How to Use a Multi-Model AI Coding Workflow: Fable for Planning, Composer for Execution, GPT for Review
Using different models for planning, implementation, and review cuts costs and speeds up delivery. Here's how to build a multi-model skill in Claude Code.
Why One Model Isn’t Enough for Serious Coding Work
Most developers using AI for coding pick one model and stick with it. That’s understandable — switching between tools is friction, and friction kills momentum.
But treating every task in a coding session the same way is like using a hammer for everything. Planning a system architecture, writing 300 lines of business logic, and reviewing a pull request for security issues are fundamentally different cognitive tasks. Different models are built for different kinds of thinking.
A multi-model AI coding workflow — where you deliberately route tasks to the model best suited for each phase — cuts costs, improves output quality, and gets features shipped faster. The pattern is straightforward: use Fable (or a reasoning-heavy model) for planning, Composer (or a code-generation-optimized model) for implementation, and GPT-4 for final review.
This article explains why that split works, how to build it inside Claude Code, and what to watch out for as you scale it.
The Core Problem with Single-Model Coding Workflows
When developers use one model for everything, two things tend to happen.
First, they overpay. High-capability frontier models like Claude Opus or GPT-4o are expensive per token. Using them to autocomplete a loop or format a config file is wasteful — that’s work a cheaper, faster model handles just as well.
Everyone else built a construction worker.
We built the contractor.
One file at a time.
UI, API, database, deploy.
Second, quality suffers at the seams. A model that’s great at fast code generation often makes shallow architectural decisions. A model with deep reasoning capability may generate verbose, over-engineered code when all you needed was a clean utility function. Forcing one model into every role introduces subtle errors that compound across a codebase.
The fix isn’t switching models manually throughout the day. That’s just as noisy. The fix is designing a workflow that routes each phase to the right model automatically, treating the AI stack more like a specialist team than a single generalist.
Understanding the Three Phases
Before looking at specific models, it helps to be clear about what each phase actually involves.
Phase 1: Planning
Planning is about understanding what to build before writing a single line of code. This includes:
- Breaking down a feature request into discrete tasks
- Identifying dependencies and potential bottlenecks
- Evaluating architectural tradeoffs
- Writing a structured spec or task list
This phase benefits from models with strong reasoning and context retention. The model needs to hold a lot of information in mind simultaneously, reason about tradeoffs, and produce output that a code-generation model can act on precisely. Mistakes here cascade — a bad plan produces bad code, no matter how good the execution model is.
Phase 2: Execution
Execution is where the actual code gets written. Given a clear plan, the model needs to:
- Translate task descriptions into working code
- Follow existing patterns and conventions in the codebase
- Handle boilerplate and repetitive structure efficiently
- Move fast without introducing subtle bugs
Execution benefits from a model optimized for speed and code quality — one that’s been trained heavily on code and can produce correct, idiomatic output with minimal token overhead.
Phase 3: Review
Review is the final check before code moves to staging or a pull request. This includes:
- Catching logic errors the implementation model missed
- Flagging security vulnerabilities
- Checking for edge cases
- Ensuring the code matches the original plan
Review works best with a model that approaches the code fresh — ideally one from a different training lineage than the model that wrote it. This surfaces blind spots. If Composer wrote the code, having GPT-4 review it means you’re getting a genuinely different perspective, not just the same model confirming its own decisions.
Choosing the Right Model for Each Phase
Fable for Planning
Fable refers to using an extended-reasoning model — one capable of structured, multi-step thinking before producing output. In practice, this is often Claude with extended thinking enabled, or a similar reasoning-first configuration.
What makes a model good at planning:
- Long context window to hold the full codebase state
- Chain-of-thought capability to reason through tradeoffs explicitly
- Strong instruction following to produce structured output (task lists, specs, pseudocode)
- Ability to ask clarifying questions before committing to an approach
In a Claude Code workflow, you’d route any “figure out what to build” prompt here. The model produces a structured plan — often a numbered task list with notes on dependencies — that feeds directly into the next phase.
Cost-wise, this phase uses fewer tokens than execution, so the per-request cost of using a more capable model is manageable.
Composer for Execution
Composer — whether you’re using Cursor’s Composer mode, Claude Code’s native execution agent, or another code-generation-optimized setup — is where the plan becomes code.
A good execution model:
- Generates code quickly with low latency
- Has strong coverage of the languages and frameworks you’re using
- Can operate autonomously across multiple files without losing track
- Handles context injection well (receiving the plan output as structured input)
The key here is not using your most expensive model. Claude Haiku, GPT-4o-mini, or equivalent fast models handle routine implementation tasks well when given a good plan. You’re paying for speed and volume, not deep reasoning. Saving 60–70% per token at this phase adds up quickly across a full project.
