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

# Infercom Inference Service Models Overview and Specifications

> Browse all models available on Infercom. EU-hosted and Global Catalog models with context lengths, capabilities, and rate limits.

The Infercom Inference Service provides access to a broad selection of AI models through the **Global Model Catalog**. Models are available in two categories: **EU-hosted** models running on Infercom's sovereign infrastructure in Germany, and additional models available via global infrastructure.

## EU-hosted models

The following models run on Infercom's EU infrastructure in Germany. For these models, all data processing happens entirely within the EU — no data leaves EU jurisdiction, with full GDPR compliance and no US CLOUD Act exposure.

### Text generation models

| **Developer** | **Model ID**                  | **Context length** | **Capabilities** | **Region** | **HF Link**                                               |
| :------------ | :---------------------------- | :----------------- | :--------------- | :--------- | :--------------------------------------------------------------------- |
| **OpenAI**    | `gpt-oss-120b`                | 128k tokens        | Text, Reasoning  | EU         | [Model card](https://huggingface.co/openai/gpt-oss-120b)               |
| **Google**    | `gemma-4-31B-it`              | 128k tokens        | Text, Vision     | EU         | [Model card](https://huggingface.co/google/gemma-4-31b-it)             |

### Embedding model

| **Developer** | **Model ID**                  | **Context length** | **Capabilities** | **Region** | **HF Link**                                               |
| :------------ | :---------------------------- | :----------------- | :--------------- | :--------- | :--------------------------------------------------------------------- |
| **Mistral**   | `E5-Mistral-7B-Instruct`      | 4k tokens          | Embeddings       | EU         | [Model card](https://huggingface.co/intfloat/e5-mistral-7b-instruct)   |

### Audio model

| **Developer** | **Model ID**                  | **Max audio length** | **Capabilities**           | **Region** | **HF Link**                                               |
| :------------ | :---------------------------- | :------------------- | :------------------------- | :--------- | :--------------------------------------------------------------------- |
| **OpenAI**    | `Whisper-Large-v3`            | 25 MB                | Transcription, Translation | EU         | [Model card](https://huggingface.co/openai/whisper-large-v3)           |

## Global Model Catalog

In addition to EU-hosted models, the Infercom Inference Service provides access to a broader selection of models through the Global Model Catalog. Models not hosted in our EU datacenters are served via global infrastructure. You can always check a model's hosting region via the API or the Playground.

<Aside type="note">
Every model is clearly labeled with its hosting region — both in the API response and in the Playground. You always know where your data is being processed.
</Aside>

| **Developer** | **Model ID**                          | **Context length** | **Region** | **Capabilities** | **HF Link**                                                              |
| :------------ | :------------------------------------ | :----------------- | :--------- | :--------------- | :------------------------------------------------------------------------------------ |
| **DeepSeek**  | `DeepSeek-V3.1`                       | 128k tokens        | Non-EU     | Text, Reasoning  | [Model card](https://huggingface.co/deepseek-ai/DeepSeek-V3.1)                       |
| **DeepSeek**  | `DeepSeek-V3.2`                       | 32k tokens         | Non-EU     | Text, Reasoning  | [Model card](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)                       |
| **Meta**      | `Meta-Llama-3.3-70B-Instruct`         | 128k tokens        | Non-EU     | Text             | [Model card](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)               |
| **MiniMax**   | `MiniMax-M3`                          | 1M tokens          | Non-EU     | Text, Reasoning  | [Model card](https://huggingface.co/MiniMaxAI/MiniMax-M3)                            |

Our provider lists `MiniMax-M3` as a preview model: it has limited capacity and can be removed at short notice. Do not build production workloads on it. It accepts text only. Requests with image or video input return an error.

See [Identifying model regions](#identifying-model-regions) below for how to check where each model runs.

## Recommended sampling parameters

Sampling parameters are model-specific. Use the values the model's publisher recommends rather than a single setting across all models. The table below reproduces each publisher's own recommendation, with a link to the source.

| **Model ID**                  | **temperature** | **top_p** | **top_k** | **Publisher recommendation**                                                          |
| :---------------------------- | :-------------- | :-------- | :-------- | :------------------------------------------------------------------------------------ |
| `gpt-oss-120b`                | 1.0             | 1.0       | -         | [openai/gpt-oss](https://github.com/openai/gpt-oss)                                    |
| `gemma-4-31B-it`              | 1.0             | 0.95      | 64        | [Model card](https://huggingface.co/google/gemma-4-31b-it)                             |
| `MiniMax-M3`                  | 1.0             | 0.95      | -         | [Model card](https://huggingface.co/MiniMaxAI/MiniMax-M3)                              |
| `DeepSeek-V3.2`               | 1.0             | 0.95      | -         | [Model card](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)                         |
| `DeepSeek-V3.1`               | -               | -         | -         | None published                                                                          |
| `Meta-Llama-3.3-70B-Instruct` | -               | -         | -         | None published                                                                          |

