Agent Routing — model, effort, and cascade selection

Current prices, and what changed in 2026-10

model	$/MTok in	out	cache read	one Agent-tool spawn, small seeded repo	one spawn, SWE-bench Verified
Haiku 5.5	$0.10	$0.50	$0.01	$0.005–0.011	$0.024–0.037
Sonnet 5.5	$2	$10	$0.20	$0.17	$0.23–0.35
Opus 5.5	$4	$20	$0.20	~$0.34 (2× Sonnet, not measured)	—
Fable 5.1	$10	$50	$0.25	—	—

Haiku 5.5 is 20× under Sonnet 5.5 on every token class (Haiku 4.5 was 2–5×), and its prices hold for prompts up to 100K tokens ($0.50/$2.50 beyond). On the 14-repo seeded-bug battery it solved 11/14 at rung 1, which is the pass set Sonnet 5.5 at low gets. With the same-model informed retry it solved 14/14 for $0.12 in total, less than one Sonnet 5.5 spawn (oaustegard/experiments → temporal-routing-headroom, 2026-10-07). Where the Haiku 4.5 figures below say Haiku loses on cost, rerun the arithmetic at 5.5 prices before trusting them.

On real issues the picture holds with a smaller margin. Over 295 SWE-bench Verified tasks (django and sympy) Haiku 5.5 resolved 256 (86.8%) at rung 1 for $7.06 in total. On its 39 misses, rung 2 with the failing tests' output resolved 30 retrying Haiku ($1.43) and 34 escalating to Sonnet 5.5 ($13.53). Sonnet solved every task the Haiku retry did and 4 more (p = 0.125) (oaustegard/experiments → swe-ladder, 2026-10-09). Real repair spawns run longer than seeded ones, so a spawn costs 3–5× the small-repo figure on Haiku and 1.5–2× on Sonnet; the ratio between the tiers stays near 10×.

In Claude Code, price a subagent per spawn first. A fresh Agent-tool subagent writes a ~55K-token prefix (system prompt, tools, skills list) to the 5-minute cache on its first request. On short repair tasks that prefix outweighed everything the subagent produced: a Sonnet 5.5 spawn cost $0.17 input-side, 14× the $0.0121 output-only figure this skill used to quote per completed task. Same-model spawns in sequence share the cached prefix (reads cost 0.1×), so serialise spawns of one model when they can wait, and count spawns before counting output tokens. Every table further down prices output tokens only. That is still right for comparing tiers on long outputs, but it understates what a spawn costs.

The rule that decides everything

Cost is output tokens × output price, plus the spawn's prefix. Prices span ~5× across tiers. Token counts span up to 7× within a single tier depending on task shape. The shape therefore decides more than the tier does, and routing on the per-token discount gets the answer backwards.

Measured 2026-08-17, 14 spec-dense Python modules graded by hidden tests, all tiers at equal quality where noted:

arm	tok/task	pass	$/task	vs opus
haiku-solo	20,051	14/14	$0.1007	1.30×
haiku + concision	13,342	12/14	$0.0672	1.01×
opus-solo	3,001	14/14	$0.0774	1.00×
sonnet-base	4,687	14/14	$0.0478	0.62×
sonnet + concision	2,951	13/14	$0.0305	0.42×
sonnet cascade (below)	—	14/14	$0.0315	0.41×

Haiku 4.5 was 5× cheaper per token and cost 30% more per solved task than Opus, because it emitted 6.7× the tokens. Prices then: Haiku 4.5 $1/$5, Sonnet 5 $2/$10, Opus 5 $5/$25 per MTok. At Haiku 5.5's $0.50 output price the same 20,051 tokens would cost $0.010 a task, a fifth of Sonnet's $0.048. Whether Haiku 5.5 is as verbose as 4.5 on generation is unmeasured. It would have to emit about 95K tokens a task to lose on output cost.