GPT-4 for Review
Using OpenAI’s GPT-4 (or GPT-4o) as the final reviewer gives you several advantages.
First, model diversity. If you used a Claude-family model to plan and execute, running a GPT-based review introduces a different training distribution. GPT-4 may catch errors that Claude overlooked specifically because it approaches the problem differently.
Second, GPT-4’s review capability is strong. It handles nuanced security analysis, spots off-by-one errors, and provides clear natural language explanations of issues — which is useful for developer feedback loops, not just automated checks.
The review phase also uses relatively few tokens (you’re sending code for critique, not generating large outputs), so using a premium model here is cost-effective.
Building This Workflow in Claude Code
Claude Code is Anthropic’s CLI-based coding agent. It supports model routing, meaning you can configure which model handles which type of task or prompt. Here’s how to structure the multi-model workflow inside it.
Step 1: Define Your Three Agents
In Claude Code, you configure agents as distinct roles with specific model assignments.
Planning Agent:
model: claude-opus-4 (or claude-3-5-sonnet with extended thinking)
system_prompt: "You are a senior software architect. Given a feature request, produce a numbered task list with clear acceptance criteria. Flag architectural risks. Do not write code."
Execution Agent:
model: claude-haiku-3 (or claude-3-5-haiku)
system_prompt: "You are a code generation agent. You receive a task list. Implement each task sequentially. Follow existing code patterns. Do not plan or review — only implement."
Review Agent:
model: gpt-4o (via API)
system_prompt: "You are a senior code reviewer. Given a diff or code block, identify bugs, security issues, and deviations from the original spec. Be specific and actionable."The system prompts are doing real work here. They constrain each model to its role, which prevents the planning model from drifting into implementation and the execution model from second-guessing the architecture.
Step 2: Build the Routing Logic
Claude Code lets you chain agents with structured handoffs. The output of the planning agent becomes the input context for the execution agent.
A simple routing script might look like this:
async function runCodingWorkflow(featureRequest) {
// Phase 1: Planning
const plan = await planningAgent.run({
input: featureRequest,
outputFormat: "numbered_task_list"
});
// Phase 2: Execution
const implementation = await executionAgent.run({
input: plan.output,
context: { codebase: await getCodebaseContext() }
});
// Phase 3: Review
const review = await reviewAgent.run({
input: implementation.diff,
context: { originalSpec: plan.output }
});
return { plan, implementation, review };
}Other agents start typing. Remy starts asking.
Scoping, trade-offs, edge cases — the real work. Before a line of code.
The key detail is passing the original plan as context into the review phase. The review agent can then flag cases where the implementation drifted from the spec — a common source of bugs in longer sessions.
Step 3: Handle the Handoff Points
The most common failure points in multi-model workflows are at the handoffs between phases.
Planning → Execution: The execution model needs structured, unambiguous task descriptions. If the planning output is too high-level (“implement auth”), the execution model makes too many decisions on its own. Require the planning model to produce tasks with specific file names, function signatures, and expected behavior.
Execution → Review: Pass the diff, not the entire file. Sending only what changed keeps the review focused and reduces token cost. Also pass the original task list so the reviewer can verify completeness.
Review → Developer: Format review output as actionable comments, not a wall of text. Structure it as: issue, severity, location, suggested fix.
Step 4: Add Cost Tracking
Once you’re routing across multiple models, costs become harder to track. Add logging at each agent call:
function logAgentCall(agentName, model, inputTokens, outputTokens) {
const costs = {
"claude-opus-4": { input: 0.015, output: 0.075 },
"claude-haiku-3": { input: 0.00025, output: 0.00125 },
"gpt-4o": { input: 0.005, output: 0.015 }
};
const cost = (inputTokens * costs[model].input +
outputTokens * costs[model].output) / 1000;
console.log(`[${agentName}] ${model} | $${cost.toFixed(4)}`);
}This gives you visibility into where your budget is going and helps you tune model choices over time.
Common Mistakes and How to Avoid Them
Using an Expensive Model for Execution
The execution phase is high volume — you’re generating a lot of tokens across many tasks. Using Claude Opus or GPT-4 here when a faster, cheaper model handles the same code quality adds significant cost without proportional benefit.
Start with Claude Haiku or GPT-4o-mini for execution and upgrade only if output quality is consistently poor for your use case.
Letting the Execution Model Replan
If the execution model receives a vague task, it will start making planning decisions. This undermines the workflow. The fix is stricter system prompts and more detailed planning output — not a smarter execution model.
Skipping the Review Phase on “Simple” Tasks
Simple tasks still produce bugs. The review phase costs relatively little and catches issues before they reach production. Make it non-negotiable in your workflow, even for small changes.
