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The Model Tiering Guide

How to combine Fable 5, Opus 4.8 and Sonnet 5 in Claude Code so the premium tokens only go where judgment matters. Planning and reasoning stay on the strong models, volume work goes to the cheap ones, and a packaged workflow does the routing for you. Up to 70% lower token spend.

Prices checked against Anthropic's pricing page, September 2026. The workflow file is on page 5.

The idea in one paragraph

Fable 5 is the strongest model available, and also the most expensive one. But in any large task, most tokens are burned on volume work: reading files, summarizing, mechanical edits, boilerplate tests. A mid tier model does that work just as well at a fraction of the price. The structure in this guide splits every big task into phases, routes each phase to the cheapest model that can do it well, and reserves Fable for the few moments where its judgment changes the outcome: planning, tricky debugging, conflict resolution. Quality is protected by an adversarial Opus review loop at the end. The blended bill drops sharply while the output stays at Fable level where it counts.

What each tier costs
MODEL MODEL ID INPUT $/1M OUTPUT $/1M VS FABLE
Fable 5claude-fable-5$10.00$50.00baseline
Opus 4.8claude-opus-4-8$5.00$25.0050% less
Sonnet 5claude-sonnet-5$2.00$10.0080% less
Haiku 4.5claude-haiku-4-5$1.00$5.0090% less

Anthropic API list prices per million tokens, checked September 2026. Sonnet 5 launched at $2 / $10 as an introductory price, and Anthropic has since made that its standard price.

Where the "up to 70%" comes from

Every token moved from Fable to Opus costs 50% less, to Sonnet 80% less, to Haiku 90% less, on both input and output. A hard task with several features run through the tiered pipeline keeps its planning and implementation on Fable, so it lands lower: typically 45 to 55% under an all Fable run (worked example on page 4). Routine work delegated entirely to Sonnet saves the full 80%. Across a week of mixed work the blended saving sits between those two, which is why the structure is summarized as up to 70% less.

The second lever is effort: implementers run at medium effort instead of high, which produces fewer thinking and output tokens on the same model without touching the plan quality.

The tier map: who does what
Pick the cheapest tier that can do the job well.

When torn between two tiers, take the cheaper one for reversible work (a weak exploration report costs one retry) and the stronger one for work that is hard to undo (a bad merge costs an afternoon).

TIER USE IT FOR NEVER USE IT FOR
Haiku 4.5 Mechanical sweeps: file listing and renaming, log parsing, screenshot capture, format conversion, running scripted QA checks. Anything that needs code judgment.
Sonnet 5 Codebase exploration, ordinary implementation, test writing, routine QA, summarizing, first drafts of docs. Architecture decisions, subtle debugging.
Opus 4.8 Adversarial code review, verification passes, judging between options. Work Sonnet already handles (Opus costs 2.5x Sonnet).
Fable 5 Planning and task decomposition, design judgment, tricky debugging, merge conflict resolution, orchestration decisions. Mechanical work of any kind.
Two standing rules
  1. Do small work inline. Spawning an agent has overhead: the agent reads again the context the main session already holds. Delegation pays off from roughly three files to touch, or two minutes of reading, upward. Below that, doing it directly is both faster and cheaper.
  2. Never delegate judgment down a tier to save money. A wrong plan from a cheap model costs more in rework than the Fable tokens it saved. The savings come from volume work, not from judgment work.
Effort levels: the second lever

Effort controls how deeply a model thinks on a given call (low, medium, high, xhigh, max). It works on the same model, so it stacks with tiering. In the workflow it is the effort option on each agent call.

EFFORT WHEN WHY
high Planning, review, anything where intelligence matters. Judgment work deserves full depth.
medium Implementation against a written plan with concrete acceptance criteria. The plan already carries the judgment; the implementer executes it.
low Mechanical steps: opening a PR, running a checklist. Fewer, more consolidated tool calls and terser output.
THE COMBINATION IS THE TRICK Tiering picks the model. Effort picks how hard that model thinks. A Fable implementer at medium effort, working from a plan Fable wrote at high effort, keeps the quality of the plan and skips the thinking it does not need. That pair is where most of the pipeline's saving on the implementation phase comes from.
The packaged pipeline: fable-five-dynamic

For hard coding tasks with several parts, the tiering is packaged as a Claude Code workflow (the file .claude/workflows/fable-five-dynamic.js, download on page 5). It splits the task into features with explicit dependencies, implements them in parallel git worktrees, and loops review failures back to the implementers until everything passes or the loop cap is hit.

