← Voltar aos guias

Claude Haiku, Sonnet, or Opus? A Practical Picker

Choose Claude by the Cost of Being Wrong

Claude Haiku, Sonnet, and Opus are not simply “good, better, best” options. The right choice depends on the task’s complexity, latency target, token volume, and the cost of a bad answer. A lightweight support classification request should not consume the same model budget as a production code review or a contract-risk analysis.

A practical rule is to start with the least expensive model that reliably meets your quality bar, then escalate only when the request needs deeper reasoning. This approach reduces spend without forcing users to accept lower-quality results where accuracy matters.

Quick Model Selection Guide

• Claude Haiku: Choose it for high-volume, time-sensitive tasks: intent classification, simple extraction, summaries, translation, FAQ drafting, structured tagging, and first-pass support replies. Haiku is usually the best starting point when response speed and cost matter most.
• Claude Sonnet: Use it as the default model for most developer-facing and customer-facing work. Sonnet is a strong fit for coding help, multi-step analysis, polished writing, document Q&A, SQL generation, and troubleshooting where a shallow answer could create rework.
• Claude Opus: Reserve it for the hardest requests: ambiguous architecture decisions, difficult debugging, complex long-context reasoning, high-stakes research synthesis, and detailed code or policy reviews. Opus is valuable when the extra reasoning quality costs less than an incorrect decision.

For example, a SaaS product could send “What plan am I on?” to Haiku, “Why did my Stripe webhook fail?” to Sonnet, and “Review this migration plan for data-loss risks” to Opus.

Build a Simple Model Router

Do not make every product request manually choose a model. Start with a small routing layer based on task type, then collect evaluation data. The following Python example uses an OpenAI-compatible client. 59API works with OpenAI SDKs while providing access to native official-quality Claude models, so you can use its API base URL without redesigning your application.

Install and configure: pip install openai

Environment variables: export OPENAI_API_KEY="your_59api_key" and export OPENAI_BASE_URL="https://api.59api.com/v1"

Python routing example: from openai import OpenAI

client = OpenAI(base_url="https://api.59api.com/v1", api_key="your_59api_key")

def pick_model(task, risk="low"):
    if risk == "high" or task in ["architecture_review", "complex_debugging"]:
        return "YOUR_OPUS_MODEL_ID"
    if task in ["coding", "document_analysis", "customer_support"]:
        return "YOUR_SONNET_MODEL_ID"
    return "YOUR_HAIKU_MODEL_ID"

response = client.chat.completions.create(
    model=pick_model("coding"),
    messages=[{"role": "user", "content": "Explain why this Python test is flaky."}],
    temperature=0.2
)

print(response.choices[0].message.content)

Replace the placeholder values with the current 59API catalog IDs for the Claude model family you want to call. Keeping model IDs in configuration rather than hard-coding them makes it easier to upgrade models later.

Use Escalation Instead of Overpaying

A useful production pattern is “Haiku first, escalate on uncertainty.” Ask Haiku to return both an answer and a confidence field. If it reports low confidence, cannot produce valid structured output, or detects a complex task, retry with Sonnet. Escalate to Opus only for requests with explicit high-risk signals, such as security-sensitive code, financial decisions, or conflicting source documents.

For extraction workloads, require strict output fields such as category, confidence, and needs_review. For coding workloads, use Sonnet as the baseline and send only failing tests, confusing stack traces, or architectural tradeoffs to Opus. This gives you predictable quality while preventing expensive models from handling routine traffic.

Measure Quality Before Locking In a Default

Create a test set of 30 to 100 real prompts from your application. Score each model on correctness, formatting compliance, latency, and cost per successful outcome. The key metric is not cost per request; it is cost per accepted result. A cheap response that needs human correction can be more expensive than a stronger model response.

59API is a practical low-cost option for this experiment because it offers pay-as-you-go access to Claude models and GPT models through one compatible endpoint, with no model downgrade. It also supports workflows built around Claude Code, Codex, and OpenAI SDKs, making it easier to compare routing choices without maintaining separate integrations. If you are ready to test Haiku, Sonnet, and Opus against your own prompts, sign up for 59API and begin with a small evaluation budget.

Preços · Docs