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

# Subagent Cost Model

> Why delegating work to subagents lowers cost in Claude Code, how much of it is the cheaper model versus the smaller context, and when delegation does not pay…

# Subagent Cost Model

Why delegating work to subagents lowers cost in Claude Code, how much of it is the cheaper model versus the smaller context, and when delegation does not pay. Prices are Anthropic first-party rates as published 2026-10-09 and change; recheck [https://platform.claude.com/docs/en/about-claude/pricing](https://platform.claude.com/docs/en/about-claude/pricing) before relying on a ratio.

## Mechanics

Every request sends the whole conversation again. With prompt caching, the resent prefix is billed at the cache-read price and only new tokens are billed at the write price, so session cost is dominated by **context size multiplied by number of requests**, not by output. Two levers follow:

1. **Context lever.** Run a loop that would grow the main context (edit, test, fix, test) inside a subagent. The subagent starts small, its context is discarded when it returns, and the main session keeps only the plan and a short report. Every later main-session request stays smaller.
2. **Price lever.** Run the loop on a cheaper model. Cache reads are the bulk of tokens, so the benefit depends on the cache-read price gap, not the headline input price.

Measured in Claude Code Subagents Pro Limits: re-pricing the subagents' tokens at Opus rates raised cost only about 5%, while total cost per PR fell about 40%. Requests per Opus session fell 75 to 54 and context per request 119k to 107k. Most of that saving was the context lever. One user and a selected sample, so indicative only.

## Price table (per million tokens, 2026-10-09)

| Model | Input | Output | Cache read | Notes |
| - | - | - | - | - |
| Opus 5.5 | \$4 | \$20 | \$0.20 | Fast mode $8 / $40 |
| Sonnet 5.5 | \$2 | \$10 | \$0.10 | Cache read cut from \$0.20 on 2026-10-07 |
| Haiku 5.5 | \$0.10 | \$0.50 | \$0.01 | Prompts up to 100k; above that $0.50 / $2.50 / \$0.05 |

Opus 5.5 is only about 2x Sonnet 5.5 on input and output, so "Opus costs several times Sonnet" is out of date. Haiku 5.5 is roughly 20x cheaper than Sonnet on input and 10x on cache read, which is why read-only exploration is the cleanest place to downgrade.

## Worked example (illustrative, not measured)

A 20-request edit-and-test loop that adds 3k tokens of context per request, counting cache reads only (cache writes and output excluded).

| | Starting context | Context summed over 20 requests | Cache-read price | Cost |
| - | - | - | - | - |
| In an Opus main session | 100k | 2.63M | \$0.20 | about \$0.53 |
| In a Sonnet subagent | 15k | 0.93M | \$0.10 | about \$0.09 |

The context lever contributes about 2.8x and the price lever about 2x. The 60k tokens the loop would have added to the main context also vanish from every later main request. The subagent's starting 15k assumes CLAUDE.md, the system prompt and the handoff; a large CLAUDE.md raises it unless the agent sets `omitClaudeMd: true`.

## When delegation does not pay

* **Handoff cost equals edit cost.** A small, fully known change is cheaper to write than to brief and then review. Keep it in the main session.
* **Stacked review.** A second review by another large model can cost nearly as much as writing the code. One default review in the main session; a dedicated reviewer only for risky changes.
* **Git steps.** A few hundred tokens of commands do not justify a handoff.
* **Agent teams.** About 7x the tokens in plan mode per the docs; use Sonnet teammates and keep teams small ([https://code.claude.com/docs/en/agent-teams](https://code.claude.com/docs/en/agent-teams)).
* **Judgment-heavy work.** Architecture, root cause, and security-sensitive changes stay with the strongest model; a cheaper model that redoes the work costs more than it saved.

## Controls that decide which model runs

Precedence for a subagent's model ([https://code.claude.com/docs/en/sub-agents](https://code.claude.com/docs/en/sub-agents)): the per-invocation `model` parameter, then the agent's frontmatter `model`, then `CLAUDE_CODE_SUBAGENT_MODEL`, then the main conversation model. The environment variable only fills gaps; it does not override frontmatter, and it does not change the built-in Explore and Plan agents unless `CLAUDE_CODE_SUBAGENT_MODEL_FORCE=1` is set.

