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 $15/M input and $75/M output tokens (5x 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 | $0.80/M | $4/M | 200K |
| claude-sonnet-4-5 | $3/M | $15/M | 200K |
| claude-opus-4-5 | $15/M | $75/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): ~$8.40/day
- All-Opus: ~$82.50/day
Choosing Opus selectively rather than universally reduces costs by 80–90% while preserving quality where it actually matters.
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.
# Opus for planning, Sonnet for execution
def run_research_pipeline(topic: str) -> dict:
# Step 1: Opus creates the research plan
plan_response = client.messages.create(
model="claude-opus-4-5",
max_tokens=1024,
messages=[{"role": "user", "content": f"Create a 5-step research plan for: {topic}. Return as JSON array."}]
)
steps = json.loads(plan_response.content[0].text)
# Step 2: Sonnet executes each step
results = []
for step in steps:
result = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=2048,
messages=[{"role": "user", "content": f"Research task: {step}"}]
)
results.append(result.content[0].text)
# Step 3: Opus synthesises
synthesis = client.messages.create(
model="claude-opus-4-5",
max_tokens=2048,
messages=[{"role": "user", "content": f"Synthesise these research findings into a report:\n\n{chr(10).join(results)}"}]
)
return {"plan": steps, "synthesis": synthesis.content[0].text}
In this five-step pipeline, Opus handles two calls (plan + synthesis) and Sonnet handles five (one per research step). Assuming similar token counts per call, roughly 30% of tokens go through Opus and 70% through Sonnet — producing a blended cost close to Sonnet while delivering Opus-quality planning and synthesis.
Frequently asked questions
When is Claude Opus worth the cost?
Opus is worth the cost when quality failure has a real consequence: a missed clause in a contract, a wrong conclusion in a research report, a subtle security bug in production code, or a poorly structured multi-agent plan that cascades errors downstream. If the task is low-stakes or the quality difference between Opus and Sonnet is within your acceptable error range, Sonnet is the better choice.
How much more expensive is Claude Opus compared to Sonnet?
Opus costs $15/M input tokens and $75/M output tokens. Sonnet costs $3/M input and $15/M output. That is a 5x price difference on both input and output. At scale, this difference is significant: 1,000 daily requests that cost $16.50/day on Sonnet cost $82.50/day on Opus.
Can I use Opus as the orchestrator and Sonnet for execution?
Yes, and this is the recommended pattern for complex workflows. Opus plans and synthesises; Sonnet executes. The orchestrator pattern typically uses 20–35% of total tokens at Opus rates, reducing cost by 60–80% compared to running everything through Opus while preserving the quality of the final output.
Does Opus perform better at coding than Sonnet?
For most coding tasks — feature implementation, refactoring, writing tests — Sonnet is good enough. Opus's advantage in code is narrow: it catches subtle edge cases (race conditions, type inference errors, security logic bugs) that Sonnet occasionally misses. For production code touching critical paths, that margin matters. For routine development work, use Sonnet.
What is the context window for Claude Opus?
Claude Opus 4.5 has a 200K token context window, the same as Sonnet and Haiku. The context window size alone is not a reason to choose Opus. The reason to choose Opus is reasoning quality and synthesis depth, not raw capacity.
Take It Further
Claude API Cost Optimization Toolkit — The complete model routing framework: when to use Opus vs Sonnet vs Haiku, the decision tree for 15 common task types, cost-per-task calculators, and the orchestrator pattern that cuts costs by 60–80% without sacrificing quality on complex workflows.
→ Get the Cost Optimization Toolkit — $59
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