Claude Haiku 5.5 Cut Prices 75%: A Framework for Model Tiering

Most teams I talk to use one AI model for everything: whatever their company defaulted to, for every task from drafting an email to analyzing a contract. That's like hiring a senior consultant to file your expense reports. On October 7, Anthropic released Claude Haiku 5.5, a small model priced up to 90% below its previous Haiku, and it's a good moment to fix that habit.

What actually changed

Haiku 5.5 is priced per million tokens at $0.10 for input and $0.50 for output on prompts up to 100,000 tokens — down from $1.00 and $5.00 for Haiku 4.5. Above 100,000 tokens, the price rises to $0.50 input and $2.50 output. Anthropic says that works out to roughly 75% lower cost on average than Haiku 4.5, once you account for a new tokenizer that counts text as somewhat more tokens than before.

It's also Anthropic's fastest model at standard speed, and the first Haiku with an adjustable "effort" setting — Low, Medium, High, Xhigh or Max — so you can trade cost against intelligence on the same model instead of switching models entirely. Anthropic positions it for high-volume, repeatable work: summarizing, classifying, querying databases, and running as a fast subagent alongside Sonnet 5.5 and Opus 5.5 on coding tasks. On its own benchmarks, Anthropic is explicit that Sonnet 5.5 and Opus 5.5 remain the better choice for complex agentic coding — Haiku 5.5 scored 39.2% on Terminal-Bench 4.0 against Sonnet 5.5's 70.6%, for instance.

This isn't just an Anthropic story. Google, OpenAI and others have all been shipping cheaper "small" models alongside their flagship ones for over a year. The pattern matters more than any single model: the real lever on your AI bill isn't which model you pick — it's whether you're matching the model to the task at all.

Why most teams get this wrong

Teams default to the most capable model available for three reasons: it's the one leadership approved first, nobody wants to be blamed for a bad output from a "cheap" model, and most people have never been shown that tiering is an option. The result is systems and habits that spend flagship-model money on work a cheap model handles just as well — reformatting a report, pulling a number out of an email thread, tagging a support ticket by category.

That's expensive in a way that compounds. A single summary might cost fractions of a rupee more on the pricier model. Multiply that by every ticket, every report, every daily automation run across a team of 200, and the gap becomes a real budget line — especially for Indian businesses where cost per interaction already has to justify the AI spend to finance.

A three-tier framework for matching model to task

Before you pick a model, sort the task into one of three tiers:

1. High-volume, low-judgment work. Classification, tagging, extracting a field from a document, short summaries, simple database or CRM queries, first-pass drafts you'll edit anyway. This is Haiku-tier work: fast, cheap, and the small loss in nuance rarely shows up in the output.
2. Mixed reasoning with real stakes, but not novel. Analyzing a sales report, drafting a client-facing email, writing code for a well-understood feature, summarizing a long contract for a human to review. This is mid-tier (Sonnet-class) work: enough reasoning ability to be reliable, still far cheaper than the top tier.
3. High-stakes or genuinely hard reasoning. Legal or financial judgment calls, complex multi-step agentic coding, strategy documents, anything where a mistake is costly to catch later. This is top-tier (Opus-class) work, where you pay for the extra reliability.

Write this tiering down as a one-page guide for your team, with five or six examples from your own workflows in each tier. Without it, people default upward "to be safe," which is exactly the habit that inflates the bill.

How to test whether a cheaper model is good enough

Don't guess — check. Take 15–20 real examples of a task you currently run on an expensive model, and run them through a cheaper model (or the same model at a lower effort setting) and compare outputs side by side. For each one, ask: would I have caught a difference if I hadn't been looking for one? If a cheap model handles most of them correctly and the few misses are easy for a human to catch at review time, move that task down a tier. If the misses are subtle or costly, keep it where it is.

This is the same test you'd run before hiring a junior team member to take over a task from a senior one: give them a batch of real work, and judge the output, not your assumption about their seniority.

A worked example

Say a customer support team handles 2,000 tickets a week. Today, every ticket runs through a single flagship model for triage (what category, what urgency), drafting a first-response, and escalation summaries for complex cases. Split by tier instead:

• Triage and categorization (2,000 tickets/week): move to a cheap, fast model like Haiku 5.5. This is exactly the high-volume, low-judgment work it's built for, and Anthropic specifically recommends it for live customer support given its speed.
• First-response drafts (2,000 tickets/week): keep on a mid-tier model, since a badly worded reply damages a customer relationship even if the facts are right.
• Escalation summaries for a human supervisor (maybe 150 tickets/week, the genuinely hard cases): keep on your most capable model, since getting these wrong means a supervisor makes a bad call.

The triage step alone, at this volume, moves from flagship pricing to a fraction of it — without touching the two steps where quality actually matters to the customer.

What to watch for

A few honest caveats, since the goal here is a bill you can trust, not just a lower one:

• Benchmarks are vendor-reported. Treat Anthropic's own comparison numbers as a starting point, not proof for your specific task. Run your own side-by-side test as above.
• New tokenizers can understate real savings. Haiku 5.5's new tokenizer counts the same text as more tokens than before, so the headline "90% cheaper" partly offsets against a slightly higher token count. The net saving is still large, but check your own before/after invoice, not just the sticker price.
• Don't tier blind. Revisit your tiering guide every quarter, the same way you'd revisit any process. Models change faster than most internal documentation does — I covered how to judge any model upgrade, not just this one, in a recent piece on evaluating AI upgrades.

If your team has never measured what its AI spend is actually going toward, model tiering is one of the fastest, lowest-risk ways to find out — and often the first real evidence that AI costs are a design choice, not a fixed bill. If you want a structured way to build this kind of judgment across a whole team, that's exactly what we cover in the AI Generalist Certification.