Claude Haiku vs Sonnet: Routing Guide
Claude Haiku 4.5 costs $1/$5 per million tokens. Sonnet 4.6 costs $3/$15. Here is when Haiku matches Sonnet and when it fails, with routing logic for each task type.
Claude Haiku 4.5 costs $1.00 per million input tokens and $5.00 per million output tokens. Claude Sonnet 4.6 costs $3.00 per million input tokens and $15.00 per million output tokens. That is a 3x gap on input and a 3x gap on output. For teams routing all Anthropic traffic to Sonnet by default, the math breaks down quickly.
The question is not whether to use Haiku. It is: for which tasks does Haiku match Sonnet closely enough that routing to it is safe? And for which tasks does the quality gap justify paying 3x more?
This guide answers both questions with specific task-type breakdowns and routing logic.
Pricing Table
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Relative Cost |
|---|---|---|---|
| Claude Haiku 4.5 | $1.00 | $5.00 | 1x |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 3x |
| Claude Opus 4.6 | $5.00 | $25.00 | 5x |
At 1 million calls per month with an average of 1,000 tokens each (split 600 input / 400 output), Haiku costs approximately $2,400/month. Sonnet costs approximately $7,800/month. The differential is $5,400/month on a fairly conservative traffic estimate.
Benchmark Comparison
The performance gap between Haiku and Sonnet is real, but narrower than the price gap suggests on most practical tasks.
| Benchmark | Claude Haiku 4.5 | Claude Sonnet 4.6 | Notes |
|---|---|---|---|
| MMLU (general knowledge) | ~83% | ~88% | 5 point gap |
| SWE-bench Verified (code) | ~45% | ~79.6% | Large gap on complex software tasks |
| MATH (reasoning) | ~78% | ~92% | Sonnet pulls ahead on hard math |
| HumanEval (code gen) | ~88% | ~95%+ | Sonnet better for complex code |
| Summarization quality | Comparable | Comparable | No meaningful difference |
| Classification accuracy | Comparable | Comparable | No meaningful difference |
The headline: Haiku and Sonnet are close on language tasks. They diverge significantly on complex coding and multi-step reasoning. This maps directly to routing decisions.
Task-by-Task Routing Decision
Route to Haiku
Text summarization. Single-document or multi-document summarization produces output that is nearly indistinguishable from Sonnet output. Teams that have shadow-tested both models on summarization tasks consistently find Haiku adequate. Route here by default.
Classification and extraction. Structured output tasks, categorizing a support ticket, extracting fields from a document, labeling sentiment, do not require frontier-model reasoning. Haiku handles these reliably at 3x lower cost.
Short-form content generation. Email drafts, product descriptions, notification copy, and other sub-500-token generation tasks fall squarely in Haiku's range.
Simple Q&A and chatbot. Customer-facing chatbots with well-defined knowledge domains (product FAQ, support triage) can run on Haiku without users noticing the difference. Response latency is also better on Haiku, which matters for interactive use cases.
Translation. Language translation quality between Haiku and Sonnet is minimal for most language pairs. Route translation traffic to Haiku.
Data formatting and transformation. JSON reshaping, CSV parsing, data normalization instructions, these are deterministic enough that Haiku handles them well.
Route to Sonnet
Complex code generation. The SWE-bench gap of roughly 35 percentage points between Haiku and Sonnet on verified software engineering tasks is meaningful. For feature-level code generation, debugging complex issues, or code review on non-trivial changes, Sonnet is the correct tier.
Multi-step reasoning tasks. Planning tasks, decomposing complex problems, writing architectural analysis, these require the reasoning depth that separates Sonnet from Haiku. The MATH benchmark gap confirms this.
Long-context synthesis. When you are asking the model to synthesize information across a very long context, draw connections, and produce a coherent output, Sonnet's larger effective context utilization matters.
High-stakes writing. Executive reports, investor-facing materials, legal drafting assistance: the quality ceiling matters more than cost here.
Agentic workflows. Multi-turn agent loops where the model must track state, make tool calls, and self-correct benefit from Sonnet's stronger instruction following and reasoning.
The Routing Decision Matrix
| Task Type | Recommended Model | Confidence | Cost Savings vs Sonnet |
|---|---|---|---|
| Summarization | Haiku 4.5 | High | 75% |
| Classification | Haiku 4.5 | High | 75% |
| Extraction | Haiku 4.5 | High | 75% |
| Short-form content | Haiku 4.5 | High | 75% |
| Translation | Haiku 4.5 | High | 75% |
| Simple chatbot | Haiku 4.5 | High | 75% |
| Complex code gen | Sonnet 4.6 | High | 0% |
| Multi-step reasoning | Sonnet 4.6 | High | 0% |
| Long-context synthesis | Sonnet 4.6 | Medium-High | 0% |
| Agent orchestration | Sonnet 4.6 | High | 0% |
| Data transformation | Haiku 4.5 | Medium | 75% |
| Customer support triage | Haiku 4.5 | Medium | 75% |
For a typical SaaS application, the tasks routing to Haiku represent 55-65% of total API calls. That portion of traffic is 75% cheaper, which translates to a 40-50% reduction in total Anthropic spend without touching the tasks that genuinely need Sonnet.
Real Savings Calculation
Assume 1 million API calls per month, with this task distribution:
- Summarization + classification: 40% (400K calls)
- Customer support + chatbot: 25% (250K calls)
- Complex code generation: 20% (200K calls)
- Reasoning + agent tasks: 15% (150K calls)
Average token count: 800 input / 400 output per call.
All Sonnet 4.6:
- Total tokens: 1.2B input, 600M output
- Cost: $3,600 + $9,000 = $12,600/month
Routed (Haiku for first two task groups, Sonnet for last two):
- Haiku traffic (65%): 780M input / 390M output = $780 + $1,950 = $2,730
- Sonnet traffic (35%): 420M input / 210M output = $1,260 + $3,150 = $4,410
- Total: $7,140/month
Monthly savings: $5,460 (43% reduction)
The savings compound with scale. At $50,000/month on Anthropic, the same routing logic saves roughly $21,500/month.
How PromptUnit Routes Between Haiku and Sonnet
PromptUnit's routing engine classifies each incoming request against a task taxonomy. For teams using Anthropic models, the proxy maps task signals to the appropriate Claude tier automatically.
The classification runs on:
- Token count and context depth
- Code detection (backtick presence, language markers)
- Multi-step instruction complexity
- Domain signals (legal, medical, financial content flags)
- Historical quality scores for similar requests
The 14-day observation period runs this classifier against your actual traffic without changing routing behavior. At day 14, you see exactly which of your calls would have gone to Haiku, the projected savings, and the quality confidence scores for each category. You activate routing only after seeing those numbers.
For more on how cross-provider routing works across OpenAI, Anthropic, and Google simultaneously, see Cross-Provider LLM Routing. For the full framework on model routing strategy, see LLM Model Routing: The Complete Guide. For the cost of staying on a single default model, see The Hidden Cost of Defaulting to GPT-4o.
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