Effort resolves in this order ([https://code.claude.com/docs/en/model-config](https://code.claude.com/docs/en/model-config)): `CLAUDE_CODE_EFFORT_LEVEL` or `--effort` or `/effort`; then `modelSettings` or `effortLevel`; then the model default, which is medium for Opus 5.5, Sonnet 5.5 and Haiku 5.5. A subagent's `effort` frontmatter overrides the session level but not the environment variable. A top-level `effortLevel` in user settings does not apply to Opus 5.5.

Other cost keys on a subagent: `omitClaudeMd`, `maxTurns`, `permissionMode: plan` for read-only scouts, `background`, and `skills` preloading. `CLAUDE_CODE_MAX_SUBAGENT_SPAWN_DEPTH` (default 3 layers) and `CLAUDE_CODE_MAX_CONCURRENT_SUBAGENTS` (default 20) bound runaway fan-out.

## Measure before and after

Statusline JSON supplies `rate_limits.five_hour.used_percentage`, `rate_limits.seven_day.used_percentage`, `cost.total_cost_usd` and `session_id` (the rate-limit fields only for Pro/Max subscribers, after the first response). Logging a line whenever a percentage changes and matching sessions to PRs gives a cost-per-PR figure for your own workload. `cost.total_cost_usd` is a client-side estimate and resets on `/clear`. The docs do not state that it includes subagent requests, though subagent usage counts toward limits and appears in `/usage`.

## Application in this setup (as of 2026-10-09)

Implementation is dispatched to Pi under the plan-handoff rule, which moves it out of Claude usage entirely and is a stronger form of the context lever. In-Claude subagents are therefore mainly for read-only exploration and review. No custom agent roster is installed; `CLAUDE_CODE_SUBAGENT_MODEL` is set as a gap filler. No cost logging exists yet.

## Related Pages

* Claude Code Subagents Pro Limits — the measured source and its caveats
* [Agent Subagents](/concepts/agent-subagents) — frontmatter, scopes, invocation
* [Model Tier Routing](/concepts/model-tier-routing) — which tier for which task
* [Context Compression Strategies](/concepts/context-compression) — KV-cache stability and compaction
* [Agent Primitive Selection](/syntheses/agent-primitive-selection) — skill vs subagent vs team