Two questions before spawning

1. Is the output short or long? Short = a schema instance, a label, an answer, a small patch. Long = a module, a document, a plan, a review.
2. Is it mechanically checkable, or does it need judgment?

	short output	long output
checkable	haiku + verifier	haiku + verifier, informed retry on haiku, then sonnet @ high
judgment	sonnet @ medium	sonnet @ high, or opus when the work must be discovered

On Haiku 4.5, output length was the discriminator, because it is what the verbosity multiplier multiplies: Haiku 4.5's premium was invisible on a 200-token JSON object and ruinous on a 700-token module that cost it 13,000 tokens of thinking. At 5.5 prices verbosity no longer decides it, so the checkable column now starts on Haiku at any output length. The long-output cell is projected from price and from the repair battery, and has not been measured on generation.

The judgment row splits on whether the prompt names its deliverable or leaves the work to be found. Sonnet 5.5 finishes a named deliverable well. Given an audit-and-update goal, it listed findings, raised doubts and stopped to ask, and Oskar moved that session to Opus 5.5 mid-task (2026-10-07; one session, an observation, not a measurement).

Routing table

Task shape	Model	Effort	Verify with
Extraction, classification, format transforms, schema-bound output	haiku	n/a	schema / spot-check
Closed-form computation, state tracking, multi-hop lookup	haiku	n/a	deterministic check
Constraint-bound generation (exact counts, required tokens, lipograms)	haiku	n/a	mechanical checker
Bulk scans/greps, per-file summaries, fan-out reads	haiku	n/a	sample audit
Code edits with tests available	haiku → haiku informed retry → sonnet on a second failure	session	run the tests (the orchestrator's, not the worker's)
Code generation from a spec; any long structured artifact	haiku → sonnet @ high on a second failure	medium	run the tests (Haiku 5.5 unmeasured here)
Judging / scoring another model's output	sonnet+	medium	— (judge ≠ worker)
Ambiguity resolution, novel synthesis, architecture, taste	sonnet/opus	high	human or panel
Long-horizon multi-step agentic work, cross-file reasoning	sonnet/opus	high/xhigh	milestone checks

Haiku holds the top four rows on merit: 240/240 measured across nested modular arithmetic, 30-hop chains, 25-operation state tracking, trap-laden word math, and 5-constraint generation, some with CoT suppressed (references/calibration-2026-07-15.md). Those were Haiku 4.5 runs, which requested effort: low and ignored it, so the column says n/a. Haiku 5.5 does take effort (see below); until it is measured, leave it at its default. Do not up-tier short checkable work "to be safe"; there is no measured benefit and it costs 3–5×. The burden of proof is on routing up.

Haiku 4.5 lost the generation rows on cost alone, not capability: it scored 14/14 on the same suite Opus swept. At 5.5 prices that cost argument is gone (see the top of this file), so the generation rows now start on Haiku behind a test verifier, pending a generation-suite run on 5.5.

Effort is model-specific — verify per model before reusing a level

Measured 2026-08-17 via per-message output_tokens_details.thinking_tokens, thinking as a share of output on identical prompts:

model	low	medium
Sonnet 5	2.9%	47.7% (61.7% without concision)
Haiku 4.5	88–91%	88–91%

low was a near kill-switch on Sonnet 5: it dropped 14/14 → 10/14. Sonnet 5.5 is different. On the 14-repo seeded-bug battery (2026-09-29, two replicates, temporal-routing-headroom):

arm	solved (r1, r2)	output tokens, 14 tasks	$/completed
Sonnet 5 @ low (2026-09-03)	9/14	16,779	—
Sonnet 5.5 @ low	10, 11	11,466	$0.0104
Sonnet 5.5 @ medium	11, 12	10,853	$0.0090
Sonnet 5.5 @ high	12, 12	14,488	$0.0121
Opus 5.5 @ high	11, 13	18,464	$0.0284
Opus 5 @ high (2026-09-03)	10/14	55,674	$0.1392

• low and medium cost the same on Sonnet 5.5 for this work, and low missed only trap tasks. Claude Code's <reasoning_effort> tag agrees: 4 at low, 5 at medium.
• high bought the paired traps: lru_ttl and wrap_text went from 0 of 2 at low to 2 of 2 and 1 of 2.
• Sonnet 5.5 @ high matched Opus 5.5 @ high at 24 of 28 each, for 0.43× the cost per completed task. Seeded repair does not separate tiers, so this says the two are close on this family and nothing about harder work.
• The token counts are one replicate (budget deltas from r2); thinking share could not be measured, because Claude Code omits thinking text.

