TL;DR. Opus 4.7 costs 67% more than Sonnet 4.6 on input and 67% more on output. That premium pays for itself on tasks that require deep multi-step reasoning, high-stakes code review, or long-horizon planning — but burns money on the 80% of workloads where Sonnet is already at ceiling. The decision rule is not "which model is smarter?" — it is "does this task hit Sonnet's ceiling?" If yes, Opus ROI is positive. If no, you are paying 67% for noise.
Pricing side-by-side
| Dimension | Sonnet 4.6 | Opus 4.7 | Delta |
|---|---|---|---|
| Input (per 1M tokens) | $3.00 | $5.00 | +67% |
| Output (per 1M tokens) | $15.00 | $25.00 | +67% |
| Cache write (per 1M) | $3.75 | $6.25 | +67% |
| Cache read (per 1M) | $0.30 | $0.50 | +67% |
| Context window | 200K | 200K | Same |
| Tool use | Yes | Yes | Same |
| Vision | Yes | Yes | Same |
The premium is uniform across all token types and cache tiers. There is no discount for switching to Opus only at the output layer — you pay the full premium on both sides.
At $1,000/month on Sonnet, the equivalent Opus workload costs $1,670/month — $670 more. That $670 delta is your ROI test: what does Opus win you that is worth $670/month?
Where Opus 4.7 pulls ahead of Sonnet 4.6
The performance gap is not uniform. Sonnet 4.6 reaches near-parity on deterministic tasks; Opus 4.7 separates on tasks requiring genuine reasoning chains.
| Task category | Sonnet 4.6 | Opus 4.7 | Gap |
|---|---|---|---|
| Simple classification / extraction | ~96% | ~97% | Negligible |
| Structured output (JSON, SQL) | ~93% | ~94% | Negligible |
| Short code completion | ~91% | ~93% | Small |
| SWE-Bench Verified (agentic coding) | ~62% | ~72% | +10 pp |
| Multi-step planning (>5 decision nodes) | ~74% | ~85% | +11 pp |
| Long-document synthesis (100K+) | ~85% | ~93% | +8 pp |
| Adversarial reasoning / edge cases | ~78% | ~89% | +11 pp |
The pattern: on tasks where the answer is deterministic and short, Sonnet 4.6 matches Opus within 1–2 points. On tasks that require holding multiple constraints in mind across a long reasoning chain — agentic coding, complex planning, adversarial edge cases — Opus 4.7 is meaningfully better.
The 5 workloads where Opus ROI is positive
1. Production code reviews. A missed security flaw costs more than the Opus premium. Opus catches more edge cases in complex diffs, especially across multi-file changes with subtle type or state interactions. If your code review catches one critical bug per 10 reviews, the ROI is clear.
2. Long-horizon agent planning. Agents that plan across 10+ steps need to maintain coherent state throughout. Sonnet 4.6 loses the thread more often on deeply nested decision trees. For orchestration agents (not execution subagents), Opus at the planner layer with Haiku at the executor layer is the efficient split.
3. Legal/contract document analysis. Long documents with subtle clause interactions require tracking a large constraint space. Opus 4.7's recall improvement at 100K+ tokens is measurable. For a 200-page contract review, missing a clause is far more expensive than the Opus premium.
4. Complex multi-domain synthesis. Research tasks that require connecting non-obvious dots across multiple knowledge domains (clinical trial design, regulatory compliance mapping, financial modeling with many variables) benefit from Opus's stronger reasoning.
5. High-stakes one-shot generation. When you generate something once and it ships (a fundraising email, a public report, a legal brief), Opus's tendency to produce more polished output in fewer iterations is worth the premium. Sonnet may need one more revision cycle.
The 4 cases where Sonnet 4.6 wins
1. Batch pipelines at scale. Classification, extraction, summarization at millions of records per month. The Opus premium compounds brutally at scale. If Sonnet accuracy is 96% and Opus is 97%, the 1-point accuracy gain rarely justifies a 67% cost increase on a pipeline processing 10M records.