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return {nodes:NODES.filter(function(n){return keep.has(n.id);}),links:LINKS.filter(function(l){return keep.has(lid(l.source))&&keep.has(lid(l.target));})};
}
var el=document.getElementById('g');
var G=ForceGraph()(el).backgroundColor('#0f1117').nodeId('id')
.warmupTicks(24).cooldownTicks(70).autoPauseRedraw(true)
.nodeColor(function(n){return C[n.group]||'#9CA3AF';}).nodeLabel('label').nodeVal(function(n){return n.val;})
.linkColor(function(){return 'rgba(255,255,255,0.12)';})
.nodeRelSize(opt.ns).linkWidth(opt.lw)
.linkDirectionalArrowLength(0).linkDirectionalArrowRelPos(1).linkDirectionalArrowColor(function(){return 'rgba(255,255,255,0.4)';})
.nodeCanvasObjectMode(function(){return 'after';})
.nodeCanvasObject(function(n,ctx,scale){var r=opt.ns*Math.sqrt(n.val||1);
if(n.id===CUR){ctx.beginPath();ctx.arc(n.x,n.y,r+1.6,0,6.283);ctx.strokeStyle='#fff';ctx.lineWidth=1.2/scale;ctx.stroke();}
if(opt.to>0&&opt.ts>0){var t=n.label.length>28?n.label.slice(0,26)+'…':n.label;ctx.globalAlpha=opt.to;ctx.font=((n.id===CUR?opt.ts+1:opt.ts))+'px ui-sans-serif,sans-serif';ctx.fillStyle=(n.id===CUR)?'#ffffff':'#aab0c0';ctx.textAlign='center';ctx.textBaseline='top';ctx.fillText(t,n.x,n.y+r+1.5);ctx.globalAlpha=1;}})
.onNodeClick(function(n){if(window.top){window.top.location.href='/'+n.id;}});
G.graphData(visible());G.d3VelocityDecay(0.4);
function fit(){G.zoomToFit(400,20);}
setTimeout(fit,350);setTimeout(fit,1100);
// Stop the render/sim loop while idle so the fixed widget never repaints during
// parent-page scroll; resume only while the pointer is over the widget.
var pt;function pause(){G.pauseAnimation();}function resume(){G.resumeAnimation();}
function idle(ms){clearTimeout(pt);pt=setTimeout(pause,ms);}
document.body.addEventListener('pointerenter',function(){clearTimeout(pt);resume();});
document.body.addEventListener('pointerleave',function(){idle(250);});
addEventListener('resize',function(){resume();G.zoomToFit(0,20);idle(700);});
idle(2000);
function apply(re){resume();G.nodeRelSize(opt.ns).linkWidth(opt.lw).linkDirectionalArrowLength(opt.ar?2.6:0);if(re){G.graphData(visible());setTimeout(fit,450);}idle(re?2200:1400);}
function bind(id,key,fmt,re){var e=document.getElementById(id),o=document.getElementById('v'+id);e.value=opt[key];if(o)o.textContent=fmt(opt[key]);e.addEventListener('input',function(){opt[key]=parseFloat(e.value);if(o)o.textContent=fmt(opt[key]);apply(re);});}
bind('ns','ns',function(v){return v.toFixed(1);},false);
bind('lw','lw',function(v){return v.toFixed(1);},false);
bind('ts','ts',function(v){return v.toFixed(1);},false);
bind('to','to',function(v){return v.toFixed(2);},false);
var dE=document.getElementById('dp'),dO=document.getElementById('vd');dE.max=MAXD;dE.value=opt.dp;dO.textContent=opt.dp;dE.addEventListener('input',function(){opt.dp=parseInt(dE.value,10);dO.textContent=opt.dp;apply(true);});
if(!CUR)document.getElementById('depthRow').style.display='none';
var aE=document.getElementById('ar');aE.checked=opt.ar;aE.addEventListener('change',function(){opt.ar=aE.checked;apply(false);});
document.getElementById('gear').addEventListener('click',function(){document.getElementById('panel').classList.toggle('open');});
var hd=document.getElementById('hd');hd.textContent='⠿  '+(CUR?'Local graph':'Knowledge graph');
// free-form placement: drag by the header. Default is bottom-right (inline style);
// a moved position is saved per parent-origin and restored on every page.
function clampPos(fe,l,t){var TW=(window.top||window),r=fe.getBoundingClientRect();return [Math.min(Math.max(0,l),Math.max(0,TW.innerWidth-r.width)),Math.min(Math.max(0,t),Math.max(0,TW.innerHeight-r.height))];}
function place(fe,l,t){var p=clampPos(fe,l,t);fe.style.left=p[0]+'px';fe.style.top=p[1]+'px';fe.style.right='auto';fe.style.bottom='auto';}
try{var sp=JSON.parse(localStorage.getItem('llmwiki_graph_pos'));if(sp&&window.frameElement)place(window.frameElement,sp.l,sp.t);}catch(e){if(window.console)console.debug('graph: saved position unavailable',e);}
hd.addEventListener('pointerdown',function(e){var fe=window.frameElement;if(!fe)return;var rect=fe.getBoundingClientRect();var sx=e.screenX,sy=e.screenY,L=rect.left,T=rect.top;place(fe,L,T);hd.setPointerCapture(e.pointerId);
function mv(ev){place(fe,L+ev.screenX-sx,T+ev.screenY-sy);}
function up(){if(hd.hasPointerCapture(e.pointerId))hd.releasePointerCapture(e.pointerId);hd.removeEventListener('pointermove',mv);hd.removeEventListener('pointerup',up);try{localStorage.setItem('llmwiki_graph_pos',JSON.stringify({l:parseFloat(fe.style.left),t:parseFloat(fe.style.top)}));}catch(e2){if(window.console)console.debug('graph: could not persist position',e2);}}
hd.addEventListener('pointermove',mv);hd.addEventListener('pointerup',up);e.preventDefault();});
</script></body></html>"
  title="Knowledge graph"
  loading="lazy"
  style={{position:"fixed",right:"18px",bottom:"18px",width:"320px",height:"340px",border:0,borderRadius:"14px",boxShadow:"0 6px 28px rgba(0,0,0,0.38)",zIndex:50,background:"#0f1117"}}
/>


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