSafe AI: A fraction of an LLM call. 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.