What Is the Anthropic Advisor Strategy? How to Use Opus as an Adviser With Sonnet or Haiku

The Case for Splitting Brains and Budgets

Running every task through Claude Opus is expensive. Running everything through Haiku is cheap but sometimes not good enough. The Anthropic advisor strategy offers a third path: let a powerful model think, and let a cheaper model act.

The idea is straightforward. Opus — Anthropic’s most capable Claude model — acts as an adviser. It analyzes the task, reasons through it, and provides structured guidance. Sonnet or Haiku then takes that guidance and executes. The result, according to Anthropic’s own research, is a setup that can cut costs by around 11% compared to using Opus throughout, while actually improving benchmark performance in certain task types.

This article breaks down exactly what the advisor strategy is, how it works, when to use it, and how to implement it in a real workflow — including how MindStudio makes it easy to set up without managing the plumbing yourself.

What the Anthropic Advisor Strategy Actually Is

The advisor strategy is a multi-model architecture pattern where two different Claude models play distinct roles in completing a task.

The adviser (Opus): Receives the task or problem, reasons through it, and returns a detailed plan, set of instructions, or intermediate output to guide the next step.
The executor (Sonnet or Haiku): Receives the adviser’s output and uses it to complete the actual work — drafting text, generating code, answering user queries, filling in a form, etc.

Think of it as separating planning from doing. Opus is expensive because it’s deeply capable at complex reasoning. But not every token in a workflow requires deep reasoning. Execution — once a clear plan exists — is often within reach of a smaller, faster, cheaper model.

This pattern differs from simple prompt chaining. In prompt chaining, you pass raw outputs between steps. In the advisor strategy, Opus is explicitly positioned as a reasoning layer that reduces ambiguity before the cheaper model acts. The smaller model doesn’t have to figure things out — it just follows well-structured guidance.

Why Anthropic Built This In

Anthropic didn’t invent the concept of model collaboration, but they formalized it within their own model ecosystem. Their research into multi-agent architectures showed that orchestrating models of different capability levels could outperform using a single model throughout — particularly when the task involves both complex reasoning and high-volume generation.

The advisor pattern emerged from the observation that expensive model capacity is wasted on routine execution steps, while cheaper models underperform when forced to handle ambiguous or complex initial reasoning. Splitting the work by type — not just by step — improves both efficiency and output quality.

How the Strategy Works in Practice

At the implementation level, the advisor strategy involves two sequential model calls with a specific role division.

Step 1: The Adviser Call (Opus)

You send the original task to Opus with a system prompt that frames its role as an adviser, not an executor. Opus doesn’t produce the final output. Instead, it produces:

A breakdown of the task into clear sub-steps
Key constraints, edge cases, or context the executor should know
A structured plan or set of instructions in a format the next model can easily follow

The system prompt matters here. You want Opus thinking like a senior analyst handing off to a junior colleague — detailed, specific, and anticipating gaps.

Example system prompt for Opus:

You are an expert advisor. Your job is to analyze the task below and produce a clear, structured plan that a capable but less experienced assistant can follow to complete it accurately. Do not complete the task yourself. Output a numbered action plan with relevant context, constraints, and examples where useful.

Step 2: The Executor Call (Sonnet or Haiku)

You pass Opus’s output — the plan or advisory notes — as context to Sonnet or Haiku. The executor’s system prompt frames it as implementing the plan, not interpreting the original problem.

Example system prompt for Sonnet/Haiku:

You are an expert assistant. You have been given a structured plan from a senior advisor. Follow the plan precisely to complete the task. Do not deviate from the guidance provided unless the instructions explicitly allow for judgment calls.

The original task description can also be included for reference, but the executor’s attention is anchored to the adviser’s plan.

What Gets Passed Between Steps

The handoff content is critical. Opus’s output should be:

Structured — numbered steps or clear sections, not a wall of text
Specific — concrete instructions, not vague direction
Complete — covers edge cases the executor might otherwise mishandle
Scoped — doesn’t try to do the work, just prepares the executor for it

If Opus produces something too vague, Sonnet or Haiku will fill gaps with its own assumptions — and those assumptions may not match what you wanted. Tight adviser output leads to tight execution.

When This Strategy Makes Sense

The advisor pattern isn’t the right fit for every use case. Here’s where it performs best and where it may not be worth the added complexity.

Good Fits

Complex, multi-part tasks: When a task requires decomposing a problem before executing it — writing a technical report, generating structured data from messy input, building a multi-step reasoning answer — Opus’s planning ability pays off.

High-volume, consistent outputs: If you’re running the same type of task at scale (summarizing hundreds of documents, drafting email responses, classifying customer tickets), using Haiku for execution and Opus only for the initial advisory layer is significantly cheaper than running Opus on every item.

Tasks requiring nuanced judgment up front: Legal document review, nuanced tone decisions, policy interpretation — Opus sets the framework, Haiku applies it repeatedly.