Not Passing Enough Context to the Review Agent
A review agent that only sees the code diff — without the original spec, the surrounding file context, or the intended behavior — will give shallow feedback. Feed it everything it needs to evaluate the implementation against the plan.
Where MindStudio Fits This Workflow
If you want to go beyond Claude Code and build this multi-model workflow as a reusable, shareable skill, MindStudio’s Agent Skills Plugin is worth looking at.
The plugin (@mindstudio-ai/agent) is an npm SDK that lets any AI agent — including Claude Code agents — call MindStudio’s typed capabilities as simple method calls. You can expose your full three-phase coding workflow as a MindStudio skill, then call it from Claude Code or any other agent with a single method invocation.
What that looks like in practice:
import MindStudio from '@mindstudio-ai/agent';
const ms = new MindStudio(process.env.MINDSTUDIO_KEY);
// Call the multi-model coding workflow as a skill
const result = await ms.workflows.runCodingWorkflow({
featureRequest: "Add OAuth 2.0 login with Google",
codebaseContext: await getRepoSummary()
});MindStudio handles the infrastructure layer — rate limiting, retries, auth across multiple model providers — so your agent just calls the workflow and gets structured output back. You don’t have to manage separate API keys for Claude, OpenAI, and whatever else you add to the stack.
The platform also gives you 200+ models available without separate accounts, which means you can swap models in or out of each phase without reconfiguring credentials. You can try MindStudio free at mindstudio.ai.
Real-World Cost Comparison
Here’s a rough estimate for a mid-size feature implementation — say, adding a new API endpoint with authentication, validation, and tests.
Single-model approach (Claude Opus for everything):
- Planning: ~2,000 tokens → $0.15
- Implementation: ~15,000 tokens → $1.13
- Review: ~3,000 tokens → $0.23
- Total: ~$1.51
Multi-model approach:
- Planning (Claude Opus): ~2,000 tokens → $0.15
- Implementation (Claude Haiku): ~15,000 tokens → $0.02
- Review (GPT-4o): ~3,000 tokens → $0.06
- Total: ~$0.23
That’s roughly an 85% cost reduction per feature, with comparable or better output quality because each model is doing what it’s suited for.
At scale — dozens of features per sprint, across multiple developers — this difference is significant.
Frequently Asked Questions
What is a multi-model AI coding workflow?
A multi-model coding workflow assigns different AI models to different phases of the development process — typically planning, code generation, and review. Each model is chosen for its strengths in that specific task, rather than using one general-purpose model for everything. This improves both output quality and cost efficiency.
Why not just use the best model for every task?
Top-tier models like Claude Opus or GPT-4 are expensive per token. Many coding tasks — especially routine implementation — don’t require their full capability. A faster, cheaper model with a focused system prompt handles standard code generation well. Reserving high-capability models for planning and final review keeps quality high where it matters while keeping costs down overall.
Can Claude Code switch between models automatically?
Yes. Claude Code supports configuring different models for different agent roles. You can define a planning agent, an execution agent, and a review agent with separate model assignments, then chain their outputs together using routing logic. The handoffs between phases are where most of the design work happens.
How do I know if the multi-model workflow is actually better than a single model?
Run a controlled test. Take 10 representative tasks and run them through both approaches. Compare: (1) bug rate in output, (2) adherence to spec, (3) total token cost. Track these across a few sprints. Most teams find the multi-model approach wins on both quality and cost once the routing logic is tuned.
What makes GPT-4 a good reviewer compared to other models?
GPT-4 (and GPT-4o) is strong at code critique because of its training on a broad range of code patterns and its ability to explain issues in clear, actionable language. When used to review code written by a Claude-family model, it also introduces model diversity — it approaches the code from a different training distribution, which surfaces blind spots the writing model might have. Using the same model family to write and review code is like proofreading your own work.
Is this approach overkill for solo developers?
Not necessarily. Even solo developers benefit from cost savings and quality improvements. You don’t need a complex infrastructure setup to start — you can begin by manually using different models for different prompts, then automate the routing once the workflow is validated. The overhead of setup pays back quickly on any project with more than a week of development time.
Key Takeaways
- A multi-model AI coding workflow assigns specific models to planning, execution, and review based on their strengths — not convenience.
- The planning phase needs reasoning depth; the execution phase needs speed and code quality; the review phase benefits from model diversity.
- Using Claude Haiku for execution instead of Claude Opus can cut implementation costs by 90% with minimal quality impact on standard tasks.
- The biggest workflow failure points are at phase handoffs — invest in structured output formats and clear system prompts to keep each model in its lane.
- Tools like MindStudio’s Agent Skills Plugin let you expose the full workflow as a callable skill, handling the infrastructure layer so your agents stay focused on reasoning.
Start with a simple three-agent setup, track your costs and output quality, and tune from there. The architecture is straightforward — the value compounds as you apply it consistently.
