Claude Haiku 5.5: the real cost of high-volume AI workflows
Haiku 5.5 starts at $0.10/M input tokens, but pricing doubles down on prompt size and migration details. See a worked cost example and routing checklist.
Anthropic launched Claude Haiku 5.5 on October 7, 2026, aiming it at repetitive, latency-sensitive work such as classification, document summaries, customer-support responses and small agent subtasks. The headline price is striking: for prompts up to 100,000 tokens, standard API rates are $0.10 per million input tokens and $0.50 per million output tokens. But that price is only useful when teams understand the prompt-size threshold, token usage and the quality required for a completed task.
The pricing has two tiers—not one flat rate
For prompts up to 100,000 tokens, Haiku 5.5 costs $0.10 per million input tokens and $0.50 per million output tokens. For prompts over 100,000 tokens, those rates rise to $0.50 and $2.50. Cache reads are $0.01 or $0.05 per million tokens respectively; cache writes are $0.125 or $0.625. These are Anthropic's published rates, not an estimate of the cost of a finished workflow. Prompt length, generated output, caching, retries and tool use determine the actual bill.
Anthropic's published API list prices (USD per 1M tokens)
| Model / prompt tier | Input | Output | Cache read |
|---|---|---|---|
| Haiku 5.5, up to 100k | $0.10 | $0.50 | $0.01 |
| Haiku 5.5, over 100k | $0.50 | $2.50 | $0.05 |
| Haiku 4.5 | $1.00 | $5.00 | $0.10 |
| Sonnet 5.5 | $2.00 | $10.00 | $0.10 |
A worked example for 1,000 small requests
Suppose a support-classification workflow runs 1,000 requests, each using 1,000 billable input tokens and generating 200 output tokens. That totals 1,000,000 input and 200,000 output tokens. At Haiku 5.5's short-prompt rates, the calculation is (1.0 × $0.10) + (0.2 × $0.50) = $0.20. At Sonnet 5.5's published standard rates, the same token counts cost (1.0 × $2.00) + (0.2 × $10.00) = $4.00. The list-price difference in this controlled scenario is 20×. It is not a measured 20× reduction in cost per successful task: models may use different token counts, require different retries or produce different accuracy.
The example assumes all requests remain in Haiku's up-to-100k prompt tier, excludes cache writes, tools, batch discounts, hosting and human review, and holds input/output token counts constant across models. Anthropic's migration guide says the same text can count as roughly 30% more tokens in Haiku 5.5 than Haiku 4.5; recalculate real prompts before forecasting savings.
Which jobs should route to Haiku?
Practical routing hypothesis to validate
| Try Haiku 5.5 first | Escalate to Sonnet or Opus when needed |
|---|---|
| Short summaries, labels and triage | Ambiguous multi-document decisions |
| Extracting a known field from a document | Complex synthesis with many constraints |
| A narrow subagent lookup | Long-horizon agent planning or coding |
| Fast support drafts with review | High-stakes replies needing careful judgment |
The best optimization target is cost per accepted outcome, not cost per million tokens. Define acceptance criteria for each route, sample failed and borderline outputs, and count escalation rates. A cheap first-pass model plus a selective stronger-model fallback can beat an all-premium workflow, but only if routing errors and repeated work do not erase the savings. Anthropic's published benchmark comparisons are vendor-run evaluations; they should guide testing rather than replace it.
Migration checklist: changes that can break an integration
- Replace the model ID with claude-haiku-5-5 for the relevant platform and recount representative prompts using the new tokenizer.
- Revisit max_tokens, since the same text may consume more tokens and adaptive thinking can use the output budget.
- If using explicit thinking budgets, switch to adaptive thinking and calibrate output_config.effort on real tasks.
- Remove unsupported sampling settings such as top_k and old temperature/top_p values; check API validation errors.
- Do not assume the first returned content block is text; handle blocks by type and test refusal handling.
- Replace assistant prefill patterns, review computer-use toolset compatibility, and retest conversation replay rules.
- Run a shadow evaluation comparing accuracy, latency, tokens, retries, escalations and cost per accepted result before shifting production traffic.
The migration is not merely a model-name swap for every Messages API client. Anthropic documents changes to thinking, sampling parameters, prefills and computer-use tools. Managed Agents integrations have a simpler path, but teams with custom API loops should use the official migration checklist. Start with a small traffic slice, watch error rates and response quality, and roll back if the end-to-end workflow gets worse.
Haiku 5.5 makes high-volume, narrowly scoped AI tasks much cheaper on paper, especially below the 100k prompt threshold. Real savings require measuring complete workflows and migrating integrations carefully. Use Sonnet or Opus where the cost of a wrong answer or an extra retry outweighs the cheaper first pass.
Sources & useful resources
- Anthropic: Claude Haiku 5.5 launch— Official launch, October 7, 2026; pricing, positioning, Sonnet cache-read reduction
- Claude Platform: Haiku 5.5 migration guide— Official API migration instructions and tokenizer changes