TypeSafe AI’s new models work with machines, not humans
TypeSafe AI's Jev trades verbose reasoning for machine-native decisions: 70-500ms latency, $0.042 per million input tokens, built for agentic workflows that would bankrupt you on general-purpose LLMs.
Why it matters
A specialized model class (decision-return vs. text-generation) is emerging to solve a real agent-economics problem: general-purpose LLMs are too slow and expensive for the millions of bounded decisions that agentic workflows demand. This represents a measurable shift in how enterprises will architect multi-model stacks.
The key facts
9 to knowTypeSafe AI founded by Diogo Almeida, OpenAI researcher and RLHF co-inventor
Jev model designed to return defined decisions with probabilities instead of free-form text
Latency: 70-500ms vs. several seconds for general-purpose LLMs tested
Pricing: $0.042 per million input tokens; output tokens 'too cheap to meter'
Use cases: routing, approval decisions, tool invocation, task handoff
Elimination of sequential text-token generation as core architectural difference
Tradeoffs: requires pre-specification of questions, outputs, thresholds; probability calibration and auditability concerns for regulated industries
Current deployment: hosted service, single region, waitlist model
Analyst consensus: Jev likely complements rather than replaces general-purpose LLMs in mixed workflows
Go to the source
CIOcio.com
Publisher excerpt: Today’s large language models are verbose, even if they’re being asked to recommend a simple decision, driving up usage costs through the sheer volume of tokens they consume or generate. Enterprises looking to incorporate AI into automated workflows will want something less verbose — both because…