Effort does not reach Haiku 4.5. The Messages API rejects effort on it; Claude Code's Workflow agent() accepts the option and drops it (0 of 12 Haiku probes carried an effort tag or a transcript effort field). The identical 88–91% thinking share at low and medium above is that fact seen from the token side, and the ~26% token drop once attributed to low sits inside the 23% run-to-run gap measured below. Tune Haiku with the prompt; the routing table lists its effort as n/a.

Haiku 5.5 takes effort. The API accepts low through max on it, defaults to medium, and thinks adaptively by default; budget_tokens and non-default temperature are 400s. Whether Claude Code's Workflow agent({effort}) reaches a Haiku 5.5 subagent, and what low does to its pass rate, are both unmeasured. The 2026-10-07 ladder ran it at the session's level through the Agent tool.

• Tune Sonnet with the prompt as well as the knob. On Sonnet 5, medium was the working floor and low overshot into thinking-off; on Sonnet 5.5 low is a usable rung 1 and high is the rung that caught the traps.
• Buy depth only for judgment-heavy roles; drop triage and formatting roles to low without touching the expensive role's budget.

Setting effort — which channel reaches which agent

Measured in Claude Code on 2026-09-28 (muninn.austegard.com/blog/effort-levels-in-claude-code-subagents.html):

channel	reaches	notes
Workflow agent(prompt, {effort})	one subagent	24/24 Sonnet and Opus probes ran at the requested level while the session sat elsewhere. The only way a parent sets a subagent's effort.
Agent tool, SendMessage	nothing directly	No effort parameter on the call. A plain subagent runs at the session's level when it starts or resumes.
Agent tool with a named agent type	one subagent	Measured 2026-10-12 (tip from Craig Rungie): an agent definition with effort: in its frontmatter (.claude/agents/<name>.md, or --agents JSON) sets the effort of every Agent-tool call to that subagent_type; the subagent's transcript records it. With none set the request ran at medium. Sonnet 5.5 thinking on a reasoning puzzle, two runs each: low 481/666, medium 567/624, high 676/703 tokens. Keep one agent file per model-effort pair you use, e.g. sonnet-low.md, and pass subagent_type. Agent files load at session start.
claude -p --effort LEVEL	one headless child	Measured 2026-10-11 on Sonnet 5.5: low gave 0 thinking tokens, high gave 112–197 on the same prompt (2 runs each). Needs no workflow opt-in; dispatching-subagents runs these children in bulk.
Session setting (/effort)	main loop, and every subagent at its next turn	A resumed subagent picks up the new level. The model cannot change this itself; it recommends a level and the human sets it.
Managed Agents	agent config	Set on the agent, not per session: an effort inside a per-session model override is silently ignored. If the create response echoes effort: None, the org's beta header (managed-agents-2026-04-01) doesn't carry the feature and the field was dropped.

A Workflow run needs the user's opt-in (ultracode, an explicit request, or a user-invoked skill that names it). Without one, a subagent spawned through the Agent tool gets the session's level, and the routing table's effort column is advisory, unless the call names an agent type defined with effort: (row above) or the task can run as a headless claude -p child, which takes --effort directly.

This skill picks the model and effort. Which mechanism launches the child (Agent tool, Workflow, or headless children), and what the launching costs the parent, is dispatching-subagents.

The concision lever, and its limit

Adding one instruction — this is routine work; do not deliberate at length, do not enumerate test cases or weigh alternative designs; write it directly — cut output 37% on Sonnet and 27% on Haiku, at no quality cost. It composes with effort. Use it on every long-output generation spawn.

