Should Your Business Switch to Claude Haiku 5.5?

Anthropic just cut Claude Haiku's price 90% and landed it dollar-for-dollar on OpenAI's cheapest model. If you're still routing ticket classification or document summaries through Sonnet, you're paying a premium for a job a $0.10-per-million-token model now does.

Claude Haiku 5.5 shipped October 7. For requests under 100,000 tokens, it runs $0.10 per million input tokens and $0.50 per million output tokens — down from Haiku 4.5's flat $1.00/$5.00. That's a 90% cut, and it ties OpenAI's GPT-6 Luna exactly. Anthropic is positioning it for the boring, high-volume work: summarizing documents, classifying support tickets, querying databases, and running the small decisions that feed a bigger model's workflow. It's live day one on AWS, Google Cloud, Azure, and the Claude Platform.

None of that means switch everything. It means the "which model should run this specific task" question just got a hard dollar number attached to it, and most SMB operators haven't rerun that math since whatever model they started with.

The actual price delta

Here's what moved, lined up against the two models operators actually ask me about:

A task running 10 million input tokens and 2 million output tokens a month cost $20 on Haiku 4.5. The same task on Haiku 5.5's low tier costs $2. That's not a rounding error on an SMB budget — that's the difference between justifying a model swap and not bothering.

The effort dial changes the math

Here's the part the price chart doesn't show: Haiku 5.5 ships with an adjustable effort setting — Low, Medium, High, Xhigh, Max — and medium is the default. On Terminal-Bench, Anthropic's own numbers put medium effort around 20% and max effort around 39%. Nearly double, for the same model, just by turning a dial.

Cheap isn't automatically capable. If your task needs max-effort performance, you're not paying the sticker price anymore — you're paying for more output tokens and more latency to get there. Run the task at the effort level it actually needs, not the default, before you compare it against what you're paying today.

flowchart TD
  A([Task you're considering<br/>moving to Haiku 5.5]) --> B{Classification,<br/>summarization, or<br/>DB query against<br/>your own data?}
  B -->|No, needs creative<br/>or multi-step reasoning| Stay[Stay on Sonnet / Opus]
  B -->|Yes| C{Wrong output costs<br/>real money or trust?}
  C -->|Yes, high stakes| D[Test at High/Xhigh effort<br/>before deciding]
  C -->|No, low stakes| E[Test at Medium<br/>default effort]
  D --> F{Still cheaper than<br/>current model<br/>after effort bump?}
  E --> F
  F -->|Yes| Switch[Switch this task<br/>to Haiku 5.5]
  F -->|No| Stay

The 5-minute test

1. Pull last month's usage. Your API dashboard or billing export already groups this by model.
2. Isolate the boring work. Classification, extraction, summarization, routing — not creative writing, not multi-step planning, not anything customer-facing without a human reviewing it first.
3. Check what's running it today. If that bucket is on Sonnet-tier or GPT-4-class pricing, the test has already failed.
4. Do the multiplication. Take that bucket's monthly token volume and apply the 90% cut. If the savings clears a few hundred dollars a month, it's worth an afternoon.
5. Re-run your eval set before you flip the switch. You have one. If you don't, build a 20-example set from real past outputs before this step, not after.

Skip if

• The task needs real reasoning or generation, not classification. Medium-effort Haiku scores roughly half of its own max-effort score on agentic benchmarks — you'll end up cranking the dial and eating back the savings.
• A wrong answer is expensive. A mis-routed support ticket is a two-minute fix. A misclassified contract clause isn't.
• The task bucket is under $50/month already. The engineering afternoon to test and cut over costs more than a year of savings.
• You don't have an eval set. Swapping models blind is how "cheaper" quietly becomes "wrong, and nobody noticed for a month."

Here's where the four models actually sit once you plot cost against capability instead of just reading the price sheet:

quadrantChart
  title Cost vs. capability, small-model tier
  x-axis Cheaper --> More expensive
  y-axis Lower capability --> Higher capability
  quadrant-1 Worth testing first
  quadrant-2 Premium, justify it
  quadrant-3 No reason to pick
  quadrant-4 Good default
  "Haiku 5.5 (medium)": [0.15, 0.45]
  "Haiku 5.5 (max effort)": [0.35, 0.75]
  "GPT-6 Luna": [0.15, 0.4]
  "Gemini 3.5 Flash-Lite": [0.55, 0.5]
  "Haiku 4.5": [0.85, 0.45]

You don't switch models because one got cheaper. You switch the specific task that was never worth the premium in the first place.

What I'd actually do

I'd run the 5-minute test against my own client stack before writing another word about it, and I'd bet most SMB operators find at least one bucket — ticket triage, report summarization, data extraction — that's been sitting on Sonnet-tier pricing out of inertia, not necessity. That's the same inertia tax I wrote about when token prices fell and AI bills didn't: the sticker price moves, but nobody goes back and re-points the pipeline unless someone makes them look at the bill. This is the automation-audit instinct I bring to stack-reduction engagements like The Hub's — the tool rarely changes the job, it just changes what the job should cost. If you want someone to run that audit on your actual stack instead of hypothetically, that's the agentic-automation work I do.

Sources

[1] Anthropic — Introducing Claude Haiku 5.5 — anthropic.com [2] VentureBeat — Anthropic launches Claude Haiku 5.5 with 90% API price reduction, matching GPT-6 Luna — venturebeat.com [3] OpenRouter — Claude Haiku 5.5: API Pricing & Benchmarks — openrouter.ai

The short version

• Claude Haiku 5.5 (Oct 7) runs $0.10/$0.50 per million input/output tokens under 100k tokens — a 90% cut from Haiku 4.5's $1.00/$5.00, and an exact price match to OpenAI's GPT-6 Luna.
• It's built for the boring, high-volume work: classification, summarization, DB queries, subagent tasks — not creative or multi-step reasoning.
• The adjustable effort dial (Low to Max) roughly doubles Terminal-Bench performance from medium to max effort — "cheap" and "capable" are two different settings, not one price.
• Run the 5-minute test: isolate your classification/summarization token spend, check what model is running it, multiply by the price cut, see if it clears a few hundred dollars a month.
• Skip the switch if the task needs real reasoning, a wrong answer is costly, the bucket's under $50/month, or you don't have an eval set to check against.