Explore
SONNET 5
parallel, read only
Plan
FABLE 5
effort: high
Implement
FABLE 5
effort: medium
Merge
FABLE 5
effort: medium
Review
OPUS 4.8
effort: high
PR
FABLE 5
effort: low
review failures loop back to Implement as fixes (bounded, default max 3) · one model tier per phase, judgment stays on Fable
PHASE MODEL / EFFORT WHY THIS TIER
ExploreSonnet 5, parallel read only agents, one per angleReading and reporting is volume work; 80% cheaper per token.
PlanFable 5, effort highThe one place where judgment sets up everything downstream.
ImplementFable 5, effort medium, one git worktree per featureThe plan carries the judgment; medium effort executes it with fewer tokens.
MergeFable 5, effort mediumConflict resolution needs care but not deep planning.
ReviewOpus 4.8, effort high, one adversarial reviewer per featureA strong reviewer at half the Fable price; failures loop back, bounded by maxLoops (default 3).
PRFable 5, effort lowMechanical.
How to run it

In a Claude Code session inside a git repository, ask for the workflow by name ("run the fable-five-dynamic workflow on: add a user settings page"), or invoke it directly:

Workflow({ name: "fable-five-dynamic", args: { task: "[WHAT TO BUILD]" } })

Useful arguments: angles overrides the four default exploration angles; maxLoops caps the review and fix loops (default 3); mode is "merge" (integrate branches, default) or "separate" (leave one branch per feature); openPR opens a GitHub PR when a remote is configured; branch names the integration branch. Preflight commits a clean baseline automatically before any worktree is created.

ONE CAVEAT Only use the pipeline when the task genuinely breaks into several features. A single file fix pushed through a seven phase pipeline costs more than doing it inline.
Delegating outside the workflow

The same map applies to individual subagents and one off workflow scripts:

Where the numbers land: a worked example

A four feature task, counting output tokens (the dominant cost; input tokens scale the same way at each tier):

PHASE TOKENS (APPROX.) ALL FABLE, HIGH TIERED
Explore (4 agents)200k$10.00$2.00 (Sonnet)
Plan30k$1.50$1.50 (Fable high)
Implement (4 features)350k high / 240k medium$17.50$12.00 (Fable medium)
Review (4 reviewers)160k$8.00$4.00 (Opus)
Total$37.00$19.50 (47% less)

The pipeline saves close to half on a hard task because planning and implementation deliberately stay on Fable. Routine standalone work routed entirely to Sonnet saves 80%, and Haiku sweeps save 90%. Across a week of mixed work the blended saving sits between those bounds, which is why the structure is summarized as up to 70% less.

Quick checklist
SITUATION DO THIS
Coding task with several partsRun the fable-five-dynamic workflow.
Search or read heavy questionSonnet explorer agent, structured output.
File sweep, log parsing, captureHaiku agent.
Ordinary coding or QA in isolationSonnet agent.
Verifying or judging finished workOpus reviewer, effort high.
Plan, architecture, gnarly bug, mergeFable, inline or effort high.
Tiny change (under about 3 files)Do it inline; do not spawn anything.
Install the workflow
Two minutes, once per repository.
  1. Download the workflow file

    Open the link below (it opens as plain text) and save it as fable-five-dynamic.js, or run this one line from the root of your repository:

    mkdir -p .claude/workflows && curl -o .claude/workflows/fable-five-dynamic.js https://easyhive.pro/lm-tokens-step1/fable-five-dynamic.js

    Direct link: easyhive.pro/lm-tokens-step1/fable-five-dynamic.js

  2. Check it landed in the right place

    Claude Code loads workflows from .claude/workflows/ inside the repository you are working in. The file name is the workflow name, so keep it as fable-five-dynamic.js.

  3. Run it on a real task

    Start Claude Code in that repository and ask: "Run the fable-five-dynamic workflow on: [WHAT TO BUILD]". Describe what done looks like in the same message. Watch the phases with /workflows.

  4. Read the result before you merge

    The run ends with an integration branch (fable-five/your-task), the merge and test status and, when review passed and a remote exists, a pull request. Review it like any PR from a colleague.

WHAT YOU NEED A git repository (preflight creates a baseline commit if the working tree is dirty). For the PR step, a GitHub remote and gh authenticated. The file contains no keys, no accounts and no secrets; it only decides which model runs which phase and at what effort.
Your next hour
MINUTES DO THIS
0 to 5Drop the file into .claude/workflows/ in one repository.
5 to 15Pick a task that genuinely has three or more features. Write the request in one paragraph, including what done looks like.
15 to 50Run the workflow. Do not intervene; let the review loop do its job.
50 to 60Compare the cost of the run with your last all Fable session of a similar size. Write both numbers down; that is your real saving.
THE POINT The savings come from the volume work, never from the judgment. Keep the plan and the review on the strong models, push everything else down a tier, and the bill takes care of itself.

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