It does not reach work whose output is small. On agentic bug repair — a patch plus a paragraph — the same instruction cut Sonnet output 2.9%, at no quality change either way. The lever acts on deliberation the model would have written down, so a task that emits little has little to cut. Measure before carrying it to a new task family; "every long-output generation spawn" is the scope, and repair work is not in it.

Then stop. Thinking below a model's natural level is load-bearing, and cutting into it buys tokens with correctness:

• An engineered suppression prompt (positive framing, bounded budget, n-shot exemplar) cut Haiku 35% and halved its pass rate, 8/9 → 4/9. Within that arm, passing runs thought 1.9× more than failing runs.
• Sonnet at low (2.9% thinking) fell 14/14 → 10/14.
• Priced per passing result the suppressed arms were more expensive: 22,143 tokens vs 17,126 for the un-engineered prompt.

A targeted checklist ("enumerate the spec's rejection rules first") helps only when it names the actual failure mode: it took one validation-heavy task from 15,220 to 9,634 tokens at equal quality, and took a semantics-heavy task from 3/3 to 0/3. Misnaming the failure mode is worse than not intervening.

Cascade

Precondition, checked first: is the cheap tier actually cheaper per task? The first rung is never free, so a cascade pays only when the cheap tier's measured cost per completed task is below the destination's. Verbosity can erase a price discount outright — Haiku 4.5 at $0.067/task against Sonnet's $0.031 made haiku → sonnet worse than Sonnet alone regardless of p_fail: the attempt cost 2× the destination's entire job. Compute this before designing the ladder.

Haiku 5.5 passes this precondition. A Haiku 5.5 rung-1 spawn cost $0.0077 against $0.17 for a Sonnet 5.5 spawn, and it solved the same 11 of 14 repairs Sonnet 5.5 solves at low. A Haiku first rung now pays even when it fails often. A failed attempt is about 5% of the Sonnet job it precedes.

Second precondition: no verifier ⇒ no cascade. Route by the table instead; silent cheap-tier errors compound with nothing to catch them.

The verifier's holder makes the escalation call. Never the worker. A subagent asked whether it finished says yes: across 58 graded runs carrying an explicit "did you finish" field, 58 said yes and 44 had passed the held-out suite. Every one of the 14 failures self-reported success. The workers were not lying — they had passed the tests they could see, and those tests stay satisfiable while the task is unfinished. "Try it, and ask for help if you fail" therefore fails on exactly the tasks that need escalation. Put the decision wherever the stronger check lives; in a fan-out that is the orchestrator.

The shape that worked (measured, 14/14 at 0.41× Opus):

result = sonnet(task, effort=low, concise)          # rung 1: 10/14, $0.0155
if verify(result) fails:
    result = sonnet(task, effort=medium, concise,   # rung 2: fixed 12/12
                    prior=result, failure=test_output)

Haiku 5.5 ladder (measured 2026-10-07, 14 seeded-bug repos, one replicate):

result = haiku(task)                                   # rung 1: 11/14, $0.108 for 14 spawns
if verify(result) fails:                               # the orchestrator's hidden suite
    result = haiku(task, prior=diff, failure=output)   # rung 2: rescued 3/3, $0.015

14/14 for $0.123. Sonnet 5.5 at rung 2 rescued the same 3/3 for $0.511, 35× the per-spawn cost, and bought nothing more. All three rung-1 misses named the second defect in their own summaries and left it as "outside the reported bug". The informed retry supplies the evidence that the shortcut was wrong; the tier adds nothing to that.

On SWE-bench Verified the tier adds a little (measured 2026-10-09, 295 tasks, one sample per arm):

on Haiku's 39 rung-1 misses	resolved	rung cost
haiku, issue text only (blind re-roll)	8/39	$1.05
sonnet, issue text only	17/39	$10.34
haiku, prior diff + failing tests' output	30/39	$1.43
sonnet, prior diff + failing tests' output	34/39	$13.53

Over the whole ladder, haiku → haiku resolved 286/295 for $8.49 and haiku → sonnet 290/295 for $20.59, so Sonnet's 4 extra tasks cost about $3 each. Sonnet's set contained the Haiku retry's, so Sonnet belongs at the third rung: haiku, haiku informed, then sonnet only on what the retry still fails. That sends Sonnet 9 tasks instead of 39. The three-rung order is a projection, not a measurement: the Sonnet arm started from the rung-1 diff, not the rung-2 one.

