Jev

OpenRouter’s Armor beta uses Jev to check tool calls against rules and user intent before execution. Agents can enable it as a plugin, including for Claude Code auto permissions with other models.

An independent DeepSWE test found Jev Router roughly matched GPT-6 Astra low on results but cost slightly more and took nearly five times as long. Practitioners also argue that request-level routing misses repository context and cache costs.

OpenRouter launched Jev Router, which selects a model and reasoning effort per turn while weighing the cost of losing cached context. OpenRouter reports 237 of 423 tasks solved; invalid Jev outputs or timeouts fail without fallback.

Developers report using Jev to score inbox content, classify invoices, and split documents into categories. DocJev’s author claims 6x lower latency than GPT-5.6 Luna at equivalent accuracy for document classification and splitting.

Practitioners are testing Jev as a fast semantic verifier for online evaluations and reinforcement-learning trajectories. A field analysis found it useful for progress and completion estimates, but warned against using it to detect harmful

Teknium’s public evaluation says a Jev compaction strategy removes tool calls and eventually stops yielding savings. Repeated compaction can invalidate caches and increase total token costs, according to the critique.

TypeSafe says jev-use generates candidate actions from browser state, has Jev select one, then validates and executes it through Cua Driver. Its CUA-S1-FORMS model scored forms locally in 7–9 ms, excluding execution.

Stagehand says adding Jev to its browser primitives cut median Act latency from 1.97 seconds to 0.46 seconds. Jev handles page-level choices and falls back to an LLM when uncertain.

Jev returns predefined choices, scores, and probabilities instead of free-form text for bounded software decisions. TypeSafe claims roughly 150 ms responses and lower inference costs for those tasks.