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What Claude Haiku 5.5 is—and which Haiku you are actually using
Anthropic’s current Haiku model is claude-haiku-5-5, released on October 7, 2026. Anthropic positions it for high-volume, latency-sensitive work such as classification, extraction, routing, and subagent tasks. That positioning makes it a practical fit for many repetitive coding operations and for narrowly defined security checks, provided your application verifies the result.
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“Claude Haiku” is not one immutable model name. Anthropic’s lifecycle information lists Haiku 4.5 as active, while Haiku 3 and Haiku 3.5 are retired. The retirement notices identify Haiku 4.5 as the recommended replacement for those older versions. If an existing integration still points at an older model identifier, check its lifecycle status before changing prompts or code.
| Model | Status in Anthropic’s current lifecycle information | How to interpret it |
|---|---|---|
claude-haiku-5-5 |
Active and latest; released October 7, 2026 | Use for a new Haiku evaluation or migration target, subject to compatibility checks. |
| Claude Haiku 4.5 | Active | Still a supported model; do not assume a 4.5 integration is automatically compatible with 5.5. |
| Claude Haiku 3 | Retired | Plan a migration rather than starting new production work on it. |
| Claude Haiku 3.5 | Retired | Plan a migration rather than starting new production work on it. |
Anthropic lists a 1-million-token context window and a 128,000-token maximum output for Haiku 5.5. These are vendor specifications, not independent measurements of coding accuracy, security detection, latency, or cost. A large context limit lets you supply more repository material, but it does not remove the need to select relevant files and control what the model can access.
#1 Best Overall
Where Haiku fits in a coding workflow
Haiku is a useful first-pass worker when the task is well bounded: explain a function, add a small test, classify findings, extract fields from logs, route a request to a specialized subagent, or make a localized patch. The model-selection question is not whether Haiku is universally the best coding model. It is whether its speed and operating cost meet your measured success threshold for a particular workload.
Good starting tasks
- Repository navigation and concise summaries of selected files.
- Small, reversible changes with explicit acceptance criteria.
- Test generation for an existing function or endpoint.
- Classification and triage of compiler, linter, or scanner findings.
- Extraction of structured data from logs, tickets, or alerts.
- Routing work to another agent or queue after applying a clear rule set.
- Repeated subagent tasks where each invocation has a narrow scope.
Tasks that need stronger controls
- Authentication, authorization, payment, deployment, data-retention, or cryptographic changes.
- Dependency upgrades that alter a large portion of the build graph.
- Changes that can delete data, publish releases, rotate credentials, or contact production systems.
- Security conclusions where a missed vulnerability has a material impact.
- Any workflow that treats untrusted documents, webpages, email, or tool output as instructions.
For the second group, Haiku can still draft, inspect, classify, or propose a patch. Keep the final decision and consequential action behind deterministic checks and human approval.
A reliable step-by-step coding workflow
1. Define the task and scope before opening tools
State the desired behavior, the files that may change, the files that must not change, and the command that proves success. Include the relevant language, framework, test location, and constraints such as backward compatibility or performance limits. Do not give a repository-wide write permission for a one-file fix.
Do these 3 things before closing this tab:
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Allowed files: src/import/account.ts and test/import/account.test.ts.
Do not change: database migrations, deployment files, or authentication middleware.
Acceptance: invalid email addresses return 400; existing valid imports remain unchanged.
Required check: run the focused test command, then the project type-check.This structure prevents the model from guessing what “done” means and makes its later report auditable.
2. Ask for an inspection and plan before a risky patch
For a change with security, data, or dependency implications, request a short plan first. Ask the model to identify relevant files, assumptions, edge cases, and the checks it will run. Review that plan before granting write access. A plan is not proof that the implementation is correct; it is a way to catch an incorrect scope while the change is still cheap to redirect.
3. Keep permissions narrow
Allow read access only to the project material needed for the task. Grant write access to a working directory or branch rather than to a whole machine. Keep network access, shell commands, secret stores, deployment systems, and production data unavailable unless a specific step requires them. If a command can modify state, require the model to show the command and obtain approval before execution.
Rank #2
4. Make verification an explicit completion condition
Anthropic warns that at low or medium effort Haiku 5.5 can sometimes claim that code changes are complete without running a check. Put the requirement directly in the instruction:
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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.
Tell the agent to report the exact command, its exit status, and the meaningful result. “Looks correct” is not a test result. If the check cannot run, the completion state should be “not verified,” with the reason and the next command a developer should run.
5. Match the check to the change
- Logic change: run focused unit or integration tests that exercise the modified path.
- Type or interface change: run the project type-checker and relevant tests.
- Build or dependency change: run the production build and dependency or lockfile checks used by the project.
