> ## 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. # Evaluate an Inference Model > Benchmark a live inference endpoint against an Eval environment, monitor the run in real time, and read the scored results. Once a model is [running as an inference endpoint](/eval/run-an-inference-model), you can benchmark it against any environment in Eval. BenchGen runs the model over every test case in the environment, scores each response, and produces a results report with downloadable predictions and a breakdown for every item. **Need a running model first?** This guide assumes you already have a live endpoint. If you don't, follow [Deploy an inference model](/eval/run-an-inference-model) and come back once its status reads **running**. *** ## Prerequisites * A model in the **running** state. See [Deploy an inference model](/eval/run-an-inference-model). * A benchmark or environment to evaluate against, either from the Environments Hub or a [custom environment](/eval/create-environment) you uploaded. *** ## Steps ### 1. Open a benchmark Open the environment you want to evaluate against. Its **Overview** tab describes what the benchmark measures and how submissions are scored, and shows tabs for **Phases**, **Leaderboard**, **Evaluations**, and **Evaluate**. To start, click **Evaluate** in the top right corner, or open the **Evaluate** tab. The GSM8K-TR benchmark overview page with the Evaluate button ### 2. Choose a model source The **Evaluate** tab opens with **"Select a model to evaluate."** Models are grouped by source. Pick the tab that matches where your model lives: | Source | Use when | | ------------------- | ---------------------------------------------------------------- | | **Platform Models** | The model is already published or deployed on BenchGen. | | **Running** | The model is a live endpoint you started with **Run Inference**. | | **Trained** | The model is a checkpoint you fine-tuned in Train. | | **HuggingFace** | You want to pull a public model from the HuggingFace Hub. | The Evaluate tab showing the four model source tabs ### 3. Select your running model Since you just deployed an endpoint, click the **Running** tab. It lists every model that is currently live. Find the one you deployed. It shows a green **running** badge. The Running tab listing the live demoaccount-gemma4 endpoint Click the model to select it. A checkmark appears and the **Run Evaluation** button becomes active. The running model selected, with Run Evaluation now enabled ### 4. Run the evaluation Click **Run Evaluation**. BenchGen creates an evaluation run, generates a submission for the selected environment, and starts running your model against the test cases. ### 5. Monitor progress The run opens to a **live log** view. Status messages stream as the run progresses: it loads the benchmark data, runs the model on each item, and reports progress like `Processing: 10/100 (10%)`. The evaluation running with live logs and the evaluation details panel The **Evaluation details** panel on the right sums up the run: | Field | Meaning | | --------------- | ------------------------------------------------------- | | **Status** | `Running` while in progress, `Completed` when finished. | | **Model** | The model being evaluated. | | **Environment** | The benchmark the model is scored against. | | **Created** | When the run started. | | **Submission** | The generated submission archive for this run. | | **Run ID** | Unique identifier for the run, for example `#678`. | As the run nears completion, the logs show predictions being generated and the final score being computed, for example `accuracy=26.00% correct=26/100`. Completed logs showing generated predictions and the computed accuracy ### 6. Review scores and results When the run finishes, the status turns to **Completed** and a **Score Breakdown** replaces the live logs. The Score Breakdown and detailed results for the completed run The headline **Overall Score** sits at the top, followed by the individual metrics: | Metric | Meaning | | ----------------- | ----------------------------------------------------------------- | | **Overall Score** | The headline score for the run. | | **accuracy** | Percentage of items answered correctly. | | **exact match** | Percentage of responses that matched the expected answer exactly. | | **answer rate** | Percentage of items the model produced any answer for. | | **duration** | Wall-clock time for the full run. | Below the metrics, **Detailed Results** shows a table with one row per test case. Each row lists the item ID, the gold (expected) answer, the model's prediction, and whether it was correct. #### Download the artifacts The **Files** section in the panel on the right lets you download everything the run produced: | File | Contents | | --------------------- | ---------------------------------------------------------------------------------------------------------------------- | | **Submission File** | The original archive submitted to the benchmark. | | **Prediction Output** | The model's raw predictions for every item. | | **Scoring Output** | The output emitted by the environment's scoring program. | | **Detailed Results** | The full breakdown for every item. View it in the browser or download it as HTML or JSON. | | **Generated dataset** | When the environment emits one, a fine-tune dataset you can open with **View fine-tune dataset** and carry into Train. | Failing cases captured in the generated dataset are exactly what you feed into a fine-tune. See [Export datasets to Train](/eval/export-datasets) to turn this run's misses into your next training set. *** ## Next Steps * [Read results](/eval/read-results) * [Export datasets to Train](/eval/export-datasets) * [Deploy an inference model](/eval/run-an-inference-model)