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OpenAI vs TypeSafe AI
TypeSafe's Jev returns typed decisions with calibrated confidence in about 100ms. It replaces GPT only for decision-shaped calls and usually works alongside it.
By The Subconscious Team · Updated
OpenAI vs TypeSafe AI: key differences
TypeSafe AI was founded by Diogo Almeida, who co-invented RLHF and InstructGPT at OpenAI, and its product is a deliberate departure from GPT-style generation. Jev does not produce text or code. A developer defines the answer space with primitives like Choice, Score and a true-or-false type, and Jev returns a typed answer plus calibrated probabilities and a confidence score. It evaluates every option in one pass, so most calls finish in about 100ms. TypeSafe puts it at roughly 40 to 200x the speed of an LLM on decision-shaped queries, at a fraction of the cost.
That makes Jev a candidate for work many teams now send to a cheap GPT tier like Luna: routing tickets, scoring leads, classifying intent or picking which model gets a prompt. Because outputs always match the schema, Jev cannot return a value outside the options. For anything open-ended, OpenAI remains the tool, from chat to computer use on GPT-6 Astra. Jev is also early access, with text-only input and a new programming model to learn. A common setup would put Jev in front of GPT as the router and guardrail.
What OpenAI and TypeSafe AI do
OpenAI
OpenAI runs the most widely adopted closed-model API. Its September 2026 lineup has GPT-6 Astra at the top for computer use, coding and long agentic runs, priced at $10 in and $50 out per million tokens. Below it sits the GPT-5.6 family: Sol for hard professional work, Terra as the balanced default, and Luna for high-volume jobs at $0.20 in and $1.20 out. All of them carry a 1.05M token context window with up to 128K output.
Example models: GPT-6 Astra, GPT-5.6 Terra
Full OpenAI 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 OpenAI or TypeSafe AI?
OpenAI
Choose OpenAI for
- Open-ended generation, chat and coding
- Computer-use and multi-tool agents
- Teams that want mature, widely adopted SDKs
TypeSafe AI
Choose TypeSafe AI for
- Routing, scoring and intent classification in about 100ms
- Guardrails that escalate when confidence is low
- Choosing which LLM should handle a prompt
OpenAI vs TypeSafe AI at a glance
| Attribute | OpenAI |  TypeSafe AI |
|---|---|---|
| Model access | Closed, plus open gpt-oss | Decision models |
| Flagship models | GPT-6 Astra, GPT-5.6 Sol, Terra, Luna | Jev, jev-1.13 |
| Speed | Fast mode: up to 2.5x at 2x price | ~100ms per call |
| Price | $0.20–$10 in, $1.20–$50 out per 1M | A fraction of an LLM call |
| Customization | N/A | Unknown |
| Deployment | API, Azure OpenAI, Bedrock | Early-access API |
| Long context | 1.05M; 2x input past 272K | Unknown |
Frequently asked questions
What is the difference between OpenAI and TypeSafe AI?
TypeSafe's Jev returns typed decisions with calibrated confidence in about 100ms. It replaces GPT only for decision-shaped calls and usually works alongside it.
When should I choose OpenAI over TypeSafe AI?
Open-ended generation, chat and coding; Computer-use and multi-tool agents; Teams that want mature, widely adopted SDKs.
When should I choose TypeSafe AI over OpenAI?
Routing, scoring and intent classification in about 100ms; Guardrails that escalate when confidence is low; Choosing which LLM should handle a prompt.
Is OpenAI or TypeSafe AI cheaper?
OpenAI: $0.20–$10 in, $1.20–$50 out per 1M. 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.