- CLI change: run the changed command with representative valid and invalid inputs.
- Configuration change: run the parser, validator, or dry-run command that consumes the configuration.
Use the project’s own commands rather than assuming a generic command is sufficient. A green test that never touches the edited path does not establish that the change works.
6. Review the diff and side effects yourself
Inspect the final diff, generated files, lockfiles, migrations, and command output. Pay special attention to dependency additions, authentication logic, authorization checks, data handling, logging, and error paths. Confirm that the agent did not broaden the patch, weaken a control to make a test pass, or introduce a secret into source or logs. Model output is an explanation of proposed work, not evidence that the work is safe.
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A useful agent report has at least four parts: files changed, behavior implemented, checks run with results, and unresolved risks or follow-up work. Your process can then require human approval before merge, release, migration, or production execution. This separation is especially important when a fast subagent is invoked many times in parallel.
Prompt patterns that improve coding results
For a small patch
Inspect only the files listed below. First describe the current behavior and a minimal plan. Make the smallest patch that satisfies the acceptance criteria. Do not edit unrelated files. Before reporting completion, run the specified test or type-check command and report the exact command and result.
Files: ...
Acceptance criteria: ...
Required command: ...For code-change verification
Review the current diff against the acceptance criteria. Identify any missing tests, regressions, security implications, or unrelated edits. Run the focused check and then the broader project check if available. Report each command, exit status, and failure output. If a check cannot run, do not call the change complete.For a review without write access
You have read-only access. Analyze the selected files for the stated risk. Return findings with file and line references, severity, reasoning, and a concrete remediation. Do not claim that a vulnerability is confirmed when the available evidence only supports a possibility.These prompts make the desired evidence and boundary explicit. They do not guarantee correctness; they make unsupported completion claims easier to detect.
Using Haiku for security work without turning it into a security boundary
Haiku 5.5 can be used as a lightweight screening or triage component: classify an alert, extract indicators, summarize a code path, identify likely vulnerability patterns, or route a case for deeper review. It is one layer in a defense-in-depth design. Do not give it secrets or powerful actions merely because it is performing a security task, and do not treat a model response as the sole authorization for a consequential operation.
Apply least privilege
- Expose only the files, records, and metadata needed for the current decision.
- Keep credentials, tokens, private keys, and production data outside the prompt whenever possible.
- Use separate credentials and a sandbox for tools invoked by the agent.
- Make destructive, external, or privileged actions unavailable by default.
- Constrain outputs to the smallest useful schema, such as a finding list with fixed severity values.
Least privilege limits damage if the model misunderstands a request or if hostile content manipulates it. It also makes audit logs and incident analysis clearer.
Treat every external source as untrusted
Prompt injection can arrive through tool output and third-party content, not only through the user’s message. Web pages, issue descriptions, emails, documents, repositories, log lines, and scanner results may contain text that looks like an instruction. Pass such material as data, preserve its source, and tell the model that it cannot override the system’s task or permissions.
Screen or filter tool output where the risk justifies it, and consider presenting only the fields needed for the decision. Keep the original artifact available for human review rather than silently rewriting it. Sandbox tools and limit network access so that an injected instruction cannot turn a read operation into an external action.
Red-team the complete workflow
Test the agent with deliberately adversarial documents and tool results before deployment. Include instructions that ask for secrets, attempt to change the task, request outbound communication, or disguise commands as code comments or log entries. Verify that the application preserves permission boundaries, records the event, and returns a safe, parseable result.
Rank #4
Testing only the prompt is insufficient. The attack surface includes retrieval, parsing, tool wrappers, approval logic, output handling, retries, and downstream automation.
Handle screening refusals explicitly
Anthropic notes that Haiku 5.5 safety classifiers can decline some requests, including some benign cybersecurity requests. Your application must treat refusal as a defined outcome, not as an empty answer to retry until the model complies.
- Record that the classifier refused and preserve the request context needed for review.
- Stop any automated action that depended on the missing classification.
- Route the case to a human or to a separately approved, deterministic procedure.
- Return a machine-readable refusal state distinct from “no issue found.”
- Avoid repeatedly sending the same request in the hope of bypassing the safeguard.
A refusal is not proof that the request is malicious, and a successful response is not proof that it is safe. Both outcomes belong in the workflow’s decision logic.
Examples of defensive security tasks
Code-review triage
Provide the model with a constrained diff and the relevant coding standard. Ask it to identify possible injection, access-control, secret-handling, or unsafe-deserialization issues, cite the affected lines, and label each item as a hypothesis or a finding supported by the shown evidence. A reviewer or dedicated scanner should confirm the result before merge.
