Generating levels in real time with the Jev model
Hugo -
On September 15, 2026, TypeSafe introduced Jev, a model designed to return structured outputs with low latency and low cost. "Eh but that's just a classifier". Ok; but can it generate a platformer level in real time? Let's see!
Why Jev looks promising for games
Unlike text-gen. models such as GPT or Fable, Jev is designed to return structured decisions. In short, it's a zero-shot classifier: it gives you picks with probabilities attached rather than raw text.
LLM vs Jev
Task: choose platform widths and gaps.
LLM
Terrain described in words
Jev
Terrain returned as choices
Now, there are two major bottlenecks to using AI at runtime in games:
- Latency. In most cases, you can't afford to wait five minutes for a model to think.
- Cost. Some will disagree, but I think current LLM pricing makes them pointless for games. It makes no sense to me to pay for expensive API calls if I talk more to the tavern keeper.
Jev promises to help with both: sub-second responses at $0.042 per million input tokens, with free output tokens. Source
Ok. Let's test the claims by fire.
Preparing a test game
For this experiment, I wanted a runner game prototype in a neon-night style. Serious things here, we'll use Sprite Fusion for pixel art generation, PhaserJS, and Codex with Astra.
Ninja Runner assets
You
Use the Sprite Fusion API to create a neon-night ninja sprite + animations for a runner game.
Codex
I created the sprites and animations, and added movement and collisions.
Generating levels with Jev
Feeding the game state & context
First, we take a snapshot of the game state: player position and velocity, current terrain blocks, dash state, etc. We send that in the request to Jev alongside some example terrain layouts.
{"x":158.86,"y":86.87,"vx":2.27,"vy":-2.34,"grounded":false,"dash_ready":true}Jev request
The task we give Jev is fairly simple: given the current game state, how would you fill the next slice of terrain? We ask about widths, gaps, heights and surface types through several choice questions, all sent in one API call. Here are the options we allow:
Options we allow
- Surface type
- Solid roof or one-way ledge
- Width
- 2, 3 or 5 blocks
- Gap before it
- 0, 1 or 2 blocks
- Height
- Rows 4–9
Choices Jev made
| Surface | Type | Width | Gap before | Height |
|---|---|---|---|---|
| 1 | Ledge | 2 blocks | None | Row 5 |
| 2 | Ledge | 3 blocks | 1 block | Row 6 |
| 3 | Ledge | 5 blocks | 2 blocks | Row 5 |
| 4 | Solid roof | 2 blocks | None | Row 4 |
Terrain built from those choices
My code then places the chosen blocks and gaps.
So, costs and latency ?
- Requests (in this demo)
- 5
- Jev API latency
- 319–375 ms
- Est. average cost / request
- $0.00057
- Est. cost / demo
- $0.00286
Well, it's not bad at all. Jev generates the terrain fast enough to keep the runner moving, at a very low cost. It's
not sub-100ms latency nor free but interersting enough to pay attention for games.
I've played the demo for longer, and the latency stays stable. Level generation keeps working fine.
Conclusion
Promising! There are plenty of ways to generate levels using handwritten rules and heuristics, I know. But still, cheaper and faster structured output models opens up a whole range of ideas and experiments for games. I'm gonna share more soon.
Sources and acknowledgments
- TypeSafe: Introducing System One models and Jev.
- Jev docs: state, choices, parallel questions, model limitations and pricing.
- Sprite Fusion: pixel art assets and animations.
