Jev VS Fable 5.1 VS GPT-6 Astra: chess typesafe's new Jev
Played Jev V13 against Fable 5.1 and GPT-6 Astra in 5+0 blitz, where Jev answered each move in about 2.6 seconds as one API call.
The original post
Jev VS Fable 5.1 VS GPT-6 Astra: chess
typesafe's new Jev V13 isn't an LLM. it doesn't chat, doesn't explain, doesn't write code — it only makes decisions. so we made it play blitz against frontier LLMs.
the test: 5+0 blitz. every move is one API call.
Jev V13 vs Fable 5.1:
• fable outplayed it. by move 29 it was +16 in material and even promoted a second queen
• but it kept burning 6-15 seconds per move on analysis. jev answered in ~2.6s
• so fable didn't have enough time and lost
Jev V13 vs Astra:
• astra didn't bother winning on material. it mated jev in 18 moves. Qe1#, with 2:27 to spare
Quoted post
@CompleteSkeptic34.6M viewssourceAfter co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
Engagement when collected
Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.
Where this case fits
Filed under games & playable demos, coding & developer tools. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Other posts in the same family are on the games & playable demos page.
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Last updated: 2026-09-22 · sources & corrections · every card links to its author's original post