The failing tests' output in that run was SWE-bench's hidden suite, an oracle no deployed ladder has. Read the informed-retry rows as the ceiling for whatever verifier the orchestrator holds; the Haiku-versus-Sonnet gap between them is a fair comparison, because both arms got identical input.

Rung 2 is the same model one effort step up. A tier jump is the exception you justify. Measured twice. On a second battery (14 seeded-bug repos, 2026-09-03) rung 2 ran from an identical failed attempt at both settings: sonnet @ medium and opus @ high rescued the same 4 of 5 tasks and both missed the same fifth, at 11,691 against 32,504 output tokens. Composed over the same rung 1, the same-model cascade cost 0.31× always-opus and the tier jump 0.76×. The tier jump costs 2.5× and buys nothing.

Those rungs are Sonnet 5. On Sonnet 5.5, low and medium solved the same tasks at the same cost, so the step that changes anything is low → high: high is where the paired traps started getting caught (see the effort table above). The cascade itself has not been run on 5.5.

A cascade can beat the frontier solo arm on correctness, not only on cost. In that run the sonnet→sonnet cascade solved 13/14 where always-opus solved 10/14. opus starting from the issue text fell into the same stop-early trap as sonnet on three tasks; opus starting from the failed patch and the failing assertions fixed all three.

How to run rung 2 in Claude Code. For a subagent, launch a fresh Workflow agent() one effort step up and pass it the diff and the test output; Agent and SendMessage cannot change a subagent's effort. For the main loop, recommend the next level and let the human set /effort; the session keeps its cache and carries on.

Caching pushes the same way. Caches are model-scoped, so a tier jump discards rung 1's prefix. An effort change does not: in Claude Code on 2026-09-28, five subagent resumes across a level change (Low→Medium, High→Extra, Extra→Max) all read their full prefix from cache, and the parent read 97,839 tokens from cache on its first request after a change. On the API the per-message effort message ({"role": "system", "content": [], "output_config": {"effort": …}}, beta mid-conversation-output-config-2026-07-01) keeps the cache on Fable 5.1, Mythos 5.1, Opus 5, Opus 5.5 and Sonnet 5.5 with thinking on; a change to the top-level effort still invalidates the messages cache.

The TTL is what misses. Every miss in that experiment followed a gap longer than five minutes. Subagents write only to the 5-minute tier and the parent to the 1-hour tier, so a subagent resumed after five idle minutes rebuilds its whole prefix (about 54k tokens for a bare general-purpose agent). Resume a subagent for a quick retry; after a longer gap, start a fresh one, since same-model agents share a cached prefix (32 of 36 fresh probes read from cache on their first request).

Every figure in this skill prices output tokens only; input and cache effects sit outside its cost model.

Carry the prior attempt and the raw failure output into the retry. Informed retry fixed 12/12; a blind re-attempt fixed 9/12 and failed one task identically across all three replicates — a systematic blind spot re-rolling never escapes. On SWE-bench Verified the gap is wider: a Haiku retry with the failing tests' output fixed 30/39 of Haiku's misses, a blind Haiku re-roll 8/39, and Sonnet from the issue text alone 17/39. Neither blind arm solved a task its informed counterpart missed. The failure output moved 22 tasks for Haiku; switching to Sonnet moved 9 without it and 4 with it. The extra input averaged 866 tokens, 5.9% of the retry's cost. Input is 1/5 the price of output, so context is nearly free relative to thinking.

The artifacts, not the prior model's account of itself. Adding rung 1's stated diagnosis on top of the patch and the assertions did nothing: 13/15 against 12/15 over three replicates, 1% fewer output tokens, and the whole difference was one replicate of one unstable task. It does not help and it does not anchor — SWE-Router (arXiv 2607.00053) restarts its strong model from the task description to avoid an anchoring effect that is not there. Pass the diff and the test output; skip the rationale.

