Haiku 5.5 for high-volume work: sorting, tagging and routing
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A look at where the fastest, cheapest Claude model fits in enterprise workflows, and where it falls short.
Haiku 5.5 is the prep cook of the Claude lineup: fastest and cheapest, at about fifty cents per million tokens written. It is built for repetitive work at huge volume.
What it does well
- Sorting incoming requests into categories
- Tagging documents or support tickets
- Routing messages to the right team
- Extracting simple fields from consistent forms
These tasks are clear and well-defined, so a small model can handle them accurately, and the savings add up fast at scale.
Where to be careful
- Ambiguous or judgment-heavy tasks
- Anything involving complex planning
- Work with high consequences for errors, such as clinical or financial decisions
In those cases, use a larger model or keep a human in the review loop.
A practical pattern
Run the high-volume pass on a cheap setting, check results against a clear standard, and redo only the misses on a stronger model. In the guide's example, running low first and redoing misses scored about 97% at 17 cents per task, versus 95% at 29 cents with everything on high.
Questions to ask before deploying
- What accuracy do we need, and how will we measure it?
- What happens to items the model gets wrong?
- What data is the model seeing, and is that appropriate?
The last question matters for privacy and fairness. Our Responsible AI-by-Design Framework covers these checks. For automation ideas, see the AI Agents Store.