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Use claude-haiku-5-5 when you need fast, high-volume coding assistance, code classification, routing, extraction, or lightweight security analysis. Make verification a hard completion condition: Claude must run the relevant test, type-check, build, or changed command and report the result. For security-sensitive agents, give it only the permissions and data it needs, treat every external document or tool result as untrusted, and design an explicit path for refusals.
Anthropic released Haiku 5.5 on October 7, 2026. Its listed 1M-token context window and 128K-token maximum output are vendor specifications, not independent performance measurements. Haiku 4.5 remains active, while Haiku 3 and 3.5 are retired, so “Haiku” is not a single interchangeable model.
Which Claude Haiku model should you use?
For a new integration, start with claude-haiku-5-5, the current latest Haiku model listed by Anthropic as active. It is positioned for high-volume, latency-sensitive work, including classification, extraction, routing, and subagent tasks. That positioning makes it useful for many coding workflows and for narrowly scoped security screening, but it does not establish that Haiku is the best choice for every difficult programming or security problem.
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| Model | Lifecycle status | What the current documentation establishes |
|---|---|---|
claude-haiku-5-5 |
Active; released October 7, 2026 | Latest Haiku model; designed for high-volume and latency-sensitive work. Anthropic lists a 1M-token context window and 128K-token maximum output. |
claude-haiku-4-5 |
Active | Anthropic lists it as an available model and recommends checking migration guidance before moving an existing integration to 5.5. |
| Claude Haiku 3 | Retired | Listed in Anthropic’s retirement notices as retired; Haiku 4.5 is the recommended replacement in those notices. |
| Claude Haiku 3.5 | Retired | Listed in Anthropic’s retirement notices as retired; Haiku 4.5 is the recommended replacement in those notices. |
Model names, lifecycle dates, platform availability, safety behavior, and API compatibility can change. Check the current Anthropic model overview, lifecycle page, release notes, and migration guidance before changing production traffic.
#1 Best Overall
What Haiku is good at in a coding workflow
Haiku is most useful when the task can be bounded, repeated, and checked automatically. Good candidates include:
- Explaining a function, error, stack trace, or unfamiliar module.
- Generating a small, well-scoped patch from an explicit specification.
- Writing or updating unit tests, fixtures, type definitions, and documentation.
- Classifying files, routing issues to specialized agents, and extracting structured information from code.
- Performing first-pass lint, dependency, configuration, or secret-exposure reviews.
- Handling subagent tasks where low latency and predictable output format matter.
Large context is useful when an issue spans many files. Haiku 5.5’s listed 1M-token context window can accommodate a substantial repository snapshot, but context capacity does not guarantee that every file is relevant or that a generated patch is correct. Select the files and instructions that matter, rather than blindly attaching an entire repository.
A reliable step-by-step coding workflow
1. Define the task and boundaries
State the desired behavior, the files or modules in scope, constraints, and how success will be checked. Tell the model what it may change and what it must leave alone. Include the language, framework, runtime, and relevant commands when they are known.
Task: Add input validation to the account-import endpoint.
Scope: src/import/accounts.ts and tests/import/accounts.test.ts only.
Behavior: Reject missing or malformed email addresses with HTTP 400; preserve existing response shape.
Do not change database schema, authentication, or dependencies.
Completion requires the focused test command and a type-check.This prevents a broad request such as “fix the importer” from turning into an uncontrolled rewrite.
2. Ask for a plan before risky changes
For authentication, deployment, data handling, dependency updates, migrations, or other high-impact changes, request a short plan and risk list first. Review the plan before allowing edits. For a small, reversible change, asking for a minimal patch may be enough.
First inspect the relevant files and propose a three-step plan. Identify assumptions and security risks. Do not edit files until the plan is approved.Keep tool permissions scoped to the project and task. An agent that only needs to edit source files should not receive production credentials, unrestricted network access, or deployment authority.
Rank #2
3. Make verification an explicit completion condition
Anthropic warns that at low or medium effort Haiku 5.5 can sometimes report that code changes are complete without running checks. Prevent that failure mode by putting verification in the request, not as an optional afterthought.
