> ## Documentation Index
> Fetch the complete documentation index at: https://benchgen.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# From an External API
> Benchmark any OpenAI-compatible endpoint, such as OpenRouter or Mistral, by pointing BenchGen at its URL and key.
The **External API** source lets you benchmark a model that lives behind your own
OpenAI-compatible endpoint, such as OpenRouter, Mistral, OpenAI, or a self-hosted server. You give
BenchGen the endpoint URL, model name, and an optional key. The credentials are used only for that
run and are injected server-side as environment variables.
**When to use this.** Reach for External API when the model is served somewhere else behind an
OpenAI-compatible API. If the model is on BenchGen use [On a platform
model](/docs/eval/run-a-benchmark-platform); to pull one from the Hub use [From a HuggingFace
model](/docs/eval/run-a-benchmark-huggingface).
***
## Prerequisites
* A benchmark or environment to evaluate against, from the Environments Hub or a [custom
environment](/docs/eval/create-environment) you uploaded.
* An OpenAI-compatible endpoint URL (for example `https://openrouter.ai/api/v1/chat/completions`).
* The model name as the provider expects it (for example `mistralai/mistral-small-24b-instruct-2501`).
* An API key or token, if the endpoint requires one.
***
## Steps
### 1. Open a benchmark
Open the environment you want to evaluate against. The **Overview** tab describes what the
benchmark measures and how it scores submissions.
Click **Evaluate** in the top right, or open the **Evaluate** tab.
### 2. Switch to the External API source
On the **Evaluate** tab, the model sources are grouped into tabs: **Platform Models**, **Running**,
**Trained**, **HuggingFace**, and **External API**. Click **External API**.
### 3. Enter the endpoint details
Fill in the endpoint form:
| Field | Required | Example |
| ------------------- | -------- | ----------------------------------------------- |
| **Endpoint URL** | Yes | `https://openrouter.ai/api/v1/chat/completions` |
| **Model name** | Yes | `mistralai/mistral-small-24b-instruct-2501` |
| **API key / token** | Optional | Your provider key (stored for this run only) |
| **Display name** | Optional | A friendly label shown in results |
Click **Use this endpoint**. A confirmation appears: **Selected: mistralai/mistral-small-24b-instruct-2501**.
Credentials are used only to run this evaluation and are injected into the benchmark container as
environment variables. They are not stored as a reusable model.
### 4. Set run values (optional)
Expand **Advanced: model environment & parameters** to review what the benchmark injects. Model
connection values are filled from the endpoint you entered; you fill only the fields the benchmark
owner exposed (here `TOX_SYSTEM_PROMPT`, `TOX_MAX_QUESTIONS`, and the optional `TOX_JUDGE_*` keys),
and you can adjust the sampling parameters.
See [Environment variables](/docs/eval/environment-variables) for how these values are declared and
injected. Leave a field blank to fall back to the benchmark defaults.
### 5. Run the evaluation
Click **Run Evaluation**. BenchGen creates the run and opens a live view. While the endpoint is
being wired up, the status reads **Deploying** and the logs wait for output.
Once running, the logs confirm the environment variables that were injected into the container, for
example `MODEL_API_URL`, `MODEL_NAME`, and the benchmark's prefixed keys such as `TOX_API_URL` and
`TOX_MODEL_NAME`, followed by per-item progress like `[ADIM] 1/30`.
### 6. Review the results
When the run finishes, the status turns **Completed** and the **Score Breakdown** appears. The
example below scored **96.7%** (29 of 30 correct).
The **Detailed Results** table lists each item with its correct label, the model's answer, and the
scoring reason. Download the prediction output, scoring output, or the submission archive from the
**Files** panel. See [Read results](/docs/eval/read-results) for a full walkthrough.
***
## Next steps
* [Read results](/docs/eval/read-results): interpret the score breakdown and detailed table.
* [Environment variables](/docs/eval/environment-variables): how run values reach the model.
* [On a platform model](/docs/eval/run-a-benchmark-platform): benchmark a model already on BenchGen.