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

# Token use

# Token use & costs

Equabot tracks **tokens**, not characters. Tokens are model-specific, but most
OpenAI-style models average \~4 characters per token for English text.

## How the system prompt is built

Equabot assembles its own system prompt on every run. It includes:

* Tool list + short descriptions
* Skills list (only metadata; instructions are loaded on demand with `read`)
* Self-update instructions
* Workspace + bootstrap files (`AGENTS.md`, `SOUL.md`, `TOOLS.md`, `IDENTITY.md`, `USER.md`, `HEARTBEAT.md`, `BOOTSTRAP.md` when new). Large files are truncated by `agents.defaults.bootstrapMaxChars` (default: 20000).
* Time (UTC + user timezone)
* Reply tags + heartbeat behavior
* Runtime metadata (host/OS/model/thinking)

See the full breakdown in [System Prompt](/concepts/system-prompt).

## What counts in the context window

Everything the model receives counts toward the context limit:

* System prompt (all sections listed above)
* Conversation history (user + assistant messages)
* Tool calls and tool results
* Attachments/transcripts (images, audio, files)
* Compaction summaries and pruning artifacts
* Provider wrappers or safety headers (not visible, but still counted)

For a practical breakdown (per injected file, tools, skills, and system prompt size), use `/context list` or `/context detail`. See [Context](/concepts/context).

## How to see current token usage

Use these in chat:

* `/status` → **emoji‑rich status card** with the session model, context usage,
  last response input/output tokens, and **estimated cost** (API key only).
* `/usage off|tokens|full` → appends a **per-response usage footer** to every reply.
  * Persists per session (stored as `responseUsage`).
  * OAuth auth **hides cost** (tokens only).
* `/usage cost` → shows a local cost summary from Equabot session logs.

Other surfaces:

* **TUI/Web TUI:** `/status` + `/usage` are supported.
* **CLI:** `equabot status --usage` and `equabot channels list` show
  provider quota windows (not per-response costs).

## Cost estimation (when shown)

Costs are estimated from your model pricing config:

```
models.providers.<provider>.models[].cost
```

These are **USD per 1M tokens** for `input`, `output`, `cacheRead`, and
`cacheWrite`. If pricing is missing, Equabot shows tokens only. OAuth tokens
never show dollar cost.

## Cache TTL and pruning impact

Provider prompt caching only applies within the cache TTL window. Equabot can
optionally run **cache-ttl pruning**: it prunes the session once the cache TTL
has expired, then resets the cache window so subsequent requests can re-use the
freshly cached context instead of re-caching the full history. This keeps cache
write costs lower when a session goes idle past the TTL.

Configure it in [Gateway configuration](/gateway/configuration) and see the
behavior details in [Session pruning](/concepts/session-pruning).

Heartbeat can keep the cache **warm** across idle gaps. If your model cache TTL
is `1h`, setting the heartbeat interval just under that (e.g., `55m`) can avoid
re-caching the full prompt, reducing cache write costs.

For Anthropic API pricing, cache reads are significantly cheaper than input
tokens, while cache writes are billed at a higher multiplier. See Anthropic’s
prompt caching pricing for the latest rates and TTL multipliers:
[https://docs.anthropic.com/docs/build-with-claude/prompt-caching](https://docs.anthropic.com/docs/build-with-claude/prompt-caching)

### Example: keep 1h cache warm with heartbeat

```yaml theme={null}
agents:
  defaults:
    model:
      primary: "anthropic/claude-opus-4-5"
    models:
      "anthropic/claude-opus-4-5":
        params:
          cacheControlTtl: "1h"
    heartbeat:
      every: "55m"
```

## Tips for reducing token pressure

* Use `/compact` to summarize long sessions.
* Trim large tool outputs in your workflows.
* Keep skill descriptions short (skill list is injected into the prompt).
* Prefer smaller models for verbose, exploratory work.

See [Skills](/tools/skills) for the exact skill list overhead formula.