When Haiku alone underperforms: If you’ve tested Haiku on your task and outputs are inconsistent or miss context, wrapping Haiku in an Opus-advised workflow often closes the quality gap without jumping to full Opus usage.

Less Ideal Fits

Simple, well-defined tasks: If the task is unambiguous — “translate this sentence,” “format this JSON,” “check this math” — adding an adviser layer adds latency and cost with no benefit.

Real-time, latency-sensitive apps: Running two sequential model calls adds response time. For chatbots or live user interactions, this may create a noticeable lag.

Tasks where execution requires the same depth as planning: Some tasks — complex code generation with unusual constraints, highly creative long-form writing — may need Opus for both thinking and doing.

Implementing the Advisor Strategy Step by Step

Here’s a concrete implementation walkthrough. This applies whether you’re building in code, using a workflow tool, or testing in a UI.

Step 1: Define the Task Clearly

Before building anything, write a precise description of what the full task is. Include:

The input (what data or prompt the user provides)
The desired output (format, length, style, accuracy requirements)
Known constraints or edge cases
Examples of good and bad outputs if available

The clearer the task definition, the better Opus can advise on it.

Step 2: Write the Adviser Prompt

Your Opus system prompt should make its role explicit. Avoid prompting Opus to produce the final answer — that defeats the purpose. Key elements:

Tell Opus it is advising, not executing
Ask for structured output (numbered steps, sections)
Specify what the executor will need to know
Optionally, ask Opus to flag anything ambiguous or risky

Test this prompt in isolation. Check that Opus produces clean, actionable plans — not rough notes or partial answers.

Step 3: Build the Executor Prompt

Your Sonnet or Haiku prompt should:

Set the role as “implementer following a plan”
Include Opus’s output as a system or context message
Reference the original task for grounding
Specify output format explicitly

One common mistake: over-explaining the task to the executor. If Opus’s plan is good, the executor doesn’t need much additional context. Over-explanation can confuse it.

Step 4: Test the Two-Step Flow

Run the full pipeline on 5–10 representative inputs. Evaluate:

Does Opus’s advisory output stay focused and structured?
Does the executor’s output meet the standard you’d expect from Opus alone?
Are there task types where the executor still underperforms despite good adviser output?

Adjust either prompt based on failure patterns. Often, the issue is that Opus is too verbose or too abstract in its advice — tighter adviser prompts usually fix execution quality.

Step 5: Optimize for Cost

Once the two-step flow works, decide which executor model fits your cost/quality target:

Sonnet — solid performance on most tasks, moderate cost, good for outputs that need to be high quality
Haiku — fastest and cheapest, suitable for simpler execution tasks or high-volume pipelines where some output variance is acceptable

Run a cost estimate based on your expected token volumes. Compare to the cost of running Opus throughout. The savings typically range from 10–30% depending on task complexity and volume, with the advisor pattern often delivering comparable or better benchmark results than single-model Opus.

Cost Breakdown: What You Actually Save

Anthropic’s Claude models have significantly different pricing tiers. As of mid-2025, approximate pricing per million tokens:

Claude Opus 4: Higher cost per token, strongest reasoning
Claude Sonnet 4: Mid-tier cost, strong general performance
Claude Haiku 3.5: Lowest cost, fast response, lighter reasoning

In a typical advisor strategy workflow, the Opus adviser call handles the reasoning-heavy portion — which tends to be shorter in token count. The executor call handles the longer generation portion — which runs on a cheaper model.

For a task that might cost 4,000 tokens end-to-end on Opus alone:

Opus adviser call: ~800 tokens (reasoning, plan generation)
Haiku executor call: ~3,200 tokens (execution based on the plan)

Depending on the exact pricing difference between Opus and Haiku, the savings on the executor portion can be substantial. Across thousands of runs, the cumulative cost difference becomes meaningful — especially for teams running AI at scale.

The 11% figure cited in Anthropic’s research represents a baseline comparison on specific benchmarks. Your actual savings will vary by use case, but the directional result holds: reserving Opus for the planning layer and using a cheaper model for execution consistently reduces per-task cost without a corresponding quality drop.

How to Build This in MindStudio

MindStudio’s visual workflow builder makes the advisor strategy straightforward to implement — no code required, and no separate API keys or account management for each model.

You can build the two-step advisor flow as a sequential workflow:

First AI block: Configured with Claude Opus, using your adviser system prompt. The block takes the user input and produces a structured plan.
Second AI block: Configured with Claude Sonnet or Haiku, using your executor system prompt. The block receives Opus’s plan as part of its context and produces the final output.

MindStudio handles the model routing, API calls, and token passing between steps. You configure the prompts, connect the blocks, and test the flow — all in the visual editor.

