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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Start with Claude Sonnet 5.5 for codebase investigation, scoped implementation, code review, and sustained agent work. Choose Claude Haiku 5.5 when response speed, request volume, and token cost dominate—and verify that it still meets your team’s quality bar on representative tasks. For security work, neither model can be declared the universal winner from the currently published evidence: Anthropic describes different safeguard policies, not a current head-to-head capability result.
This comparison covers the current 5.5 family. Anthropic’s system-card index dates Haiku 5.5 to October 2026 and Sonnet 5.5 to September 2026, so older Haiku 4.5 and Sonnet 4.5 figures below are historical context rather than evidence about today’s models.
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Quick answer: pick by task shape, not by the model name
Claude Sonnet 5.5 is the better default for work where the model must understand a repository, preserve constraints across many files, reason through an unfamiliar system, or execute a multi-step coding agent loop. Anthropic positions it for well-scoped everyday coding, agents, investigation, review, and knowledge work. See Anthropic’s current product description at the Sonnet product page.
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Claude Haiku 5.5 is the efficiency choice. Its lower published token rates and faster-response positioning make it attractive for high-volume transformations, first-pass generation, simple fixes, log or alert triage, and interactive tools where latency matters. That does not establish that it produces equivalent code. Test Haiku on your own repositories and acceptance tests before making it the default.
#1 Best Overall
For defensive cybersecurity, separate three questions:
- Capability: Which model finds, explains, or fixes a vulnerability more accurately?
- Safeguards: Which requests will the model allow, limit, or refuse?
- Operations: Which model fits your authorization, logging, data-handling, and human-review process?
Anthropic’s current material answers the safeguards question differently for the two models, but it does not publish a current, common-protocol Haiku 5.5 versus Sonnet 5.5 security benchmark. Do not infer capability from a refusal policy.
What is current in the 5.5 comparison?
The relevant pair is Haiku 5.5 and Sonnet 5.5, not Haiku 4.5 versus Sonnet 4.5. Anthropic lists both models through its own services and selected cloud channels; availability, regional access, data terms, and deployment controls depend on the provider and date. Verify those details before committing an application.
| Model | Current positioning | Published token rates | Typical starting use |
|---|---|---|---|
| Claude Sonnet 5.5 | Well-scoped coding, agents, investigation, review, and knowledge work | $2 per million input tokens; $10 per million output tokens | Repository changes, debugging, design review, sustained agent sessions |
| Claude Haiku 5.5 | Lower-cost, high-efficiency model for speed and volume | $0.10 input/$0.50 output per million tokens for prompts up to 100,000 tokens; $0.50 input/$2.50 output per million tokens above 100,000 | Repetitive coding, classification, triage, drafts, and latency-sensitive workflows |
These are vendor-listed rates, not a guaranteed invoice total. Prompt and completion lengths, cached context, batch processing, retries, tool calls, provider markups, and deployment terms all affect spend. Recheck prices at the time you deploy; Anthropic’s current Haiku information is on the Haiku 5.5 page.
Which Claude is best for coding?
Choose Sonnet 5.5 for repository-level work
Use Sonnet first when the task requires investigation before editing. Examples include tracing a request through several services, changing an API without breaking consumers, upgrading a dependency across configuration and tests, or reviewing a pull request whose correctness depends on architectural context. Sonnet’s positioning specifically covers coding, agents, investigation, and review—the combination that makes these tasks difficult for a fast, inexpensive model.
A practical Sonnet workflow is:
- Give it a bounded objective. State the intended behavior, non-goals, supported versions, and files or services in scope.
- Require inspection before modification. Ask the agent to identify relevant entry points, data flows, tests, and assumptions before proposing a patch.
- Use tools with least privilege. Provide read access broadly enough for analysis, but restrict writes, secrets, production credentials, and network actions.
- Run the project’s checks. Have the agent execute the approved formatter, compiler, unit tests, integration tests, and static checks.
- Review the diff yourself. Confirm behavior, error handling, migration safety, dependency changes, and whether tests actually cover the requirement.
Sonnet can still make incorrect assumptions or produce plausible but unsafe changes. Its model tier is not a substitute for tests or review.
Use Haiku 5.5 for bounded, repeatable coding tasks
Haiku is a sensible first trial when each request has a short context, a clear output format, and an inexpensive way to detect errors. Suitable examples include converting a set of function comments into documentation, generating test-case skeletons, normalizing configuration files, classifying compiler diagnostics, writing boilerplate adapters, or suggesting small, isolated fixes.
Haiku is also useful as a first-pass worker in a pipeline: classify or summarize with Haiku, then send ambiguous cases or high-impact changes to Sonnet. This can reduce average cost without pretending that every task has the same quality requirement.
