Claude 4 Lands Three Distinct Tiers — Here's How App Builders Should Use Each

Anthropic's Claude 4 family — Opus 4.7, Sonnet 4.6, and Haiku 4.5 — gives mobile app teams three clearly differentiated AI tiers. Here's how to route tasks to the right model without blowing your API budget at scale.

Anthropic's Claude 4 family — Opus 4.7, Sonnet 4.6, and Haiku 4.5 — is now the reference model suite for most teams building AI-powered features. For mobile app teams specifically, the differentiation across tiers matters more than it might seem: choosing the wrong tier means either paying 5–10× too much for inference you didn't need, or shipping a noticeably worse user experience that shows up in your reviews. Here's how to think about routing for an iOS or Android app.

The Three-Tier Architecture

Claude 4's three-tier structure follows a clear capability-cost curve:

The pattern Anthropic intends — and that most production teams end up at — is Sonnet as the workhorse, Haiku for anything that needs to be fast or happens millions of times per day, and Opus for genuinely hard tasks where quality is worth the cost premium.

Routing the Right Tasks in a Mobile App

Mobile is volume-sensitive in a way that a SaaS dashboard is not. A productivity app with 100k DAU and an AI assistant could trigger anywhere from 500k to 2M API calls per day. At that scale, every tier decision shows up in your infrastructure bill.

Haiku: the speed layer

• Real-time autocomplete or in-app search suggestions
• Classifying support tickets into routing buckets before a human or heavier model sees them
• Quick sentiment checks — "is this user frustrated enough to churn?"
• Lightweight content moderation: flagging for human review rather than rendering a verdict
• Router logic itself: is this request simple enough to answer without escalation?

Haiku's latency profile suits features that need to feel instant. Sub-second streaming responses are what make in-app AI feel native rather than bolted-on.

Sonnet: the everyday workhorse

• In-app support chat: answering product questions, explaining billing, handling refund logic
• Generating personalised onboarding copy or push notification variants
• Drafting App Store review reply templates
• Adapting in-app strings for a new market (though purpose-built localisation pipelines still outperform for volume)

Reports from development teams suggest Sonnet delivers output quality close to Opus for most everyday tasks, at meaningfully lower cost. It's the tier most apps should default to when they add a first AI feature.

Opus: reserve it for genuinely hard work

• Long-document analysis — a full App Review guideline diff, a lengthy revenue-report interpretation
• Complex agentic chains: a workflow that calls App Store Connect API, interprets results, then drafts a pricing-change recommendation
• Any output where a mistake has a real cost — a legal summary, a pricing strategy memo

One practical pattern: use Haiku as a router. Let it classify each incoming request first. Low-complexity → answer with Haiku directly. Medium → escalate to Sonnet. Defined high-complexity task types → Opus. This keeps your median cost near Haiku while preserving quality for edge cases — and it's composable with any orchestration layer (LangChain, raw API, or a simple if/else in Swift).

Vision and Tool Use: The Capabilities Mobile Teams Should Actually Exploit

Beyond text, the Claude 4 family supports vision input and structured tool use — two capabilities that open up genuinely new mobile product patterns.

Vision is useful for: letting users photograph an error and have your support bot diagnose it; building in-app document or receipt scanning; visual search in marketplace apps. The key constraint is latency — for real-time camera features, batch or pre-process before sending to the API rather than calling on every frame.

Tool use (function calling) lets Claude trigger external actions: querying RevenueCat for subscription status, pulling App Store Connect analytics, updating a CRM record. For teams building retention or onboarding agents, this turns Claude from a chat interface into something that can actually act in your stack. The developer experience for defining and handling tools has improved noticeably in the 4.x family — structured outputs are more reliable, and error handling is more predictable. It's not magic; you still own the tool definitions and the result-handling code. But the integration surface is smaller than it was a year ago.

For the Lean Team: Start Simple, Measure, Then Optimise

If you're an indie or small team shipping a first AI feature, the practical advice is: start with Sonnet 4.6, instrument your call volume and latency, and only layer in Haiku routing once you have real traffic data to justify the added logic. Premature tier optimisation is a genuine trap — routing code adds maintenance surface area, and the cost delta only matters at meaningful scale.

If you're already running significant AI call volume and haven't revisited tier allocation, that's worth a look — the earlier piece on LLM cost thinking for indie iOS devs covers the mental model in more depth. And if you're evaluating where AI tooling sits alongside your App Store ops budget overall, the AppsOps pricing page shows what best-in-class localization and ASO infrastructure costs — useful context for building a tool-spend spreadsheet.

Sources and Further Reading

• Anthropic — official model documentation, API reference, and release notes
• Apple Developer — App Store guidelines and iOS development resources
• RevenueCat — subscription analytics and mobile monetisation infrastructure
• Anthropic News — product and model announcements