This is particularly useful for teams running high-volume pipelines. If you’re summarizing customer feedback at scale, generating product descriptions from structured data, or drafting outbound emails from CRM records, you can build a workflow that runs the full advisor pattern automatically — triggered by a schedule, a webhook, or a connected tool like HubSpot or Airtable.

Because MindStudio includes 200+ AI models in one place, you can also test different executor models quickly. Swap Sonnet for Haiku, compare outputs, and optimize without changing your infrastructure setup.

If you want to try it, MindStudio is free to start at mindstudio.ai. Building a two-step advisor workflow typically takes under an hour with the visual builder.

For more on how to structure multi-step AI workflows, the MindStudio workflow documentation covers sequential, conditional, and loop-based patterns that pair well with the advisor strategy.

Common Mistakes and How to Avoid Them

Letting Opus Do Too Much

The most common error is prompting Opus to produce a “plan and then also a draft.” Once Opus starts executing, you’ve lost the cost benefit. Keep the adviser role strictly in the planning lane.

Passing Raw Opus Output Without Formatting

If Opus produces a rambling advisory response, Sonnet or Haiku will struggle to extract clear instructions from it. Add output formatting requirements to the Opus prompt — ask for numbered steps, bullet points, or structured JSON if your task benefits from it.

Using Haiku When the Task Needs Sonnet

Haiku is very capable, but some tasks — nuanced writing, complex code, multi-step reasoning even with a good plan — need Sonnet’s stronger execution capabilities. Start with Sonnet as executor, then test Haiku if cost pressure requires it.

Skipping Evaluation

Running the advisor pattern without comparing outputs to a baseline (Opus alone, or Sonnet alone) means you won’t know if it’s actually working. Build a small evaluation set, run both approaches, and compare on your specific quality criteria.

Over-Engineering the Adviser Prompt

Some teams try to make the adviser prompt do everything — account for every edge case, produce every possible guidance variation. This leads to verbose, unfocused adviser output. Keep it targeted to the task at hand.

Frequently Asked Questions

What is the Anthropic advisor strategy?

The Anthropic advisor strategy is a multi-model workflow pattern where Claude Opus acts as a reasoning adviser — breaking down a task and producing a structured plan — while a cheaper model like Claude Sonnet or Haiku executes the task based on that plan. It’s designed to reduce inference costs while maintaining or improving output quality compared to using Opus alone.

Does the advisor strategy actually save money?

Yes, in most cases. Since Opus is significantly more expensive per token than Sonnet or Haiku, routing only the planning step through Opus and handling execution with a cheaper model reduces overall cost. The savings depend on your task profile, but Anthropic’s own benchmarks show around an 11% cost reduction with comparable or improved performance on certain task types.

When should I use Sonnet vs. Haiku as the executor?

Use Sonnet when your execution task still requires solid language quality, nuanced tone, or complex generation. Use Haiku when the task is simpler, more templated, or the volume is high enough that the cost difference matters significantly. Test both on your specific use case — the right choice depends on your acceptable quality threshold.

Can I use this pattern with other Claude models, or only Opus?

The pattern works with any adviser/executor pairing where the adviser model is significantly more capable. You could use Claude Sonnet as the adviser and Haiku as the executor for less complex tasks, though the quality gap between Sonnet and Haiku is smaller than between Opus and Haiku, so the benefit is less pronounced.

How is the advisor strategy different from prompt chaining?

Prompt chaining passes outputs from one step to another sequentially. The advisor strategy is a specific version of this where the first model’s role is explicitly to reason and plan, not to execute. The framing matters: Opus is given a distinct “adviser” role and prompted not to produce final outputs. This produces better planning artifacts than simply asking Opus to “think step by step” before generating.

Does this work for agentic or multi-step workflows?

Yes, and this is where the advisor pattern becomes especially useful. In agentic workflows where an AI takes multiple actions — searching the web, reading documents, generating outputs, calling APIs — using Opus to plan the sequence of actions and a cheaper model to execute each step can significantly reduce cost while keeping the overall agent behavior coherent and effective.

Key Takeaways

The Anthropic advisor strategy pairs Claude Opus as a reasoning adviser with Sonnet or Haiku as a cheaper executor, separating planning from doing.
Anthropic’s research shows the pattern can reduce costs by around 11% while maintaining or improving benchmark performance on targeted task types.
The Opus adviser call handles complex reasoning and produces a structured plan; the executor call follows that plan without needing to re-reason from scratch.
The pattern works best for complex, multi-part, or high-volume tasks — and less well for simple, unambiguous, or real-time tasks.
Common mistakes include letting Opus do too much, passing unformatted adviser output, and skipping comparative evaluation.
MindStudio makes building this two-step advisor workflow easy with its visual builder — just configure two AI blocks, set the models and prompts, and connect them in sequence.

If you want to try building an advisor-pattern workflow without writing any infrastructure code, MindStudio is free to start. You can have a working two-model pipeline running in under an hour.