Comparison
Claude Haiku vs Sonnet — When to use which
The most common model decision developers face. Haiku is fast and cheap. Sonnet is more capable. The right answer depends entirely on your task — here's how to think about it.
Haiku — Use when
- High volume, cost is a real constraint
- Classification, routing, extraction tasks
- Latency is critical (<500ms first token)
- Simple, well-defined tasks with clear rubrics
- You've validated quality is acceptable
Sonnet — Use when
- Quality directly affects your product's reputation
- Complex instructions or nuanced judgment required
- Customer-facing chat or document analysis
- Coding, debugging, and technical writing
- Default production model for most apps
Haiku (input / output per MTok)
$0.80 / $4.00
Sonnet (input / output per MTok)
$3.00 / $15.00
Typical cost difference
3–4× cheaper
Verify at anthropic.com/pricing
By task type
Excellent for structured tasks, simple Q&A, classification, and extraction. Noticeably weaker on nuanced reasoning or multi-step logic.
Handles complex reasoning, nuanced writing, and ambiguous instructions reliably. The workhorse model for production apps that need real quality.
Fastest Claude model. Noticeably snappier in chat interfaces. Best for latency-sensitive use cases where you want <1s first-token.
Fast enough for most production use cases. Streaming masks latency well. Noticeably slower than Haiku on high-volume tasks.
Substantially cheaper per million tokens. For high-volume use cases (support, classification, pipelines), Haiku can cut costs by 70–90% vs Sonnet.
More expensive, but the cost is justified for tasks that actually need the quality. Wrong choice for bulk classification or simple data extraction.
Follows clear, simple instructions well. Degrades with very long or contradictory system prompts. Needs tighter, simpler prompts to perform consistently.
Reliable with complex, multi-part system prompts. Handles edge cases in instructions better. More forgiving of prompt imprecision.
Handles simple code generation and explanation well. Misses edge cases and writes less idiomatic code on complex tasks. Not for production code review.
Strong coding model. Use for code generation, debugging, and refactoring. Close to Opus for most everyday coding tasks at a fraction of the cost.
Good at extraction, summarization, and classification from documents when the task is well-defined. Less reliable for synthesis across multiple sources.
Better at synthesis, inference, and nuanced interpretation. Use when the analysis requires judgment, not just pattern-matching.
Excellent choice. Fast, cheap, reliable for binary or multi-class classification with a clear rubric. The right model for 95% of classification pipelines.
Overkill for most classification. Unless your categories are genuinely ambiguous and need deep reasoning, Haiku is the better pick.
Acceptable for simple FAQ-style support. Tone is slightly flatter. May stumble on edge cases that require nuanced judgment.
Better emotional calibration, more consistent adherence to complex policies. Worth the cost for any customer-facing use case where quality reflects on your brand.
The decision rule
Start with Sonnet. Test with Haiku once your prompts are stable. If Haiku passes your evals on a representative sample of real inputs — use Haiku in production. If it fails, you now have evidence for why Sonnet is worth the cost.
The mistake most teams make: choosing Haiku up front because it's cheap, then debugging quality issues in production. The model switch has real cost — in prompt re-tuning, in user trust, in engineering time. Earn the cost savings by validating first.