# Models

> Reference for GET /v1/models — the models in Mortar's catalog with each one's served context window, engine, GPU, availability and sample per-token prices.

The model list tells a client which IDs it can pass as `model` and how many tokens each one can take. It follows the OpenAI list format, with Mortar's own details in a `mortar` object on each entry.

## List models

`GET /v1/models`

Lists every model in the catalog, including models that aren't deployed yet. Needs an API key.

### Example

```bash title="Request"
curl https://api.mortar.one/v1/models \
  -H "Authorization: Bearer $MORTAR_API_KEY"
```

```json title="Response, first entry" output exact
{
  "object": "list",
  "data": [
    {
      "id": "Qwen/Qwen2.5-7B-Instruct",
      "object": "model",
      "created": 1767225600,
      "owned_by": "Alibaba Qwen",
      "max_model_len": 4096,
      "permission": [],
      "mortar": {
        "name": "Qwen 2.5 7B Instruct",
        "family": "Alibaba Qwen",
        "size": "7B dense",
        "context": "32K",
        "quant": "BF16 · AWQ INT4",
        "gpu": "1× L4 or A10G",
        "engine": "vLLM",
        "badge": "Popular",
        "available": true,
        "costPerMillionInput": 0.0313,
        "costPerMillionOutput": 0.2373,
        "costPerMillionCacheRead": 0.00313,
        "costPerMillionCacheWrite": 0.039125
      }
    }
  ]
}
```

### Response

| Field | Type | Description |
| --- | --- | --- |
| `object` | string | Always `list`. |
| `data` | array | One entry per model in the catalog. |
| `data[].id` | string | The ID to pass as `model`. |
| `data[].object` | string | Always `model`. |
| `data[].created` | integer | A fixed timestamp, the same for every model. |
| `data[].owned_by` | string | The model's family, such as `Alibaba Qwen`. |
| `data[].max_model_len` | integer | The context window this deployment serves, in tokens. Prompt and reply together must fit in it. |
| `data[].permission` | array | Always empty. |
| `data[].mortar.name` | string | The display name. |
| `data[].mortar.family` | string | The model family. |
| `data[].mortar.size` | string | Parameter count and architecture, such as `7B dense`. |
| `data[].mortar.context` | string | The longest context the model itself supports, such as `32K`. |
| `data[].mortar.quant` | string | The precisions Mortar has tested, such as `BF16 · FP8`. |
| `data[].mortar.gpu` | string | The GPU the model is sized for. |
| `data[].mortar.engine` | string | The serving engine: `vLLM` or `SGLang`. |
| `data[].mortar.badge` | string or null | A catalog label, `Popular` or `Coming soon`, or `null`. |
| `data[].mortar.available` | boolean | Whether the model can serve requests now. Requesting one that can't fails with `model_not_available`. |
| `data[].mortar.costPerMillionInput` | number | Sample price for a million prompt tokens, in US dollars. |
| `data[].mortar.costPerMillionOutput` | number | Sample price for a million completion tokens, in US dollars. |
| `data[].mortar.costPerMillionCacheRead` | number | Sample price for a million prompt tokens read from the prefix cache: a tenth of the input price. |
| `data[].mortar.costPerMillionCacheWrite` | number | Sample price for a million prompt tokens processed and written to the prefix cache, so a later request can read them: 1.25 times the input price. |

> [!NOTE]
> Prices are sample pricing, not a quote. Mortar derives them from on-demand GPU list prices and measured throughput at 65% utilization. [Prompt caching](/docs/guides/prompt-caching#what-caching-saves) explains how cache reads and writes add up, and the [model catalog](/models) shows each model's input price at a cache hit rate you choose.

## Context windows

Two fields describe context, and they answer different questions:

- `mortar.context` is the most the model can handle.
- `max_model_len` is what this deployment serves. Budget `max_tokens` against it.

They differ when a replica is started with a shorter window than the model allows. `Qwen/Qwen2.5-7B-Instruct` supports 32K tokens but is served here with 4,096, so a request with a 4,050-token prompt fails even though the model could read it. [Chat completions](/docs/api/chat-completions#request-body) explains how Mortar fits `max_tokens` into the window.