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

# Evaluation results

> Read the model health score and decide whether it's trustworthy

**Evaluation rates the models your agents depend on.** Discovery finds the model; evaluation tells you how it behaved under attack.

## Reading the health score

Every evaluated model carries a **Health score**. You meet it first as a column in **Assets** → **Models**, showing your models side by side.

The health score is a grade from 0 to 100, and it runs the opposite direction from severity: a high severity is bad news, a high health score is good news. The **Models** table carries both, because they answer different questions: how the model held up under attack, and how serious the worst finding against it is.

Each score falls into a band:

| Health score | Band      |
| ------------ | --------- |
| 81 to 100    | Excellent |
| 61 to 80     | Good      |
| 41 to 60     | Fair      |
| 21 to 40     | Poor      |
| 0 to 20      | Very Poor |

<Note>
  **Not every model is evaluated.** When no evaluation is available, the health score is empty rather than zero. This is expected.
</Note>

## What the category scores tell you

Behind the overall **Model health score** are category scores for **Jailbreaks**, **Misuse**, **Toxicity**, **Security**, and **Robustness**. Each summarizes how the model performed across the tests in that category, scored 0 to 100 the same way. A higher score is stronger performance under evaluation, not a guarantee of how the model behaves in your application. For what each category tests, see [How evaluation works](/flintai/platform/evaluation/how-evaluation-works).

Open an evaluated model to reach its **Overview** tab, where a **Model health score** card shows the overall score with the category scores beside it. The card appears only once a model has been evaluated, and a category with no result is left out rather than shown as zero.

<Note>
  **A strong overall score can still conceal a weakness in one category.** A model scoring 65 overall carries the **Good** band, but with **Jailbreaks** at 30 it is not a good choice for an agent that takes untrusted input — it is a jailbreak risk that the overall score doesn't show you.
</Note>

## Probe results

Open a model and select its **Evaluations** tab to see the individual tests behind the score. Each row is one probe — a named attack technique — showing:

* The probe name, and its category as a chip
* What it tries, in a sentence
* The score it earned, with its band

This is where a weak category score becomes something you can act on. A low **Jailbreaks** score is a list of named techniques the model did not hold up against, and the probe descriptions tell you what those techniques actually do.

## What to do about a weak model

A low score is not automatically a problem. It is a problem in context.

<Steps>
  <Step title="Check which agents use it">
    Open the model and read **Used by** on its **Overview** tab, or open the **Locations** tab for the files each dependency sits in. A weak model nothing calls yet is a decision you get to make before it matters.
  </Step>

  <Step title="Match the weakness to the exposure">
    A low **Toxicity** score matters most where output reaches users. A low **Jailbreaks** score matters most where input comes from outside your organization. A weak score in a category your agent never exercises is a lower priority than a middling one it leans on.
  </Step>

  <Step title="Check whether it filed a finding">
    Scores past a threshold appear in **Insights** → **Issues** with a severity attached. If it's there, it has already been triaged into your work queue — see [Discovery results](/flintai/platform/discovery/results).
  </Step>

  <Step title="Swap it or contain it">
    Either move the agent to a stronger model, or leave it and constrain what reaches it with [guardrails](/flintai/platform/guardrails/index). If you're swapping, read the **Details** card on the model's **Overview** tab — **Deprecation type**, **Suggested replacement**, and **Deprecation date** tell you whether the model is on its way out and what replaces it.
  </Step>
</Steps>

## Next steps

<CardGroup cols={2}>
  <Card title="How evaluation works" icon="flask" href="/flintai/platform/evaluation/how-evaluation-works">
    What gets tested, how scoring works, and how a score becomes a finding
  </Card>

  <Card title="Discovery results" icon="triangle-exclamation" href="/flintai/platform/discovery/results">
    Triage findings, confirm they're real, and know when they're resolved
  </Card>
</CardGroup>