A dash means the publisher does not publish a value for that parameter. Where no recommendation exists, start at `temperature` 1.0 and tune for your own workload. `DeepSeek-V3.1` publishes no prose guidance, but the generation config it ships defaults to `temperature` 0.6 and `top_p` 0.95.

The Infercom API accepts a `temperature` between 0 and 2. `temperature=1.0` does not mean "maximum randomness" - it means the model's probability distribution is used exactly as trained. Values below 1.0 sharpen it, values above 1.0 flatten it. Adjust `temperature`, `top_p`, or `top_k`, but generally not more than one of them at a time.

### If you omit `temperature`

The Infercom API applies no service-wide default. What you get depends on the model.

| **Model ID**                  | **Behaviour when `temperature` is omitted**      |
| :---------------------------- | :------------------------------------------------ |
| `gemma-4-31B-it`              | Greedy decoding. Identical to `temperature` 0.     |
| `gpt-oss-120b`                | Greedy decoding. Identical to `temperature` 0.     |
| `DeepSeek-V3.2`               | Greedy decoding.                                   |
| `Meta-Llama-3.3-70B-Instruct` | Greedy decoding.                                   |
| `MiniMax-M3`                  | Greedy decoding.                                   |

Greedy decoding means the model always takes its highest-probability token. It is a valid choice for reproducible output. It is also the setting most likely to trigger the repetition loop described below.

`/v1/responses` and `/v1/messages` behave the same way. Set `temperature` on every request.

<Aside type="caution">
**`/v1/messages` differs from the Anthropic API here.** The Anthropic API documents a `temperature` default of 1.0. Infercom applies no default. Code that you port from the Anthropic SDK switches to greedy decoding unless you set `temperature` yourself.
</Aside>

<Aside type="caution">
**Very low temperatures can cause non-terminating reasoning.** Reasoning models can enter a repetition loop, restating the same point until `max_tokens` runs out, returning `finish_reason: "length"` and no usable answer. Raising `max_tokens` does not help. Omitting `temperature` counts as a very low temperature on most models, so this failure can appear in requests that set no sampling parameters at all. A low temperature is still a legitimate choice for reproducible or structured output - just move it back towards the recommended value if you see truncated responses. See [Reasoning](/en/build/reasoning#budgeting-tokens-for-reasoning).
</Aside>

### Defaults for the other sampling parameters

When you omit a sampling parameter, the platform applies the value below. These hold for every model.

| **Parameter** | **Applied when omitted** | **Effect** |
| :------------------- | :----------------------- | :--------- |
| `temperature`        | 0.0                      | Any effective value below 0.001 is clipped, which forces greedy decoding |
| `top_p`              | 1.0                      | Unrestricted |
| `top_k`              | 1048576                  | Effectively unrestricted. This is a sentinel, not a vocabulary size - `gemma-4-31B-it` reports a vocabulary of 262,144 |
| `repetition_penalty` | 1.0                      | No penalty |

<Aside type="note">
  **`repetition_penalty` is the only repetition control this API applies.** `frequency_penalty` and `presence_penalty` are accepted for OpenAI compatibility and ignored, on every model. See [Accepted and ignored](/en/features/openai-compatibility#accepted-and-ignored). Raise `repetition_penalty` only after you have set a correct `temperature`, and keep it at or below 1.2. The accepted range is 1 to 2, but values at or above 1.8 can stop a reasoning model from producing any content. It is honoured on `gpt-oss-120b` and `gemma-4-31B-it`, and ignored on `DeepSeek-V3.1`, `DeepSeek-V3.2` and `Meta-Llama-3.3-70B-Instruct`. On `MiniMax-M3` any value other than 1.0 returns HTTP 500. It applies on `/v1/chat/completions` only, and it penalises tokens in your prompt as well as generated ones.
</Aside>

<Aside type="note">
  The byte-identical output you get when you omit `temperature` is **not** a pinned seed. It is the forced `top_k` of 1 described below. Do not rely on it for reproducibility across releases.
</Aside>

### How `top_k` interacts with `temperature`

<Aside type="caution">
  **`top_k` has no effect unless `temperature` is 0.001 or above.** Below that threshold, including when you omit `temperature` entirely, `top_k` is forced to 1 and decoding is greedy. Nothing in the response signals that your value was ignored.

