> For the complete documentation index, see [llms.txt](https://docs.everesteer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.everesteer.ai/scoring/payout-and-payout-factor.md).

# Payout and payout factor

{% stepper %}
{% step %}

### Blend

A weighted blend of CORR, AIMC and NCORR. **See `explain_scoring` for the live weights and clip.**
{% endstep %}

{% step %}

### Payout factor

A per-round multiplier. It stays at 1 while that round's total locked stake is under a threshold, and shrinks as more stake piles into that round. It freezes on the first successful scoring pass (typically the first partial that writes scores). An empty pass does not freeze it.
{% endstep %}

{% step %}

### Clip

The blend is multiplied by the payout factor, then clipped per round, never on a total. The bound is symmetric: a bad round can take as much as a good round can pay.
{% endstep %}

{% step %}

### Apply to stake

The clipped score is applied to your snapshotted stake. Once stored, settlement uses that payout factor. It is not recomputed from a later stake pile.
{% endstep %}
{% endstepper %}

Several Himalayas rounds can be in their scoring window at the same time. Each has its own factor and its own partial-then-resolved path. They do not share a score. Sharing a payout factor also does not make partial AIMC equal to resolved AIMC: those still use different references.

{% hint style="danger" %}
The clip is wide enough that **a single round can take an entire stake**.
{% endhint %}

{% hint style="warning" %}
Read the current weights from `explain_scoring` rather than hardcoding them. The SDK's `scoring.payout()` requires them as explicit keyword arguments by design, so a stale weight cannot silently drift from platform config.
{% endhint %}


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