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When you change code that can be run, built, or type-checked, run a real check that exercises the change before reporting it done: the project's tests, type-checker, or build, or the changed command itself. Report the exact command, exit status, and material output. If a check cannot run, say why and do not claim completion.Use the narrowest check that genuinely exercises the change, then add broader checks when the risk warrants them. A passing formatter is not evidence that behavior is correct; a focused test may be necessary, followed by the project’s type-check or build.
4. Inspect the diff and side effects
Read the final diff yourself. Confirm that only intended files changed, no credentials or generated artifacts were added, and dependency or configuration changes are justified. Review command effects such as migrations, file deletion, network access, and package installation. A model’s report is not evidence that a change is correct, secure, or suitable for production.
5. Separate implementation from approval
Use one request to implement and verify, and a separate human review to approve the result. For sensitive changes, require a second check—such as a targeted security review, a different test suite, or a human maintainer—rather than treating the same model’s summary as an independent control.
Prompt patterns for code changes and verification
Small patch
Inspect the current implementation first. Make the smallest patch that satisfies the specification below.
Requirements:
- Preserve the public API.
- Add tests for the new behavior and one regression case.
- Do not add dependencies.
Before reporting completion:
1. Run the focused tests.
2. Run the project type-checker.
3. Show the exact commands and whether each passed.
4. Summarize files changed and any remaining uncertainty.Debugging
Reproduce the failure from the supplied error and relevant files. Explain the likely cause before editing. Make one minimal fix, then run the command that previously failed and the nearest regression tests. If reproduction is impossible, list the missing prerequisite instead of claiming a fix.Structured output for automation
When another program consumes the answer, require a strict schema and reject anything that does not parse. Keep free-form explanations outside the machine-readable object or omit them entirely. Constrained output makes routing and screening more predictable, but it does not make the underlying judgment correct.
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Use Haiku as one layer in a defensive workflow, not as the security boundary. Appropriate tasks include triaging findings, identifying suspicious patterns, summarizing a vulnerability report, checking configuration against a stated policy, and prioritizing remediation for human review. Requests involving exploitation or other cyber activity may be declined by Anthropic’s safety classifiers, including some requests that a user considers benign. Your application must handle a refusal explicitly; repeatedly sending the same request is not a reliable recovery strategy.
Apply least privilege
- Expose only the files, repositories, tools, and actions required for the task.
- Do not provide production secrets, private keys, tokens, or customer data when a redacted or synthetic substitute will work.
- Use read-only access for analysis whenever edits are unnecessary.
- Require a separate approval step for destructive actions, deployment, privilege changes, or data exfiltration.
- Sandbox tools and constrain network destinations where the platform allows it.
Treat external content as hostile input
Web pages, emails, issue descriptions, documents, repository files, and tool results can contain prompt injection. A sentence embedded in a fetched document is data to analyze, not an instruction that outranks your system policy or task scope.
Mark untrusted content clearly in the prompt, keep it separate from control instructions, and prevent it from expanding permissions. Screen tool output where the risk justifies it. For high-impact workflows, log the source of each instruction and retain enough context to investigate an unexpected action.
Build an explicit refusal branch
A security-screening call can refuse. Design the surrounding application so that a refusal becomes a safe, parseable state such as needs_human_review, rather than an empty result interpreted as “no issue found.” Do not allow a failed classifier call to automatically approve a file, permit a deployment, or suppress an alert.
Red-team the complete workflow
Test the agent with deliberately adversarial documents, issue text, web pages, and tool responses. Include attempts to override instructions, request secrets, broaden file access, trigger unsafe commands, or disguise an instruction as a test result. Evaluate the whole chain—prompt, tools, permissions, parser, refusal handling, logging, and approval—not just the model’s text in isolation.
How to verify security findings
Use evidence-based checks
Ask Haiku to identify the suspected issue, affected code path, assumptions, and a reproducible validation step. Then confirm the finding with deterministic tools, a test, a static analyzer, a dependency advisory, or a qualified reviewer. A plausible explanation is a lead for investigation, not proof of a vulnerability.
Control remediation suggestions
Require proposed fixes to preserve authentication, authorization, input validation, logging, and error-handling invariants. Ask for a regression test that would fail before the fix and pass after it. Review whether the patch merely hides a finding, weakens monitoring, or introduces a new trust boundary.