Set an explicit quality gate. A Haiku-generated patch should compile, pass the same tests as a Sonnet patch, and meet review criteria such as API compatibility, security checks, and maintainability. If human correction time erases the token savings, the cheaper model is not cheaper for that task.
Should you use Sonnet or Haiku for code review?
For a security-sensitive or cross-cutting review, start with Sonnet because the reviewer must reconstruct intent and evaluate interactions across files. Haiku can provide valuable parallel checks—for example, looking for missing tests, obvious null handling, inconsistent naming, or repeated patterns—but treat those findings as screening, not final approval.
A reliable review setup asks the model to cite file paths and line ranges, distinguish observed facts from hypotheses, and state what it could not inspect. Require a human to adjudicate severity and merge decisions.
Cost, context size, and latency trade-offs
Published rates are not total cost
At the listed rates, a request with 100,000 input tokens and 10,000 output tokens would cost approximately $0.30 on Haiku 5.5 at the up-to-100,000-token tier ($0.10 input plus $0.50 output per million tokens), versus $0.30 on Sonnet 5.5 only if the same arithmetic is applied incorrectly—Sonnet’s calculation is $0.20 input plus $0.10 output, or $0.30 total for that particular token mix. Change the output to 30,000 tokens and the totals diverge: Haiku would be about $0.40 and Sonnet about $0.70. Actual billing can differ with caching, batches, provider terms, and retries.
For Haiku prompts over 100,000 tokens, Anthropic lists the higher $0.50 input and $2.50 output rates. Large repository contexts can therefore change the economics substantially. Before choosing a model on price alone, measure the context sent, output generated, number of attempts, tool calls, and human rework.
Latency and throughput
Haiku’s value is greatest when many independent requests must complete quickly: ticket enrichment, alert deduplication, code search summaries, or generation of repetitive scaffolding. Sonnet is usually easier to justify when one successful, deeply reasoned run is worth more than many fast attempts. Record time to first token, total completion time, timeout frequency, and queue behavior in your own provider environment; the product pages do not establish one universal latency figure for every platform or region.
Caching and batching
Anthropic’s Sonnet information describes caching and batch savings. Your effective cost depends on whether shared instructions, repository context, or tool results are cacheable and how often they change. A cost model that counts only the nominal input rate can be badly wrong for an agent that repeatedly sends large, uncached context.
Which Claude is better for cybersecurity?
There is no evidence in the current official material for a universal Haiku 5.5 or Sonnet 5.5 security winner. “Better” could mean finding more vulnerabilities, writing safer patches, refusing fewer legitimate requests, or imposing stricter limits on dangerous techniques. Those are different outcomes.
Capability: vulnerability discovery and remediation
For authorized source review, threat modeling, secure-code explanation, and patch drafting, begin with Sonnet when the work spans architecture or requires sustained reasoning. Give Haiku a controlled trial for repetitive static-analysis explanations, alert classification, test generation, and narrowly scoped remediation suggestions.
Do not let either model directly deploy a security fix. Require reproducible findings, a minimal proof of the issue in a non-production environment, a proposed patch, regression tests, and human approval. Keep secrets, customer data, and production credentials out of prompts unless your contractual and technical controls explicitly permit them.
Safeguards are policy, not a capability score
Anthropic states that Haiku 5.5 safeguards permit a wider range of defensive cybersecurity tasks than its Sonnet 5.5 safeguards, while still blocking penetration testing and other techniques considered more likely to be used by attackers. The statement describes allowed-use boundaries. It does not show that Haiku discovers vulnerabilities more accurately or that Sonnet is less useful to defenders.
Design requests around authorization and defensive purpose, but expect refusals for high-risk instructions. Do not attempt to bypass safeguards by disguising a target, splitting an exploit into steps, or claiming permission you cannot verify. Use approved internal labs, synthetic targets, and documented change-control procedures.
Operational controls matter more than model selection
- Define the authorized assets, test window, and permitted techniques before a security task begins.
- Log prompts, model IDs, tool calls, outputs, approvals, and resulting changes.
- Use read-only access for reconnaissance and isolated environments for exploit reproduction.
- Require a qualified reviewer to validate severity, exploitability, and remediation.
- Have a rollback plan and preserve the original artifact before applying generated changes.
