Jev Competition

Per WSJ's search excerpt, the three-week-old Jev has set Silicon Valley abuzz with talk of alternatives to large language models and is already sparking copycats. It classifies inputs to predetermined outputs: yes or no, a numerical score, or answers on a list.

Fortune reports Jev went viral among developers, with nearly 13% of Vercel's paid teams trying it within a day of its gateway arrival. Three weeks after Jev's launch, OpenAI rolled out a competing Decisions API built on GPT-6 Luna.

Bloomberg News reports Jev went viral with the promise of a cheaper, faster alternative to large language models, its launch video nearing 40 million views on X. Per the Financial Times, investors have offered funding valuing TypeSafe AI above $10 billion.

INXEN's syndication of Financial Times reporting says TypeSafe, last valued at $200 million, is fielding offers that would value it at $10 billion or more days after launching Jev, with Vercel citing record paid-developer interest.

A community roundup of every public Jev evaluation: TypeSafe's own workflow evals plus at least twenty independent builder benchmarks. The site reports speed medians of 127-422 ms per call leading every LLM compared, while accuracy splits by task.

Blackwell Systems' adversarial technical due diligence on Jev, built only from public material and live API calls. The author reproduces TypeSafe's published addressee numbers within a point and finds a real but noise-type-specific robustness moat.

An independent hands-on evaluation that ran Jev live on 16,379 benchmark requests and reports it is no frontier model but genuinely useful for cheap real-time sub-frontier judgement, with 82.7% MMLU-Pro and 76.5% GPQA Diamond.

A playground operator's audit of every published Jev number: TypeSafe's four-workflow evals, the favourable homepage demo, independent tests and its own calls. It concludes the price and speed are real and the intelligence claim is roughly mid-tier frontier on decision workflows.

Celeris's launch post for Celeris-1-decision, a hosted diffusion language model for typed decisions. It reports 80.0% on the full jev-bench, the lowest Brier scores of five decision APIs tested, a 67 ms median and $0.04 per million input tokens.

AICrier's news-feed summary of Celeris-1 Decision: a multimodal diffusion model for bounded structured decisions that Celeris says reaches 80% on JevBench, 7 points above Jev 1.13.0, with 67 ms median latency.

Interactive comparison of 21 decision models, with direct-provider and OpenRouter routes measured separately on 1,100 public jev-bench items. The charts plot accuracy against benchmark cost, official prices and median latency, with Jev 1.13 and 1.14 measured separately.

Doug Does' benchmark dashboard for JevBench: 534 questions, 48 ranked models, composite scores that already include cost, head-to-head comparison, latency and a Pareto frontier. Jev 1.13.0 tops the table at 74.40.

The desk's index of every Jev measurement it has published, keeping each figure's original caveat: JevBench boards, Vercel gateway reach, Celeris and Artificial Analysis results, and the desk's own Jev Day probes which it says it did not rerun.

Side-by-side tables placing similar Jev tests next to each other, from the JevBench API board to Vercel paid-team reach, with the desk's warning that differing sets, labels and comparators mean the rows are not a ranking.