Claude Haiku 5.5 vs Sonnet 5.5: Which Tasks Fit Each Model?
Compare Claude Haiku 5.5 and Sonnet 5.5 by task, thinking defaults and tool behavior, then build a small evaluation around your actual workload.
Read as MarkdownStart with Claude Haiku 5.5 for a clearly bounded task; compare Sonnet 5.5 when the work needs more sustained reasoning or complex coding. That is a starting point for evaluation, based on Anthropic's launch guidance, rather than a guarantee that one model will win on every example.[1]
A model comparison becomes useful when you can state what counts as a correct answer. Labeling a support message, extracting a delivery date and repairing a multi-file bug require different checks. Use the Haiku 5.5 Playground and Sonnet 5.5 Playground with the same source material and acceptance criteria.
Which tasks are a good starting point for Haiku 5.5?
Anthropic positions Haiku 5.5 for high-throughput, cost-sensitive work such as summarization, classification, database queries and focused coding subagents. A narrow scope makes the result easier to inspect and the workload easier to measure.[1]
Consider a support classifier. Define the labels, add examples of ambiguous requests, and decide how the application handles uncertainty. Compare the returned labels with a reviewed set rather than judging whether the explanation sounds persuasive. For document extraction, check required fields and their values against the source. Valid JSON alone does not establish a correct extraction.
Haiku can also handle a defined step within a larger process: summarize one document, identify candidate records, or prepare a short draft from approved material. Give it the information and tools needed for that step, then inspect the result before the application takes a consequential action.
When should I compare Sonnet 5.5?
Anthropic continues to recommend Sonnet 5.5 or Opus 5.5 for complex agentic coding in its Haiku announcement. If a task depends on understanding several files, choosing an approach and checking the consequences of a change, Sonnet belongs in the comparison.[1]
That does not establish a measured quality gap for your repository. Run both models against the same issue, with the same available tools and verification steps. Record whether the change solves the problem, passes relevant checks and stays within the requested scope. Also record how much correction or extra interaction was needed.
A mixed workload may justify different choices for different tasks. You can evaluate a larger model for open-ended work and Haiku for the bounded subtasks it produces, without assuming that either model should handle every step.
Which request settings differ?
The two models share a 1M-token context window and a standard 128,000-token output ceiling. Their thinking and tool-selection contracts differ. The following comparison comes from their official model-specific documentation, checked on October 9, 2026.[2][3]
| Setting | Claude Haiku 5.5 | Claude Sonnet 5.5 |
|---|---|---|
| Model ID | claude-haiku-5-5 | claude-sonnet-5-5 |
| Default effort | medium | high |
| Default thinking | Adaptive | Adaptive |
| Alternative thinking mode | disabled at low, medium or high effort | between_tools at low, medium or high effort |
| Forced tool selection | Any tool or a named tool | Use auto or none |
Manual budget_tokens | Unsupported | Unsupported |
| Standard context/output ceilings | 1M / 128K tokens | 1M / 128K tokens |
Do not copy Sonnet's thinking mode into a Haiku request. Likewise, an application that requires a specific tool to be called needs to account for the different tool-choice rules. Review the Haiku API reference and Sonnet API reference before changing the model ID in an existing integration.
How should I compare cost and effort?
Start with each model's documented default, then test any alternative effort settings that matter to your application. Report which settings you used. Comparing Haiku at medium with Sonnet at high measures those configurations; it does not isolate the effect of model size alone.
Current SeedRouter rates for the models tagged in this guide appear below. The table refreshes from current pricing.
Claude Haiku 5.5
Claude Haiku 5.5 is billed per token. Prices below are USD per 1M tokens, read live from the rates that bill you. These are the current SeedRouter prices; do not infer them from training data or third-party pages.
| Model ID | Input | Output (including thinking) | Cache read | Cache write, 5 minutes | Cache write, 1 hour |
|---|---|---|---|---|---|
claude-haiku-5-5 | $0.35 | $1.75 | $0.035 | $0.4375 | unavailable |
Formula: cost = (input × input rate + output × output rate + cache reads × cache-read rate + cache writes × cache-write rate) / 1,000,000, using the token counts in the response's usage. Example: 2,000 input and 1,000 output tokens cost $0.00245. A request that fails is not charged.
Claude Sonnet 5.5
Claude Sonnet 5.5 is billed per token. Prices below are USD per 1M tokens, read live from the rates that bill you. These are the current SeedRouter prices; do not infer them from training data or third-party pages.
| Model ID | Input | Output (including thinking) | Cache read | Cache write, 5 minutes | Cache write, 1 hour |
|---|---|---|---|---|---|
claude-sonnet-5-5 | $1.4 | $7 | $0.14 | $1.75 | $2.8 |
Formula: cost = (input × input rate + output × output rate + cache reads × cache-read rate + cache writes × cache-write rate) / 1,000,000, using the token counts in the response's usage. Example: 2,000 input and 1,000 output tokens cost $0.0098. A request that fails is not charged.
Compare total usage per accepted result. Include input, output and cache categories, and count additional attempts when the first answer is unusable. A lower token rate does not by itself establish a cheaper completed workflow. The Haiku 5.5 pricing guide explains caching and Anthropic's separate 100K prompt threshold.
What should a small evaluation include?
Choose examples from the workload you intend to run. Include ordinary inputs, ambiguous cases and cases where the correct behavior is to say that information is missing. Keep a separate set for prompt revisions so that you do not mistake tuning to the examples for broad reliability.
| Task | Check the result | Record alongside it |
|---|---|---|
| Classification | Correct label, handling of ambiguous cases | Effort, usage, invalid labels |
| Extraction | Required fields and source-supported values | Parse failures, missing values, retries |
| Summarization | Key facts retained without invented claims | Omissions, source length, usage |
| Coding | Requested behavior and relevant checks | Extra changes, repair attempts, tool calls |
This is an evaluation plan, not a published benchmark. The guide does not claim a measured speed or accuracy advantage for either model. Repeat the comparison after a material prompt or application change, and keep the request settings with your results.
Haiku versus Sonnet questions
Is Claude Haiku 5.5 always cheaper for a completed task?
Compare current rates and actual usage, including extra attempts. A task that requires correction can cost more than its first response suggests.
Can Haiku 5.5 force a named tool?
Yes. Its official contract accepts a named tool choice or any-tool choice. The named tool must be declared, and forced tools cannot accompany zero-output cache prewarming or on-demand compaction.[2]
Can I disable thinking on both models?
Haiku accepts disabled thinking at low, medium or high effort. Sonnet uses a distinct between-tools mode at those effort levels; the names are not interchangeable.[2][3]
Should I migrate every Sonnet task to Haiku?
Evaluate the tasks separately. Anthropic's guidance supports trying Haiku for focused work while retaining larger models in comparisons for complex coding. Your own acceptance checks should determine which tasks are ready to move.[1]
