> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vlm.run/llms.txt
> Use this file to discover all available pages before exploring further.

# Models

> The engines that answer typed, calibrated decisions

Several engines answer the same questions and return the same answer shapes, in one of two
ways. A **diffusion** read seeds a canvas and denoises it once. A **generative** read
writes the answer template under a grammar. Either way nothing is parsed, so an answer
cannot be off-schema.

| Model                              | Read       | Notes                                                                                 |
| ---------------------------------- | ---------- | ------------------------------------------------------------------------------------- |
| `google/diffusiongemma-26b-a4b-it` | diffusion  | The default engine, and the most peaked distributions.                                |
| `google/gemma-4-26b-a4b-it`        | generative | The sparse sibling of the default: 4B active, so a read costs a fraction of its size. |
| `qwen/qwen3.8-27b`                 | generative | The largest generative engine on this route.                                          |
| `qwen/qwen3.5-0.8b`                | generative | The smallest engine on this route. Decisions are less peaked than the default's.      |

Each also accepts its Hugging Face spelling, for example `Qwen/Qwen3.8-27B`.

<Warning>
  **Generative reads are experimental.** The diffusion engine reads probabilities straight
  off the canvas. A generative engine writes the answer under a grammar, so the shape is
  guaranteed but the calibration is not: on the smaller engines, measured probabilities sit
  much closer to uniform and a confident-looking answer can still be wrong. Treat
  `confidence` from a generative read as a weak signal, validate on your own labelled data
  before you threshold on it, and prefer `google/diffusiongemma-26b-a4b-it` wherever the
  decision matters.
</Warning>

## Naming a model

<Warning>
  **There is no catch-all alias.** Name the model you want. An id the deployment does not
  serve is a `404`, so the TypeSafe SDK's `jev-latest` default has to be overridden. See
  [TypeSafe SDK Compatibility](/gateway/jev-compatibility).
</Warning>

Which engines a deployment serves varies, and a decision reports the id that answered it,
so read the live list rather than hard-coding an id:
[`GET /typesafe/v1/models`](/gateway/api-reference/get-typesafe-models).

## Which one to reach for

| If you need                                    | Reach for                                     |
| ---------------------------------------------- | --------------------------------------------- |
| Calibrated probabilities you will threshold on | `google/diffusiongemma-26b-a4b-it`            |
| The lowest cost per read                       | `qwen/qwen3.5-0.8b`                           |
| A reasoning budget before the answer           | Any generative engine, via `reasoning_effort` |

`reasoning_effort` applies to generative engines only; a diffusion engine rejects it with a
`422`. See [Gateway extensions](/gateway/system-one#gateway-extensions).

## Related

<CardGroup cols={2}>
  <Card title="System One" icon="scale-balanced" href="/gateway/system-one">
    Question types, inputs, confidence, and cost levers.
  </Card>

  <Card title="List Models" icon="table-list" href="/gateway/api-reference/get-typesafe-models">
    The live list for a deployment.
  </Card>
</CardGroup>