Alert classification
Give Haiku only the alert fields needed to route a case. Require a fixed output such as category, confidence band, evidence fields, and escalation requirement. Reject outputs that contain extra instructions or fail schema validation. The routing policy—not free-form prose—should decide whether a ticket is escalated.
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Use a sandboxed parser to extract relevant fields before sending them to the model. Redact credentials and unrelated personal data. Ask for a summary and indicators, not for an unrestricted action plan. Preserve the original log and the model’s structured result so an analyst can reproduce the decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing Haiku 5.5 versus another model
Anthropic’s guidance is to start with an efficiency-sensitive trial, evaluate the real application, and move to another model only when the evaluation shows a capability gap. Do not assume that the fastest or least expensive option is appropriate for every coding or security task.
| Evaluation axis | What to measure in your workflow |
|---|---|
| Task success | Whether the requested change, classification, extraction, or review outcome is actually achieved. |
| Correctness | Human- or test-verified accuracy, including false positives and false negatives where relevant. |
| Human review | How much checking and rework each result requires before acceptance. |
| Latency | Time to a usable result under your normal concurrency and context size. |
| Cost | Total application cost for successful outcomes, including retries, tool calls, and review. |
| Tool behavior | Whether the model selects permitted tools, follows scope, and reports command results accurately. |
| Refusal rate | How often legitimate tasks require a documented fallback or human route. |
| Missed-issue impact | The consequence of an undetected defect or vulnerability in this particular workflow. |
Run a representative, versioned test set rather than judging from a few impressive examples. Include normal cases, edge cases, malformed input, adversarial content, and cases where the correct answer is to ask for clarification or refuse an action. Record the model version, prompt, tools, checks, and outcome so a later model change can be compared fairly.
Migrating an existing Haiku 4.5 integration
Do not switch the model identifier and assume the rest of the integration will continue to work. Anthropic’s October 7, 2026 release notes warn that code written for Haiku 4.5 can break on Haiku 5.5. The named compatibility risks include legacy manual extended thinking, temperature, top_p, top_k, assistant prefills, and older computer-use tool names.
- Inventory every request parameter, message pattern, tool definition, and response parser used by the current integration.
- Read the current Haiku 5.5 release notes and migration guidance for the API or cloud platform you use.
- Run the existing automated test set against a non-production 5.5 configuration.
- Check validation errors, output parsing, tool calls, refusals, token limits, and retry behavior—not just successful text responses.
- Update incompatible patterns according to the migration documentation, then repeat the full evaluation.
- Roll out gradually with logging and a rollback path, keeping the previous supported model available until the new path is proven.
Compatibility is platform-specific. Confirm the exact request schema and tool names in the documentation for your deployment rather than copying an older example.
Context-window and output planning
The 1-million-token context window and 128,000-token maximum output can accommodate large repositories or long security records, but sending everything is rarely the safest design. Select the files, history, and artifacts that bear directly on the task. Large irrelevant context increases exposure to hostile instructions, makes review harder, and can obscure the evidence that supports a conclusion.
For long-running work, divide the process into stages: inventory, focused analysis, patch or classification, verification, and human approval. Persist structured intermediate results instead of asking a single call to remember an entire investigation. Set application-level limits on input size, output size, tool duration, and retries even when the model supports larger limits.
Operational checklist
- Confirm that the integration uses an active model, especially after the retirement of Haiku 3 and 3.5.
- Define allowed files, tools, commands, network access, and acceptance criteria before the call.
- Request a plan for risky changes and review it before granting write access.
- Require a real test, type-check, build, or changed command before accepting a code change.
- Record the exact verification command and result.
- Review diffs, dependency changes, authentication logic, data handling, and side effects.
- Keep secrets and unnecessary privileges out of the agent environment.
- Treat all retrieved or tool-produced content as untrusted data.
- Sandbox tools and red-team the workflow with adversarial inputs.
- Represent refusals as an explicit state with a safe fallback.
- Evaluate success, correctness, review effort, latency, cost, tool behavior, refusal rate, and missed-issue impact.
- Read the 5.5 migration guidance before changing a 4.5 integration.
Bottom line: use Haiku where speed is measurable and controls are enforceable
Claude Haiku 5.5 is a strong candidate for fast, repeated coding assistance, repository triage, extraction, routing, and first-pass security analysis. Its 1-million-token context and 128,000-token output limits are useful capabilities, but they do not establish correctness or make the model a security boundary. The dependable pattern is narrow scope, least privilege, untrusted-input handling, explicit refusal paths, real verification commands, and human review for consequential decisions. Start with an evaluated workload; keep Haiku when it meets your measured quality and risk threshold, and choose another model when the evidence shows a capability gap.
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