Cost-Effective AI: How to Pick Claude Opus vs Sonnet vs Haiku for Each Workload

Anthropic ships three Claude models: Opus (the most capable), Sonnet (the daily-driver), Haiku (the fast, cheap one). They’re ~5-15x apart in cost per token. Most teams pick one and use it for everything. That’s the wrong move — different workloads have different quality bars, and over-spending on Opus for jobs Haiku could handle eats your AI budget without producing better output. Here’s the per-workload framework.

#Current pricing (May 2026)

• Claude Opus 4.7: $15 / $75 per million tokens (input / output).
• Claude Sonnet 4.6: $3 / $15 per million tokens.
• Claude Haiku 4.5: $0.80 / $4 per million tokens.

Opus is ~5x Sonnet and ~19x Haiku on input cost; ~5x Sonnet and ~19x Haiku on output cost. The cost gap is real and matters at scale.

#What each model is good at

Opus 4.7: complex reasoning, multi-step problem solving, code generation in unfamiliar codebases, dense analytical work, anything where the answer needs to be exactly right. Sonnet 4.6: most daily work — drafting, summarization, structured extraction, code edits in familiar codebases, customer-facing chat that needs polish. Haiku 4.5: high-volume bulk work — classification, simple transformations, fast Q&A from a small context, lead scoring, embedding-decisioning.

#The FH workload-to-model mapping

• Blog post drafts (long-form, voice-sensitive): Opus.
• Location/service page drafts (templated bulk): Sonnet with prompt caching.
• Image alt-text generation: Haiku.
• Lead scoring: Haiku.
• Customer chat (RAG-backed Q&A): Sonnet.
• Code review of complex changes: Opus.
• Boilerplate code generation: Sonnet.
• PR-summary generation: Haiku.
• Image curation rubric scoring: Sonnet (with vision).
• Bulk classification (e.g. ‘is this email spam?’): Haiku.

#The decision framework

1. Is the output going to a customer or end-user (vs. internal)? If yes, never below Sonnet.
2. Does the task require multi-step reasoning across multiple inputs? If yes, Opus.
3. Is the task volume high (1000+/day) and the output simple? Haiku.
4. Is the task voice-sensitive (FH brand voice, customer support tone)? Sonnet or Opus, depending on stakes.
5. Default for everything else: Sonnet.

#The cost trap: defaulting to Opus

Most new Claude users default to Opus because ‘the best model.’ That’s 5x the cost for many tasks Sonnet handles equally well. Run a small evaluation: take 20 representative tasks, run them through both Opus and Sonnet, blind-score the outputs. For most tasks Sonnet wins or ties; you save 5x. For the tasks Opus genuinely wins, you keep Opus on them and save on the rest.

#Mixing models in a single workflow

Common pattern: Haiku triages the request, Opus answers if Haiku flags it as complex. We use this in customer support chat: Haiku reads the user’s message and either answers from a small FAQ set (cheap, fast) or escalates to a Sonnet/Opus-backed agent (more capable, slower).

async function triage(userMessage: string): Promise<"simple" | "complex"> {
  const res = await claude.messages.create({
    model: "claude-haiku-4-5-20251001",
    max_tokens: 20,
    system: "Classify the user message as 'simple' (one-shot answer fits) or 'complex' (needs reasoning).",
    messages: [{ role: "user", content: userMessage }],
  });
  const text = res.content[0].type === "text" ? res.content[0].text : "";
  return text.toLowerCase().includes("complex") ? "complex" : "simple";
}

async function answer(userMessage: string) {
  const level = await triage(userMessage);
  const model = level === "complex" ? "claude-opus-4-7" : "claude-sonnet-4-6";
  return await claude.messages.create({ model, max_tokens: 1024, messages: [{ role: "user", content: userMessage }] });
}

#When Haiku is actually too small

• Tasks needing context beyond ~30k tokens. Haiku’s effective context window is smaller in practice.
• Tasks requiring nuanced understanding of brand voice. Haiku tends generic.
• Tasks with tool use that requires careful reasoning about when to call which tool.
• Anything customer-facing where a slightly-off response damages trust.

#When Opus is overkill

• Anything classification-flavored (yes/no, category-A/B/C).
• Anything where the input is structured and the output is short.
• Anything that runs 100+ times per day per user.

#Batch API for non-realtime workloads

Anthropic’s Batch API processes requests asynchronously at 50% the per-token cost of the regular API. Use it for: nightly content generation, periodic re-indexing, weekly digest emails, anything that doesn’t need a real-time response. Halving costs on batch workloads compounds quickly.

#Measuring the savings

Track per-workload cost in your observability. Tag every Claude call with `workload_id`. Roll up monthly: Opus calls × Opus rate, Sonnet × Sonnet rate, Haiku × Haiku rate. Compare to ‘what if we used Opus for everything’ — that’s your savings number. We track this at FH and report it monthly.

#How this lands across FH client work

Every FH AI workflow has an explicit model choice documented. Lead scoring: Haiku. Customer chat: Sonnet (sometimes upgrading to Opus mid-conversation). Image curation: Sonnet (vision). Blog drafts: Opus with prompt caching. Total monthly AI spend across the FH client book sits well under $500 despite running tens of thousands of inference calls. If your AI bill feels too high and you’re running everything on Opus, book a consultation — the model-routing setup is a one-week engagement with immediate compounding savings.