Don't pay a frontier model to write guidance. An Opus diagnosis added zero over raw test output in two independent tests, at ~$0.15/task. The failing test already says what the orchestrator would say.

Verify content, not envelope. Strip fences, preambles, and trailing commentary before checking; hard-fail only on semantic content and log envelope deviations as soft. Two Haiku runs returned 7/7 and 6/6 correct fields while both wrapping output in a markdown fence the prompt forbade — a verifier keying on raw.startswith('{') would have escalated both for zero content error. Spurious escalation is a cascade failure mode, not a safety margin.

Judgment tasks fail in a shape checkers miss. Asked to rebut a stakeholder's "spend is down 66%" off a partial-month extract, Haiku killed the bad conclusion but normalized per calendar day across a 40%-weekend window and missed a model-mix confound — while passing every mechanical check available (word count, prose form, internal arithmetic consistency). The cheap tier fails as right headline, missed confound. This is why judgment rows route up rather than cascade.

Context handoff — routing picks the tier; the prompt carries the context

Subagents inherit nothing: not the conversation, not loaded skills, not the existence of artifacts already on disk. Every index, scan output, artifact path, or tool recipe must be serialized into the prompt (or a file the prompt points at). Otherwise the agent falls back to blind rediscovery and the tier premium is spent on crawling. A Sonnet with no handoff wastes more than a Haiku with a good procedure.

Per spawn: (1) artifact paths + how to query them, (2) tool commands verbatim, interpreter path included — subagents don't know your venv, (3) explicit anti-patterns ("no ls/glob discovery"), (4) an output spec.

Evidence: 2026-07-16, four Sonnet Explore agents launched onto a 2,300-file repo without the handoff opened with ls crawls despite a full tree-sitter symbol index sitting on disk; relaunched with per-agent index slices, the verbatim command, and anti-crawl rules, discovery cost dropped to ~zero.

To convert a judgment-shaped task into a cheap-tier-executable one (explicit procedures, n-shot examples), use the sibling down-skilling skill. This skill decides the routing; that one engineers the prompt.

Shared-prefix caching cuts the fan-out multiplier (unmeasured, conditional). When N subagents share a byte-stable prefix — the fixed handoff, not the per-agent slices — prefix caching can pull that portion toward a read-discount rate where the orchestration surface exposes it. Keep per-agent content at the tail. Verify your surface caches subagent prefixes before relying on it.

Loop discipline

Never blind-loop. Re-applying a prompt to a model's own output is the identity at best — an LLM call already unrolls its reasoning internally — and regression-then-freeze at worst: a re-looped haiku broke its own middle line on iteration 2 and froze on the broken text for every iteration after.

1. Loop only with an out-of-band evaluator — ground truth, mechanical checker, or an up-tier judge scoring every iteration.
2. Select, don't trust the last: final = argmax_r eval(answer_r).
3. Stop on first regression. If eval(r) < eval(r-1), stop; loops froze on degraded output rather than recovering.
4. Loop for diversity, not depth. Vary the angle per iteration; identical re-application converges instantly.
5. "Improve this" with no headroom is the danger zone. It pressures the model to change something; without a selector, that change ships.

Judge rules

• Judge model ≠ worker model; judge at least one tier up. Same-model self-assessment is untested.
• Prefer mechanical checkers wherever a spec can be executed (counts, schemas, tests, regex): free, deterministic, zero judge tokens.
• Judges are for rubric quality, not arithmetic — don't ask a model to verify a sum a Python one-liner can check.

Escalation triggers (route up despite the table)

• The prompt names a goal rather than a deliverable (audit-and-update, follow-through across repos, "find what's outdated and fix it"). Sonnet 5.5 tends to stop and ask where Opus 5.5 carries on (observed 2026-10-07).

• The verifier fails twice at the same tier. Route up for capability, not for thoroughness — opus @ high fell into the same stop-early trap as sonnet @ low on three of four tasks built to reward a second look. A verifier catches that; a bigger model does not.

