Model Routing for AI Agents: Matching Task Complexity to the Right Model

Quick answer

A model routing framework directs each agent task to the most appropriate LLM based on complexity, cost, and latency requirements—sending simple tasks to fast, cheap models like Haiku and reserving powerful models like Sonnet or Opus for complex reasoning. This prevents overspending on trivial operations while ensuring critical work gets the intelligence it needs.

Key takeaways

Model routing is dynamic task assignment, not one-time model selection—your framework evaluates each task and picks the right model in real time
Haiku excels at structured, bounded tasks like unit tests, linting, and formatting; Sonnet handles nuanced logic, refactoring, and multi-file changes
Escalation rules prevent failures: route to a stronger model when the first attempt hits token limits, produces errors, or requires deeper reasoning
Cost savings compound quickly when you stop using Sonnet for tasks Haiku can handle in seconds
Cross-provider routing (Claude, Codex, GPT-4) requires understanding each model’s strengths and your agent’s ability to switch contexts
Production routing needs monitoring: track success rates, costs, and latency per route to tune your framework over time

Why Model Routing Matters When You’re Running Multiple Agents

When you’re driving AI coding agents from your phone—checking on Claude generating tests, Codex refactoring a module, and another agent fixing linter errors—you need each task to land on the right model. A model routing framework makes this automatic.

Without routing, you face two bad outcomes: you either use a single expensive model for everything (burning budget on trivial tasks), or you manually pick models per task (slow, error-prone, doesn’t scale). Model routing solves this by evaluating task characteristics—complexity, context size, required reasoning depth—and assigning the optimal LLM.

The practical benefit shows up in everyday moments. You start a test-generation task from your phone while commuting. Your router sees “write unit tests for this pure function” and sends it to Haiku, which finishes in 8 seconds at a fraction of Sonnet’s cost. Later, you ask an agent to refactor a tangled authentication flow across three files. The router recognizes the complexity and escalates to Sonnet, which has the reasoning capacity to untangle dependencies safely.

This matters more when you’re running multiple agents in parallel across different folders or worktrees. Each conversation might need a different model at different stages. Your router becomes the traffic controller that keeps costs down and quality up without requiring you to micromanage every task from a mobile screen.

Haiku for Unit Tests, Sonnet for Logic: A Practical Routing Framework

A working llm routing strategy starts with clear task categories. Here’s a framework that maps common agent tasks to Claude’s model tiers:

Haiku (fast, cheap, structured):

Writing unit tests for pure functions with clear inputs/outputs
Formatting code to match a style guide
Generating boilerplate (CRUD endpoints, config files, simple schemas)
Running linter fixes that follow deterministic rules
Extracting data from logs or structured text
Updating documentation strings based on code changes

Sonnet (balanced reasoning and speed):

Refactoring logic across multiple functions or files
Debugging failures that require understanding control flow
Implementing features that need domain knowledge or context from several modules
Writing integration tests that coordinate multiple components
Reviewing diffs and suggesting improvements
Translating requirements into implementation plans

Opus (deep reasoning, rare):

Architectural decisions that affect multiple systems
Complex debugging where the root cause is unclear
Security-sensitive changes requiring careful analysis
Performance optimization that demands understanding of trade-offs across layers

The key insight: most agent tasks fall into the first two categories. Opus is rarely needed if your prompts are well-scoped. A good haiku sonnet opus routing strategy reserves Opus for the 5% of tasks where Sonnet genuinely struggles, not as a default for “important” work.

Task-based model selection works because these models have different strengths. Haiku is fast and reliable for bounded problems with clear success criteria. Sonnet handles ambiguity and multi-step reasoning. Opus brings the deepest context window and reasoning, but at a cost and latency that only make sense for genuinely hard problems.

Building a Router That Knows When to Escalate

A practical model routing framework needs escalation logic—rules that detect when a task needs a stronger model. Static routing (“always use Haiku for tests”) breaks when a test requires understanding complex state management or mocking intricate dependencies.

Escalation triggers:

Token limit approaching: If a task’s context is nearing Haiku’s window, route to Sonnet preemptively
Error patterns: If Haiku produces code that fails tests or linter checks, retry with Sonnet
Explicit complexity markers: Keywords in the task like “refactor,” “debug,” “optimize,” or “why” signal reasoning needs
File count or dependency depth: Tasks touching 4+ files or requiring cross-module understanding go to Sonnet
User override: Let the developer force a specific model when they know the task is tricky

Here’s a simple escalation flow:

Task arrives → Router analyzes prompt + context
  ↓
Haiku attempt (if task looks bounded)
  ↓
Success? → Done
  ↓
Failure or complexity detected → Escalate to Sonnet
  ↓
Sonnet attempt
  ↓
Success? → Done
  ↓
Still failing or hitting limits → Escalate to Opus (rare)

The router should log each decision and outcome. When you’re checking agent progress from your phone, you want to see “Haiku failed, escalated to Sonnet, now working” rather than a cryptic error. This visibility helps you tune routing rules over time.