  To sample, set `temperature` explicitly.
</Aside>

The request below asks for sampling across 40 candidate tokens. It returns argmax instead, because `temperature` is absent:

```json
{
  "model": "gemma-4-31B-it",
  "messages": [{"role": "user", "content": "Write one sentence about the sea."}],
  "top_k": 40
}
```

On `gemma-4-31B-it`, with an identical prompt:

| Request | Result |
| :------ | :----- |
| `top_k: 40`, `temperature` omitted | Greedy. The same text on every request, `top_k` ignored |
| `top_k: 40`, `temperature: 1.0` | Sampling. The response varies on every request |
| No `top_k`, `temperature` omitted | Greedy. Byte-identical to the first row |

#### Accepted range for `top_k`

Send `top_k` as **1 or greater**. Unlike some other providers, `-1` and `0` are not accepted as a way to disable it.

| **Value sent** | `/v1/chat/completions` and `/v1/messages` | `/v1/responses` |
| :------------- | :---------------------------------------- | :-------------- |
| `-1`           | HTTP 400 `Invalid value for 'top_k': -1 is less than the minimum of 1.` | HTTP 200. Not validated |
| `0`            | HTTP 400 `Invalid value for 'top_k': 0 is less than the minimum of 1.`  | HTTP 200. Not validated |
| `1`            | HTTP 200 | HTTP 200 |
| `1048576`      | HTTP 200 | HTTP 200 |
| `2000000`      | HTTP 200. No upper bound is enforced | HTTP 200 |

<Aside type="caution">
  `/v1/responses` accepts `-1` and `0` without an error, but does not treat them as "unrestricted". Do not rely on that. Use a value of 1 or greater on every endpoint.
</Aside>

To leave `top_k` unrestricted, omit the parameter or send a value larger than the model's vocabulary.

## Identifying model regions

You can identify where a model is hosted through the API or the Playground.

### Via the API

The `/v1/models` endpoint includes an `sn_metadata` object for each model. Use the `region` field to determine where a model is hosted: `"EU"` for sovereign models on Infercom's EU infrastructure, or a non-EU region (e.g. `"US"`, `"JP"`) for models on global infrastructure.

Use the `?verbose=true` query parameter to retrieve detailed model metadata including sovereignty information:

<Tabs syncKey="code">
<TabItem label="cURL">

```bash
curl -s "https://api.infercom.ai/v1/models?verbose=true" \
  -H "Authorization: Bearer $INFERCOM_API_KEY" | \
  jq '.data[] | {id, region: .sn_metadata.region}'
```

</TabItem>
<TabItem label="Python">

```python
from openai import OpenAI
import requests

# Using requests for metadata inspection
headers = {"Authorization": f"Bearer {api_key}"}
response = requests.get(
    "https://api.infercom.ai/v1/models?verbose=true",
    headers=headers
)

for model in response.json()["data"]:
    region = model.get("sn_metadata", {}).get("region", "Global")
    print(f"{model['id']}: {region}")
```

</TabItem>
</Tabs>

**Example response for an EU-hosted model:**

```json
{
  "id": "gpt-oss-120b",
  "object": "model",
  "sn_metadata": {
    "is_external": false,
    "region": "EU"
  }
}
```

**Example response for a globally-routed model:**

```json
{
  "id": "DeepSeek-V3.1",
  "object": "model",
  "sn_metadata": {
    "is_external": true,
    "region": "JP"
  }
}
```

### Via the Playground

In the [Infercom Playground](https://cloud.infercom.ai), region flags are displayed next to each model name, letting you see at a glance where each model runs.

## Data sovereignty

<Aside type="caution">
**EU sovereignty applies to EU-hosted models only.** When using models from the Global Model Catalog that are not hosted on EU infrastructure, requests are processed on global infrastructure outside the EU. Always check the model's region before processing sensitive or regulated data.
</Aside>

For EU-hosted models, Infercom provides:

- **EU data residency** — inference runs in our EU datacenters
- **GDPR compliance** — full compliance with EU data protection regulations
- **No US CLOUD Act exposure** — your inference data is not subject to US jurisdiction
- **AI Act readiness** — designed for compliance with the EU AI Act