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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, or token cost dominates—and verify that it meets your quality bar on representative tasks. For security work, do not treat Anthropic’s safeguard settings as a capability ranking: Haiku 5.5 allows a wider range of defensive cyber tasks than Sonnet 5.5 under Anthropic’s policies, but the available evidence does not establish a universal winner at finding or fixing vulnerabilities.
This comparison uses the current 5.5 models: Anthropic’s system-card index dates Haiku 5.5 to October 2026 and Sonnet 5.5 to September 2026. Older Haiku 4.5 and Sonnet 4.5 benchmark results are included only as historical context, not as evidence for a current head-to-head result.
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The short answer: pick by task shape, not by model name
Claude Sonnet 5.5 is the safer default when a task requires understanding an unfamiliar repository, making a coordinated change across files, reviewing a pull request, planning an agent workflow, or maintaining accuracy over several turns. Anthropic describes Sonnet 5.5 as a model for well-scoped everyday work across coding, agents, and knowledge work.
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#1 Best Overall
For defensive security, define “better” before choosing. It might mean stronger vulnerability discovery, better patch suggestions, fewer refusals on legitimate analysis, lower cost, or more restrictive safeguards. Anthropic’s current material supports a safeguard distinction, not a universal capability ranking. Use authorized, reviewed workflows with either model.
Haiku 5.5 and Sonnet 5.5 at a glance
| Decision factor | Claude Sonnet 5.5 | Claude Haiku 5.5 |
|---|---|---|
| Anthropic’s positioning | Well-scoped everyday coding, agents, investigation, review, and knowledge work | Lower-cost, high-efficiency model; teams should validate quality on their own tasks |
| Input price | $2 per million tokens | $0.10 per million tokens for prompts up to 100,000 tokens; $0.50 per million for prompts over 100,000 tokens |
| Output price | $10 per million tokens | $0.50 per million tokens for prompts up to 100,000 tokens; $2.50 per million for prompts over 100,000 tokens |
| Security safeguards | Anthropic’s current Sonnet safeguard baseline | Anthropic says safeguards permit a wider range of defensive tasks than its Sonnet 5.5 safeguards, while still blocking penetration testing and other high-risk techniques |
| Access listed by Anthropic | Claude.ai, Claude Platform, and listed cloud channels | Claude.ai and listed cloud channels |
| Best starting point | Complex or consequential work where correction time matters | Repetitive, high-volume, or latency-sensitive work after a quality trial |
Prices are vendor-published rates and can change by date, region, platform, provider, and product configuration. The rates above are not a complete cost estimate: caching, batch processing, context length, retries, tool calls, and provider fees can materially change the bill.
Which Claude model is best for coding?
Use Sonnet 5.5 for repository-level work
Choose Sonnet first when the model must build a mental model of an existing system rather than produce an isolated snippet. Typical examples include tracing a request through several services, changing an API and its callers, updating tests and fixtures together, migrating a configuration format, or reviewing a pull request for regressions.
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Sonnet is also the better starting point for agent loops that inspect files, run tests, interpret failures, and revise a patch. The relevant measure is not just whether the first code compiles. It is how often the model reaches an accepted change with limited human intervention, how much context it can keep straight, and how reliably it follows repository conventions.
Trial Haiku 5.5 for bounded, repeatable coding tasks
Haiku is a strong candidate when each request has a narrow contract and a fast automated check. Examples include generating unit-test cases from a function signature, converting data between known schemas, writing repetitive boilerplate, explaining compiler diagnostics, extracting symbols from files, or producing standardized documentation blocks.
Keep the task boundary explicit. Give Haiku the relevant file or interface, state the output format, and attach a test or validator. If the model’s output is consumed by another automated step, validate it before it reaches production. A small reduction in per-request cost is valuable at scale, but only when retries and manual cleanup remain low.
Use a split workflow when both speed and depth matter
Many teams can assign different stages to different models:
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- Use Haiku to classify tickets, summarize logs, draft test matrices, or propose initial approaches.
- Use Sonnet to investigate the repository, choose among competing designs, implement the accepted change, and perform the final review.
- Run deterministic linters, tests, type checks, dependency scanners, and policy checks independently of either model.
This arrangement is not automatically cheaper or safer. Measure the total number of model calls and the human time needed to correct the handoff between stages.
