Originally published at claudeguide.io/claude-opus-when-to-use
Claude Opus: When to Use It and What Makes It Different
Claude Opus is Anthropic's most capable model — designed for tasks that require sustained multi-step reasoning, deep synthesis, and high-stakes judgment. At $5/M input and $25/M output tokens (1.67x the cost of Sonnet), Opus is worth the premium for complex legal analysis, research synthesis across dozens of documents, orchestrating multi-agent pipelines, and edge-case code reasoning. For most coding, content generation, and data extraction tasks, Sonnet is good enough and significantly cheaper.
What Opus is and where it fits
Opus sits at the top of the Claude model family. It is not simply a "bigger Sonnet" — it makes qualitatively different judgments on ambiguous tasks, catches subtle errors that Sonnet misses, and maintains coherence across very long reasoning chains.
The tradeoff is cost and latency. Opus tokens cost 5x what Sonnet tokens cost, and responses are slower. Every application that reaches for Opus by default is paying a tax on tasks that do not need it.
The right mental model: Opus is a senior expert you call in for hard problems. Sonnet handles the day-to-day.
Pricing comparison
| Model | Input | Output | Context |
|---|---|---|---|
| claude-haiku-4-5 | $1.00/M | $5.00/M | 200K |
| claude-sonnet-4-6 | $3.00/M | $15.00/M | 200K |
| claude-opus-4-7 | $5.00/M | $25.00/M | 200K |
At 1,000 requests per day with an average of 500 input tokens and 1,000 output tokens per request:
- All-Sonnet: ~$16.50/day
- Opus orchestrator + Sonnet execution (10% Opus, 90% Sonnet): ~$17.35/day
- All-Opus: ~$27.50/day
Opus is now 1.67x Sonnet rather than 5x, making selective use still worthwhile for quality on hard tasks — but no longer the dramatic cost cliff it once was. The 80/15/5 routing model (80% Haiku, 15% Sonnet, 5% Opus) remains the most cost-effective structure for mixed workloads.
Where Opus meaningfully outperforms Sonnet
1. Complex multi-step reasoning
Legal contract review, advanced mathematics, and financial modelling that require chained inferences — tasks where an error in step 3 corrupts every subsequent step — are where Opus separation from Sonnet is most visible.
A Sonnet prompt asked to analyse a 40-page commercial lease for hidden assignment restrictions may miss a cross-reference between clause 12.4(b) and schedule 3. Opus follows the full dependency chain.
2. Orchestrator role in multi-agent systems
Opus excels at planning, task decomposition, and quality judgment — the control-plane work in a multi-agent pipeline. It writes better research plans, creates tighter task boundaries, and produces synthesis that holds up across many sub-results.
Sonnet and Haiku execute those tasks reliably. The orchestrator pattern (described below) combines the strengths of both.
3. Deep synthesis from many documents
Summarising 50 research papers, reconciling contradictory evidence across a document set, or producing a coherent report from fragmented source material demands sustained attention to what each source actually says versus what it implies. Opus handles conflicting evidence without flattening nuance.
4. Long-form creative work requiring coherence
Book chapters, long proposals, and multi-section technical documents that need consistent voice, internal consistency, and structural integrity over thousands of words benefit from Opus's stronger working-memory-equivalent behaviour.
5. Edge cases in code reasoning
Subtle race conditions in concurrent code, complex type inference in generic systems, and security-relevant logic errors (time-of-check/time-of-use bugs, integer overflow paths) are tasks where Sonnet occasionally misses the edge case and Opus catches it. For production code touching authentication, payments, or data integrity, the cost difference is trivial against the risk.
Where Sonnet is good enough
Sonnet handles the following at high quality with no practical gap versus Opus:
- Most coding tasks — feature implementation, refactoring, test writing, documentation
- Customer support — intent detection, FAQ matching, draft responses
- Content generation — blog posts, emails, product descriptions, social copy
- Data extraction — pulling structured fields from unstructured text
- RAG pipelines — answering questions from retrieved context
- Translation and summarisation — standard-length documents
If your evaluation shows Sonnet scoring 92% on a task and Opus 94%, the 2-point quality gain rarely justifies a 5x cost increase at scale. Run the numbers before defaulting to Opus.
Decision framework: start with Sonnet, upgrade with evidence
-
Default to Sonnet. Build your first version using
claude-sonnet-4-5. - Measure quality on a representative sample. Define a concrete quality metric (accuracy, human preference score, error rate).
- Run the same sample on Opus. If the quality delta is measurable and consequential for your use case, switch.
- Consider the orchestrator pattern first. Before paying Opus rates across all requests, test whether Opus-as-planner plus Sonnet-as-executor achieves the same quality improvement at a fraction of the cost.
- Segment by task type. Route easy sub-tasks to Haiku, standard tasks to Sonnet, and genuinely hard tasks to Opus. Most applications have a natural split.
The orchestrator pattern
The most cost-effective use of Opus is to handle the planning and synthesis steps in a pipeline while Sonnet does the execution. Opus creates a research plan, Sonnet executes each research step, Opus synthesises the results. You pay Opus rates for a small fraction of the tokens.
python
# Opus for planning, Sonnet for execution
def run_research_pipeline(topic: str) -
[→ Get the Cost Optimization Toolkit — $59](https://shoutfirst.gumroad.com/l/msjkda?utm_source=claudeguide&utm_medium=article&utm_campaign=claude-opus-when-to-use)
*30-day money-back guarantee. Instant download.*
---
## Related guides
- [Claude Model Routing: Automatic Selection Between Haiku, Sonnet, and Opus](/claude-model-routing-automatic)
- [Claude API Cost Optimization: Four Techniques That Stack](/claude-api-cost-optimization-guide)
- [Claude Multi-Agent Orchestration Patterns](/claude-multi-agent-orchestration)
Top comments (0)