• The task requires weighing trade-offs with no checkable ground truth.

• Output ships verbatim to a human without review.

• The subagent must plan its own multi-step tool strategy over many turns.

• The task spans multiple sources that may disagree and must be reconciled.

Observing the fan-out — you can't govern what you can't watch

Stop-on-regression and "verifier failed twice" assume you can see a subagent's work while it runs. By default you can't: the session stream previews only the primary thread, and a subagent's output lands only after its whole turn buffers.

Attach one stream per thread. Read the session stream for the coordinator; on every session.thread_created (carrying session_thread_id and agent_name), attach a watcher to GET /v1/sessions/{id}/threads/{thread_id}/stream with event_deltas.

• Preview is a scratch buffer; the buffered event is the record. Deltas are best-effort and shed under load, so concatenated deltas are a prefix of the final text. Reconcile by a single replace when the buffered agent.message arrives; the SDK's accumulate_managed_agents_event folds start/delta/record into one snapshot. One accumulator per connection. (The same trap appears offline: per-message usage records in transcripts include streaming partials — take the max per message id, or you undercount tokens ~2×.)
• No replay. A stream opened after a request started gets no deltas for it, and reconnects never replay — attach on thread_created or miss the first response.
• Coordination events live on the primary thread — session.thread_created, agent.thread_message_sent, agent.thread_message_received. Child tool calls cross-posted to the primary carry session_thread_id; skip them.
• Terminate cleanly. Watchers exit on session.thread_status_idle; the main loop on session.status_idle — print the stop reason when it isn't end_turn, and break on terminated-status events.

Operational, not calibrated. Source: Anthropic Managed Agents notebook CMA_watch_subagents_live (beta managed-agents-2026-04-01); contract in events and streaming.

Measure before trusting this

Everything above is measured on four batteries: a 300-call deterministic calibration (references/calibration-2026-07-15.md), a 14-task hidden-test coding suite (2026-08-17, ~190 subagent runs), and a 14-repo seeded-bug agentic battery (2026-09-03, ~120 subagent runs, oaustegard/experiments → temporal-routing-headroom) that measured the cascade rungs, the escalation signal, and the tier gap against each other, rerun on 2026-10-07 with Haiku 5.5 at both rungs and priced per spawn from the subagent transcripts (harness/subagent_cost.py there), and 295 SWE-bench Verified django and sympy tasks graded Docker-free (2026-10-09, ~530 subagent runs, oaustegard/experiments → swe-ladder). Re-measure when:

• A model or price revision lands. Both the verbosity multipliers and the cost table above invert on either. Sonnet 5.5 (2026-09-29) and Haiku 5.5 (2026-10, 10× cheaper than Haiku 4.5) are such revisions. Haiku 5.5 is measured on the seeded-bug battery only; its verbosity on the generation suite, and what effort does to it, are not. Sonnet 5.5's effort levels are measured above on the seeded-bug battery only; the other Sonnet figures in this skill are Sonnet 5 data, and the verbosity multipliers and the generation-suite costs have not been re-run on the 5.5 generation.
• The task family is off all four batteries. No deterministic task has made Haiku fail on correctness yet, so the capability cliff is past what's been probed. Seeded-bug repair in a small module is now measured as not tier-separating: three probe shapes aimed at thoroughness, at ambiguity the tests underdetermine, and at a repo with no test suite at all, and sonnet @ low solved all six cells against opus @ high. Real repair separates the tiers modestly: on SWE-bench Verified, Sonnet 5.5 alone is estimated at 92.5% against Haiku 5.5's 86.8%, with all of the gap on the tasks Haiku misses (Sonnet solved 50/50 sampled Haiku successes).
• Output length differs materially from what was measured. The whole cost model keys on token volume; a 10× longer artifact re-opens the tier question.
• You need pass-rate differences of 1–2 tasks. Run-to-run variance swamps them: two runs of the same model on the same 14 tasks produced disjoint failure sets and a 23% token gap. Token deltas are trustworthy; small pass-rate deltas are not.