Is Haiku cheaper than Sonnet?
At the published token rates, Haiku 5.5 is substantially less expensive for prompts up to 100,000 tokens: $0.10 per million input tokens and $0.50 per million output tokens, compared with Sonnet 5.5 at $2 and $10. For Haiku prompts over 100,000 tokens, the listed rates rise to $0.50 per million input tokens and $2.50 per million output tokens.
| Example token usage | Sonnet 5.5 at listed rates | Haiku 5.5, prompt up to 100k | Haiku 5.5, prompt over 100k |
|---|---|---|---|
| 1 million input tokens | $2 | $0.10 | $0.50 |
| 1 million output tokens | $10 | $0.50 | $2.50 |
These figures are rates, not a prediction of your invoice. A coding agent may send the repository context repeatedly, invoke tools, retry failed calls, or generate long outputs. Cached context and batch-processing discounts may change effective Sonnet costs, while the provider you use may apply its own terms. Recalculate with your actual input, output, cache, retry, and tool-call volumes, and recheck Anthropic’s current pages before committing to a budget.
For a fair comparison, calculate cost per accepted result rather than cost per request. If Haiku needs three attempts and a developer spends ten minutes repairing the result while Sonnet succeeds once, the nominal token-price difference may not represent a real saving.
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Separate capability from safeguards
Anthropic states that Haiku 5.5 safeguards “permit a wider range of defensive tasks than our safeguards for Sonnet 5.5,” while still blocking penetration testing and techniques considered more likely to be used by attackers. This describes what the safety layer permits, not which model is more capable of discovering, exploiting, or repairing a weakness.
A model can refuse fewer legitimate requests yet produce weaker analysis. Conversely, a more restrictive model may still be highly effective on the defensive tasks it allows. The current official material does not provide a common, current Haiku 5.5-versus-Sonnet 5.5 benchmark that resolves this trade-off.
Use either model only in an authorized workflow
Keep authorization and human review outside the model. Before testing a production-like system, document the owner, scope, dates, permitted techniques, data handling rules, and escalation contact. Do not let an agent independently expand the target, send unreviewed exploit traffic, or apply a patch to production.
- Vulnerability triage: provide sanitized findings and ask for prioritization, evidence requirements, and remediation options.
- Secure code review: ask the model to identify data-flow risks, missing authorization checks, unsafe parsing, secrets exposure, and weak validation; have a reviewer confirm each finding.
- Patch development: require a minimal diff, regression tests, and an explanation of why the change closes the reported path without breaking legitimate behavior.
- Detection engineering: use the model to draft rules or queries, then test them against known benign and malicious samples before deployment.
- Incident support: use it to organize timelines and propose investigative questions, not as the sole authority for containment or attribution.
When Haiku’s broader defensive allowance helps
Haiku may be more convenient when a legitimate defensive task is blocked by a stricter safeguard or when a security team needs to process a large queue of low-risk analysis requests. That convenience does not remove the need to keep requests within the approved scope, and it does not demonstrate that Haiku finds more vulnerabilities.
When Sonnet is the better security starting point
Start with Sonnet when the work involves joining evidence across a large codebase, reasoning about a multi-step data flow, comparing several remediation designs, or maintaining a detailed investigation over many turns. Confirm the result with reproducible tests and a qualified reviewer; model fluency is not evidence that a vulnerability exists or that a patch is complete.
What the published benchmark numbers actually tested
The following figures belong to earlier model generations and specific evaluation setups. They should not be placed on a chart as if Haiku 5.5 and Sonnet 5.5 were tested against each other under one protocol.
Haiku 4.5 coding result
In its October 15, 2025 announcement, Anthropic reported 73.3% on SWE-bench Verified for Haiku 4.5 under the described harness and setup. The same announcement said Haiku 4.5 delivered similar coding performance to Sonnet 4 at one-third the cost and more than twice the speed. Those statements concern Haiku 4.5 and Sonnet 4, not the current 5.5 pair. See Anthropic’s Haiku 4.5 announcement.
