# TypeSafe Jev Played Chess — And Landed Next to Reasoning Models

> Source: <https://dev.to/maximsaplin/typesafe-jev-played-chess-and-landed-next-to-reasoning-models-28ga>
> Published: 2026-09-17 17:13:09+00:00

TypeSafe's [Jev](https://docs.typesafe.ai/introduction) is an odd one. It isn't a chat model - you send a **state** plus typed **questions** (Choice / Score / Noul) and get labels with probabilities — like a classifier, except the input can be free text and you choose the labels each time.

I've been running models through [LLM Chess](https://maxim-saplin.github.io/llm_chess/) for a while. LLMs play in an agentic setting with a multi-turn protocol: `get_current_board`, `get_legal_moves`, `make_move <uci>`. Jev doesn't do that. Can you still put it on the same leaderboard?

Apparently yes — if you talk to it the way it expects.

TypeSafe is openly [anti-benchmaxxing](https://typesafe.ai/blog/antibenchmaxxing). In the [System One / Jev launch post](https://typesafe.ai/blog/introducing-system-one-models-and-jev) they say existing LLM benchmarks don't really apply here, and they skip the usual public-eval scoreboard putting it's model next to other models. They prefer dated internal snapshots instead.

I put Jev on [LLM Chess](https://maxim-saplin.github.io/llm_chess/) anyway — same harness, same opponents as the chat models.

It reminded me of talking to Stephen Hawking: smart, but no free speech. Answers do not come out in a free from

Outside chess I tried a silly next-character loop — Choice over `a–z` plus `<eos>`, one API call per letter. Asked to spell *blue* or finish *hel…*, it spat out almost-words (`bue`, `helhoh`). Started from `pari…` and it finished `paris`. Generating from scratch was weak; finishing a partial worked better. A fixed list of legal chess moves fits this model much better than spelling English letter by letter.

Each ply:

`{ fen, side_to_move }`
Jev returns a UCI string. It turns into `make_move e7e5` — same action chat models send to the eval runner, on the outside seems almost like normal eval interaction.

Pricing: **$0.042 / 1M input tokens**, output free.

On the current leaderboard `jev-latest` is around **#59**, Elo **~243**, sitting next to `qwen3.6-27b` and `o4-mini-medium`:

| # | Player | Elo | Game Duration | Tokens | Cost/Game | Time/Game | Games | 
|---|---|---|---|---|---|---|---|
| 58 | qwen3.6-27b@q4_k_s | 270.0±148.6 | 100% | 5956 | $1.46 | 7.54h | 27 | 
| **59** | **jev-latest** | **242.9±117.5** | **100%** | **119** | **$0.0015** | **35.6s** | **80** | 
| 60 | o4-mini-medium | 240.3±64.3 | 100% | 2156 | $1.11 | 41.5m | 199 | 

Dragon (white) vs Jev (black), 10 games per level:

| Dragon | Dragon wins | Draws | Jev wins | 
|---|---|---|---|
| L1 | 5 | 5 | 0 | 
| L2 | 5 | 5 | 0 | 
| L3 | 5 | 5 | 0 | 

**50% draws** at L1, L2, and L3. Flat. With most chat models, stronger Dragon usually means fewer draws — the engine finishes more games. Here the draw rate doesn't drop across three levels. That helps the Elo.

TypeSafe calls these System One models: fast decisions, no deep thinking.

Chess still needs real choices — what to capture, when to trade, when to aim for a draw. Jev has no chat, costs fractions of a cent per game, and still lands near mid-pack reasoning models on Elo. So the skill isn't "writing about chess." It's picking a move from a list.

TypeSafe doesn't want a public leaderboard as their proof. This is one outside eval.

`bue` / `helhoh`).
Leaderboard: [LLM Chess](https://maxim-saplin.github.io/llm_chess/)

Harness: [maxim-saplin/llm_chess](https://github.com/maxim-saplin/llm_chess) (`feat/typesafe-jev-player` / TypeSafe Jev player)