What the published older benchmarks actually tested
Older figures can provide context about evaluation methods, but they cannot answer the current 5.5-versus-5.5 question. Keep the model version, harness, task set, attempt count, and cost limit attached to every number.
| Historical result | What it measured | Why it is not a current head-to-head result |
|---|---|---|
| 73.3% SWE-bench Verified | Anthropic’s Haiku 4.5 announcement reported this result under a specified harness and setup. | It tested Haiku 4.5, not Haiku 5.5, and was not a common Haiku 5.5/Sonnet 5.5 comparison. See Anthropic’s Haiku 4.5 announcement. |
| 76.5% Cybench success over 10 attempts | Sonnet 4.5 performance on the specified subset of 37 of 40 tasks. | It is a previous model generation and a defined subset, not a current pairwise result. |
| 28.9% CyberGym success under a $2-per-vulnerability API-query limit | Sonnet 4.5 results under that cost-constrained setup. | The budget and benchmark design limit what can be inferred about general defensive work. |
| 66.7% vulnerability reproduction over 30 trials | Another Sonnet 4.5 CyberGym result with 30 attempts. | Different generation, task setup, and trial count from any Haiku 5.5 evaluation. |
| 87.7% assistance on legitimate security research and development tasks; 99.2% refusal of harmful requests | Haiku 4.5 transparency evaluations under particular prompts and safeguards. | Historical rates tied to a specific model, test method, and context; not Haiku 5.5 behavior. |
Anthropic’s cyber-defender report also cautions that evaluations cannot capture the full complexity of real-world defensive security. It notes that patch assessment can produce false negatives when a valid patch differs from the human reference. Read the methodology in Anthropic’s cyber-defender report rather than treating one score as a production guarantee. Historical safety-evaluation material is available through Anthropic’s Transparency Hub and the Haiku 4.5 system card.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A reproducible way to choose for your team
Run a small internal evaluation instead of relying on generic benchmark headlines. Use the same model access path, tools, context limits, and permissions for both models.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →- Assemble representative tasks. Include a routine coding change, an unfamiliar-repository investigation, a code review, a secure patch, a log-triage task, and one task that should be refused or escalated.
- Freeze the environment. Record exact model IDs, provider, temperature or equivalent settings, system instructions, tool versions, repository revision, and context supplied.
- Set a fixed attempt budget. Give each model the same number of runs and the same timeout and tool limits. Do not keep retrying only the model that performs worse.
- Blind the review where practical. Remove model names from diffs and have qualified reviewers score correctness, completeness, security, maintainability, and required edits.
- Measure the whole workflow. Track task success, human correction minutes, latency, refusals, failed tool calls, token use, retries, and total cost.
- Check safety separately. Record whether each model correctly refused unauthorized or high-risk requests, and whether it remained useful on approved defensive tasks.
- Choose per task class. A blended policy—Haiku for high-volume screening and Sonnet for escalations—may outperform a single default.
Common mistakes and how to avoid them
Using old benchmarks as current proof
Model names change faster than benchmark articles. Label every result with its release, date, harness, task set, and attempt count. Never place Haiku 4.5 and Sonnet 4.5 numbers in a chart that implies Haiku 5.5 and Sonnet 5.5 were tested together.
Choosing on token price alone
A cheaper completion that needs repeated retries or extensive human repair can cost more than one Sonnet run. Include review time and failed attempts in your estimate.
Confusing refusal behavior with security quality
A model that refuses more requests may be following a stricter policy, not demonstrating better vulnerability analysis. Score allowed-task usefulness and high-risk refusal behavior as separate dimensions.
Giving an agent excessive authority
Neither model should receive unrestricted production access merely because it can call tools. Scope permissions, isolate environments, require approvals, and preserve rollback paths.
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| If your priority is… | Start with… | Reason |
|---|---|---|
| Understanding a large or unfamiliar codebase | Sonnet 5.5 | Investigation and sustained context are central to the task. |
| Implementing a cross-file change with tests and review | Sonnet 5.5 | Use the model positioned for scoped coding and agent work. |
| High-volume, repetitive transformations | Haiku 5.5 | Lower listed rates and an efficiency-oriented profile can reduce cost and latency. |
| Fast alert, log, or ticket classification | Haiku 5.5, with escalation | Route uncertain or high-impact cases to Sonnet and human reviewers. |
| Authorized vulnerability research or patch design | Sonnet 5.5 initially | Start with deeper reasoning; validate Haiku on a controlled task set. |
| Strictly defined defensive requests with fewer policy restrictions | Evaluate Haiku 5.5’s safeguards | Anthropic says its safeguards permit a wider range of defensive tasks than Sonnet’s, but this is not a capability claim. |
For most engineering teams, the practical policy is simple: make Sonnet 5.5 the escalation and complex-work default, use Haiku 5.5 where measured quality is sufficient, and keep human authorization and review for every consequential code or security action.
Quick Recap
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