Rank #4
Protect sensitive evidence
Minimize the data sent for triage. Redact credentials, personal information, proprietary source, and exploitable details when they are not needed to answer the question. Keep retention and access policies aligned with the sensitivity of the material.
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Anthropic’s guidance is to start with an efficiency-sensitive trial, evaluate the actual application, and move to another model only when an evaluated capability gap justifies it. There is no universal coding or security benchmark in the available documentation for your workload.
| Evaluation axis | What to measure | Why it matters |
|---|---|---|
| Task success | Percentage of tasks completed to the stated acceptance criteria | Shows whether the model solves the work, not merely produces plausible prose. |
| Correctness | Passing tests, type-checks, builds, and validated security findings | Catches silent implementation errors and false positives. |
| Human review | Time and expertise required to approve or repair the output | A fast model can still be expensive if every result needs extensive rework. |
| Latency and cost | Measured response time and per-task spend in your application | Determines whether high-volume routing or subagent use benefits from Haiku. |
| Tool behavior | Correct tool selection, argument accuracy, and adherence to scope | Unsafe or unnecessary actions can outweigh speed gains. |
| Refusal rate | How often required defensive tasks are declined | Refusals need a safe fallback and may affect operational coverage. |
| Missed-issue impact | Consequences of an incorrect approval or missed vulnerability | High-consequence work may require a stronger model or mandatory human review. |
Build a representative evaluation set from your own repositories, coding conventions, security policies, and historical incidents. Compare models on the same prompts, tools, context, and acceptance tests. Keep a model that meets the required quality and safety threshold at the lowest operational burden; do not select solely on speed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Migration from Haiku 4.5 to Haiku 5.5
Do not assume an existing Haiku 4.5 integration is drop-in compatible. Anthropic’s October 7, 2026 release notes warn that code written for Haiku 4.5 can break on Haiku 5.5. Review the exact migration guidance for your API or cloud platform before changing the model identifier.
The release notes specifically call out these potential breaking areas:
- Legacy manual extended-thinking patterns.
temperature,top_p, andtop_kparameters.- Assistant prefills.
- Older computer-use tool names.
Test the integration in a staging environment. Exercise normal responses, tool calls, structured output, timeouts, refusals, long-context requests, and error handling. Pin or record the model identifier during rollout so a later lifecycle change cannot silently alter behavior.
Operational checklist
- Confirm that
claude-haiku-5-5is available on the API or cloud platform you use. - Define task scope, allowed files, tools, network destinations, and expected behavior.
- Use synthetic or redacted data unless sensitive information is essential.
- Ask for a plan before high-impact edits.
- Require a real test, type-check, build, or changed command before completion is reported.
- Inspect the diff, command effects, dependencies, and generated files.
- Parse structured output strictly and fail closed on malformed output.
- Handle model refusal as an explicit state that routes to a safe fallback or human review.
- Test prompt-injection resistance with hostile documents and tool results.
- Measure success, correctness, review effort, latency, cost, tool behavior, refusals, and missed-issue consequences.
- Recheck lifecycle and migration documentation before production upgrades.
Common failure modes and fixes
“The agent says it is done, but no check ran.”
Make the verification command a written acceptance criterion and require the exact command and result in the response. If the command could not run, treat the task as incomplete.
“A document changed what the agent tried to do.”
Classify documents and tool results as untrusted data, isolate them from control instructions, reduce permissions, and add adversarial tests that attempt instruction hijacking.
“A security classifier returned nothing.”
Distinguish an empty, malformed, timed-out, or refused response from a clean result. Route ambiguous outcomes to human review instead of granting approval.
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Check the 5.5 migration guidance for legacy thinking controls, sampling parameters, assistant prefills, and computer-use tool names. Re-test in staging before restoring production traffic.
“The model finds too many security issues.”
Require a precise finding format containing location, evidence, exploitability assumptions, confidence, and a validation step. Measure false positives against a labeled internal set and keep human review for consequential decisions.
What the published specifications do—and do not—tell you
The 1M-token context and 128K-token maximum output indicate the documented limits for Haiku 5.5; they do not predict your application’s accuracy, latency, or cost. Actual results depend on prompt design, tool latency, repository structure, context selection, concurrency, and the checks surrounding the model. Treat Anthropic’s positioning and safety guidance as product documentation, not as an independent test of your implementation.
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