One practical detail: escalation should preserve context. If Haiku’s attempt produced partial output or revealed constraints, pass that to Sonnet. Don’t start from scratch—treat escalation as a handoff, not a reset.

Cost and Latency Trade-offs in Real Agent Workflows

Model routing is fundamentally about trade-offs. Haiku is roughly 20x cheaper than Sonnet per token and responds in seconds; Sonnet costs more but handles complexity Haiku can’t. Opus is another 3-5x more expensive than Sonnet and slower, but solves problems the others miss.

Real workflow math:

Imagine you run 100 agent tasks per day. Without routing, you use Sonnet for everything:

100 tasks × $0.015 per task (average Sonnet cost) = $1.50/day = ~$45/month

With a model routing framework:

60 tasks routed to Haiku × $0.0008 = $0.48
35 tasks routed to Sonnet × $0.015 = $0.525
5 tasks routed to Opus × $0.075 = $0.375
Total: ~$1.38/day = ~$41/month

The savings look modest here, but they scale. At 500 tasks/day (realistic for a team running multiple agents across projects), you’re saving $60-100/month. More importantly, Haiku’s speed means those 60 simple tasks finish in seconds instead of 15-30 seconds, so you get faster feedback when checking from your phone.

Latency matters when you’re remotely monitoring agents. If you approve a task and it routes to Haiku, you might see results before you switch apps. If it routes to Opus, you’re waiting 45+ seconds. A good router minimizes unnecessary waiting by matching task urgency to model speed.

When to optimize for cost vs. speed:

Cost-sensitive: batch tasks, background work, non-blocking operations (use Haiku aggressively)
Speed-sensitive: tasks blocking other work, user-initiated requests, approval-required steps (use Sonnet to avoid retries)
Quality-sensitive: production code, security changes, customer-facing features (use Sonnet or Opus, accept the cost)

Routing Across Claude, Codex, and Other Providers

A model routing framework can route between providers, not just models within one provider. This is trickier because each provider has different strengths, APIs, and context-handling.

Claude (Haiku/Sonnet/Opus):

Best for reasoning-heavy tasks, multi-step logic, and natural language understanding
Strong at refactoring, debugging, and explaining code
Native support in MobileVibe via Claude Desktop and headless CLI

Codex (OpenAI’s code models):

Excellent at code completion, boilerplate generation, and pattern matching
Fast for straightforward implementation tasks
Available through MobileVibe’s headless codex-cli

GPT-4 / GPT-4 Turbo:

General-purpose reasoning, good for tasks that mix code and prose
Larger context windows in Turbo variants
Useful for documentation, requirements analysis, and cross-domain tasks

Cross-provider routing strategy:

Route by task type: Use Codex for pure code generation (functions, classes, tests), Claude for logic and refactoring, GPT-4 for tasks requiring broad context or non-code reasoning
Fallback chains: If Claude hits a quota or rate limit, fall back to GPT-4 for similar tasks
Provider-specific strengths: Route SQL generation to models trained on databases, frontend tasks to models strong in JavaScript frameworks

One caveat: switching providers mid-conversation is harder than switching models within Claude. Each provider maintains separate conversation history. If you start a task with Codex and need to escalate, you may need to summarize context for Claude rather than seamlessly continuing. MobileVibe helps by letting you manage multiple agent conversations in parallel, so you can run a Codex lane and a Claude lane side-by-side rather than forcing one conversation to jump providers.

Testing Your Router Before Agents Hit Production

A model routing framework is code—it needs testing. Before you trust it with production agent workflows, validate that it routes correctly and handles edge cases.

Testing approach:

Unit test routing logic: Given a task description and context, assert the router picks the expected model

Task: "Write unit tests for calculateDiscount()"
Expected: Haiku

Task: "Refactor authentication to support OAuth and SAML"
Expected: Sonnet

Simulate escalation: Force a Haiku failure (mock an error response) and verify the router escalates to Sonnet with preserved context
Cost and latency benchmarks: Run a batch of real tasks through your router and measure total cost and average latency vs. a baseline (all-Sonnet or all-Haiku)
Edge case handling: Test tasks with ambiguous complexity, very large context, or missing metadata—ensure the router has sensible defaults
Provider fallback: If you route across providers, test quota exhaustion and rate-limit scenarios

MobileVibe’s desktop connector lets you run these tests locally before deploying routing logic to agents you’ll monitor from your phone. You can iterate on routing rules, check logs, and tune thresholds without risking production work.

One practical tip: log every routing decision with the task description, chosen model, and outcome (success/failure/escalation). This log becomes your dataset for improving the router. After a week of real usage, analyze which routes succeeded, which escalated, and which cost more than expected.