Sonnet 4.5 cyber-defender results
Anthropic’s 2025 cyber-defender report described Sonnet 4.5 achieving 76.5% success on Cybench over 10 attempts for a specified subset of 37 of 40 tasks. It also reported 28.9% on CyberGym under a $2-per-vulnerability API-query cost limit and 66.7% success reproducing vulnerabilities with 30 trials. These are historical, setup-dependent results, not evidence that Sonnet 5.5 beats Haiku 5.5. The report also warns that patch assessment can produce false negatives when a valid patch differs from the human reference and that real-world defensive security is more complex than evaluations. Read the cyber-defender report for the stated methods and limitations.
Haiku 4.5 safety-evaluation figures
Anthropic’s transparency material reported 87.7% assistance on legitimate security research and development tasks and 99.2% refusal of harmful requests with enhanced safety measures for a particular Haiku 4.5 evaluation. It also reported evaluation awareness in about 9% of some Haiku 4.5 test scenarios. These percentages depend on the model version, safeguards, prompts, and test method; they are not current Haiku 5.5 rates or a measure of coding capability. See the Transparency Hub and Haiku 4.5 system card.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to run a fair Haiku-versus-Sonnet evaluation
Because no independent current head-to-head result establishes a winner, test both models on the work your team actually performs.
- Define an authorized task set. Include representative coding changes, reviews, security analyses, and remediation tasks. Remove secrets and restrict every security task to systems you own or are explicitly authorized to test.
- Freeze the task specification. Give both models the same repository snapshot, issue text, documentation, tool access, context limits, and system-level constraints.
- Set an attempt budget. Record the maximum turns, retries, tool calls, and wall-clock time. Do not allow one model unlimited chances while the other gets one attempt.
- Predefine acceptance tests. Use tests, type checks, linters, security reproductions, and review criteria that can be applied consistently.
- Blind the first review when practical. Remove model names from patches and ask reviewers to score correctness, completeness, maintainability, security impact, and policy compliance.
- Record operational metrics. Capture task success, human correction time, latency, refusal rate, number of retries, input and output tokens, tool failures, and total cost.
- Inspect failures, not only averages. One severe security mistake or a consistently missed authorization check may matter more than a small average-speed advantage.
- Repeat after prompt and tool changes. A new system prompt, context strategy, retrieval layer, or agent tool can change the outcome enough to invalidate an earlier comparison.
Report the exact model IDs, date, provider, settings, tools, attempt count, and review rubric with your internal result. This makes the decision reproducible and prevents a benchmark number from being mistaken for a permanent model ranking.
Practical decision guide
| Your situation | Recommended starting model | Reason |
|---|---|---|
| Changing several parts of an unfamiliar application | Sonnet 5.5 | Prioritizes repository understanding and coordinated implementation |
| Reviewing a consequential pull request | Sonnet 5.5 | Leaves more room for cross-file reasoning and detailed critique |
| Generating thousands of routine transformations | Haiku 5.5 | Lower published token rates and fast responses can matter at volume |
| Drafting tests or documentation with automated validation | Haiku 5.5 trial | Bounded output makes quality easier to check cheaply |
| Tracing a vulnerability through multiple services | Sonnet 5.5 trial | Requires sustained context and careful reasoning; verify every conclusion |
| High-volume defensive triage within an approved scope | Haiku 5.5 trial | Broader stated defensive allowance and lower rates may improve throughput |
| Any task involving production changes or exploit traffic | Neither without human control | Authorization, review, testing, and change controls are mandatory regardless of model |
Access, version checks, and operational caveats
Anthropic lists Sonnet 5.5 for Claude.ai, Claude Platform, and listed cloud channels, while Haiku 5.5 is listed for Claude.ai and listed cloud channels. Availability, regional terms, data controls, rate limits, and exact model identifiers can differ by channel. Confirm those details in the provider console before deployment.
For release dates and model-card links, consult Anthropic’s model system-card index. Pin the exact model ID in application configuration rather than relying on a moving display label. Recheck pricing and safety documentation when a model version changes.
Bottom line
Use Sonnet 5.5 as the default for difficult coding, repository investigation, code review, and sustained agent execution. Use Haiku 5.5 where speed and low token cost are central, then keep it only if a controlled sample proves that its accepted-result rate and correction time meet your standard. In defensive security, choose based on the specific task and safeguard behavior—not on refusal percentages or marketing labels—and keep authorization, automated checks, and human approval in the loop.
Quick Recap
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