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Relace vs TypeSafe AI
Two specialist tools for agent builders. Relace's small models apply edits and search code; TypeSafe AI's Jev makes typed decisions with calibrated confidence. Different steps of the same harness.
By The Subconscious Team · Updated
Relace vs TypeSafe AI: key differences
Relace and TypeSafe AI share a thesis: small specialized models beat frontier LLMs on narrow utility tasks. They apply it to different tasks. Relace works on code. relace-apply-3 merges a lazy edit snippet into a file at about 10,000 tokens per second with 128K tokens of input and output, agentic search answers codebase questions in seconds, and a compaction model runs at 50,000 tokens per second. TypeSafe AI's Jev works on decisions. Developers define answer types like Choice, Score or true-or-false, and Jev returns a typed answer with calibrated confidence in about 100ms.
Neither generates open-ended text, and neither replaces the other. In a coding agent, Jev could route a request, grade a tool call or detect a jailbreak, while Relace applies edits and retrieves code for the main model. Relace is further along, with hosted and self-hosted options and a listing on OpenRouter. Jev is in early access with text-only input and a new programming model to learn. Coding tools start with Relace. Workflows full of if-statements and routing, coding or not, start with Jev.
What Relace and TypeSafe AI do
Relace
Relace trains small, fast models that act as tools for coding agents. Its best-known product is Instant Apply: a frontier model writes a lazy edit snippet, and relace-apply-3 merges it into the original file at about 10,000 tokens per second with 128K tokens of input and output. Relace says this runs over 3x faster and cheaper than having the big model rewrite the file. It exposes both a REST endpoint and an OpenAI-compatible one, and the model is also listed on OpenRouter.
Example models: relace-apply-3, Relace agentic search
Full Relace 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 Relace or TypeSafe AI?
Relace
Choose Relace for
- Applying model edits to files at about 10,000 tokens per second
- Fast search and compaction over large repos
- Self-hosted deployment for in-house code
TypeSafe AI
Choose TypeSafe AI for
- Routing and classification with calibrated confidence
- Guardrails and tool-call grading in agent harnesses
- Decision steps that must return in about 100ms
Relace vs TypeSafe AI at a glance
| Attribute | Relace |  TypeSafe AI |
|---|---|---|
| Model access | Specialist models | Decision models |
| Flagship models | relace-apply-3, agentic search | Jev, jev-1.13 |
| Speed | ~10,000 tok/s apply | ~100ms per call |
| Price | 3x+ cheaper than full rewrites | A fraction of an LLM call |
| Customization | Unknown | Unknown |
| Deployment | Hosted API or self-hosted | Early-access API |
| Long context | 128K max | Unknown |
Frequently asked questions
What is the difference between Relace and TypeSafe AI?
Two specialist tools for agent builders. Relace's small models apply edits and search code; TypeSafe AI's Jev makes typed decisions with calibrated confidence. Different steps of the same harness.
When should I choose Relace over TypeSafe AI?
Applying model edits to files at about 10,000 tokens per second; Fast search and compaction over large repos; Self-hosted deployment for in-house code.
When should I choose TypeSafe AI over Relace?
Routing and classification with calibrated confidence; Guardrails and tool-call grading in agent harnesses; Decision steps that must return in about 100ms.
Is Relace or TypeSafe AI cheaper?
Relace: 3x+ cheaper than full rewrites. 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.