Monitoring and Adjusting Routes as Tasks Evolve

A model routing framework isn’t set-and-forget. As your codebase grows, your team’s tasks change, and model capabilities improve, your routing rules need tuning.

What to monitor:

Success rate per route: What percentage of Haiku tasks succeed without escalation? If it’s below 70%, you’re routing too aggressively to Haiku.
Cost per task type: Are test-generation tasks costing more than expected? Maybe they’re escalating to Sonnet too often.
Latency distribution: Are Sonnet tasks taking longer than they used to? Provider performance changes over time.
Escalation frequency: If 40% of tasks escalate, your initial routing is too optimistic.

When you’re checking agent progress from your phone, MobileVibe shows which conversations are active, blocked, or waiting for approval. If you notice a pattern—like test tasks frequently needing approval because Haiku’s output is wrong—that’s a signal to adjust routing.

Adjustments to consider:

Tighten Haiku criteria: If Haiku escalates often, add stricter rules (e.g., “only route to Haiku if task mentions a single function and no external dependencies”)
Expand Sonnet’s role: If Sonnet rarely fails, you can route more borderline tasks to it
Introduce new categories: As you identify task patterns (e.g., “API endpoint generation”), create specific routes for them
Provider rotation: If one provider’s quality drops or pricing changes, shift tasks to alternatives

Model capabilities evolve. Claude’s Haiku might get better at reasoning over time, or a new model tier might emerge. Your router should be easy to update—parameterize model names and thresholds so you can swap “Haiku” for “Haiku-2” or adjust cost limits without rewriting logic.

FAQ

What’s the difference between model routing and simple model selection?

Model selection is choosing one model for all tasks or manually picking a model per task. Model routing is an automated framework that evaluates each task’s characteristics (complexity, context size, required reasoning) and dynamically assigns the optimal model. Routing scales because it makes decisions for you, adapts to task variety, and handles escalation when the first choice fails.

How do I decide which tasks should go to Haiku vs. Sonnet?

Start with task structure and reasoning depth. Route to Haiku if the task is bounded, has clear success criteria, and doesn’t require multi-step reasoning—like writing unit tests for a pure function, formatting code, or generating boilerplate. Route to Sonnet if the task involves ambiguity, multiple files, debugging, or refactoring logic. When in doubt, start with Sonnet and add Haiku routes as you identify patterns that succeed reliably.

Can I route between different providers (Claude, Codex, etc.) in the same agent?

Yes, but it’s more complex than routing between models within one provider. Each provider maintains separate conversation history, so switching mid-conversation requires summarizing context for the new provider. A practical approach is to run separate agent lanes per provider (one Codex conversation, one Claude conversation) and route tasks to the appropriate lane from the start. MobileVibe supports managing multiple conversations in parallel, making this workflow feasible from your phone.

What happens if my router sends a task to the wrong model?

The task either fails (producing incorrect code, hitting token limits, or timing out) or succeeds but costs more than necessary. A good router includes escalation logic: if Haiku fails, retry with Sonnet. If Sonnet struggles, escalate to Opus. Log every routing decision and outcome so you can identify patterns and tune your rules. The cost of occasional mis-routes is usually lower than the cost of always using expensive models.

How do I measure whether my routing strategy is actually saving money?

Track total cost per day or week with routing enabled, then compare to a baseline (what you’d spend using one model for all tasks). Log cost per task type (tests, refactoring, boilerplate) and calculate savings per category. Most routing frameworks save 30-50% on cost by offloading simple tasks to cheaper models. Also measure latency—if routing saves money but doubles wait time, the trade-off might not be worth it for time-sensitive work.

Can I test routing logic before deploying agents to production?

Yes. Unit test your routing rules with sample task descriptions and assert the expected model is chosen. Run a batch of real tasks through your router locally and measure cost, latency, and success rate. Simulate failures (mock error responses) to verify escalation logic works. MobileVibe’s desktop connector lets you test routing on your own machine before trusting it with production agent workflows you’ll monitor remotely.

How does model routing work with multi-agent workflows?

Each agent conversation can have its own routing rules, or you can share a routing framework across agents. In practice, you might run one agent with aggressive Haiku routing for background tasks (tests, linting) and another agent with Sonnet-first routing for critical features. MobileVibe’s conversation-based model lets you manage multiple agents in parallel, each with its own folder, model, and routing strategy. You check their progress from your phone, approve tasks that need input, and let the router handle model selection per task.

If you’re running AI coding agents across multiple projects and want to control them from your phone—checking progress, approving tasks, and adjusting routing strategies on the go—try MobileVibe free at mobilevibe.com. Your agents run on your own machine, and you route tasks to the right model from anywhere.

Model Routing for AI Agents: Matching Task Complexity to the