2. High-volume customer support / chat. Most customer queries are FAQ-level. Sonnet handles them well. Run Opus only on escalation paths — the 5–10% of queries that require deep reasoning.
3. Structured output endpoints. JSON extraction, SQL generation, data transformation — tasks where the schema constrains the output. Sonnet 4.6 performance on these tasks is close enough to Opus that the gap does not clear the cost bar.
4. Any task you have not benchmarked. "Opus is smarter" is not a strategy. Before routing to Opus, run 100 prompts through both models on your actual data. Measure output quality by your actual quality criteria. If the delta does not clear $0.67 per input-million tokens of upgrade cost, stay on Sonnet.
The 80/15/5 routing model
The cost-optimal split for most production systems is:
Haiku 4.5 ($1/$5) → 80% of requests (high-volume, deterministic)
Sonnet 4.6 ($3/$15) → 15% of requests (moderate complexity, code, analysis)
Opus 4.7 ($5/$25) → 5% of requests (planning, high-stakes, adversarial)
Implemented as a routing layer:
def route_model(task_type: str, complexity_score: float) -> str:
if task_type in ("classify", "extract", "translate") or complexity_score < 0.3:
return "claude-haiku-4-5-20251001"
elif task_type in ("code", "analyze", "summarize") or complexity_score < 0.7:
return "claude-sonnet-4-6-20251120"
else:
# planning, high-stakes review, complex reasoning
return "claude-opus-4-7-20260301"
At a $1,000/month baseline, this routing reduces to approximately $350–$450/month versus routing everything to Sonnet, and $200–$280/month versus routing everything to Opus.
Mid-article CTA. The routing math above is one chapter of the Cost Optimization Masterclass — the full playbook covers model tiering, prompt caching, Batch API routing, and the spreadsheet framework to forecast and cap monthly Claude spend. $59 one-time.
Migration checklist — adding Opus to an existing Sonnet stack
If you are currently routing everything to Sonnet and want to add Opus for high-complexity tasks:
-
Identify your complexity signals. What attributes of an incoming request correlate with Sonnet failures? Typical signals: task type, input length, presence of adversarial phrases, downstream criticality flag.
-
Canary the Opus route at 5%. Route by deterministic hash (request type + user ID). Do not random-sample — you need stable cohorts.
-
Measure quality delta, not just cost. Build a quality eval that captures what Opus should improve. If your eval does not detect the quality delta, your routing signal is probably wrong.
-
Watch output token drift. Opus 4.7 sometimes produces longer outputs than Sonnet 4.6 on the same prompt. Longer outputs mean higher cost. Cap
max_tokensexplicitly on all Opus endpoints. -
Implement a cost ceiling. Add per-request cost estimation and reject routing to Opus if the request would exceed a threshold (e.g., $0.50/request). Prevents runaway costs on adversarial inputs.
See claude-api-cost-monitoring-guide for the dashboard setup that makes this measurable.
Illustrative cost scenario
A research assistant agent running 25K tool-using requests per month, routed with the 80/15/5 rule versus all-Sonnet:
| Routing | Input cost | Output cost | Total/month |
|---|---|---|---|
| All Sonnet 4.6 | $75 | $375 | $450 |
| All Opus 4.7 | $125 | $625 | $750 |
| 80/15/5 (Haiku/Sonnet/Opus) | ~$30 | ~$150 | ~$180 |
The 80/15/5 routing delivers comparable quality to all-Sonnet at 60% less cost, and better quality than all-Sonnet on the 5% of complex requests where Opus matters.
Frequently Asked Questions
What is the latest Claude Sonnet model?
As of May 2026, the latest production Sonnet is Claude Sonnet 4.6 (claude-sonnet-4-6-20251120). The next step up in the Claude family is Opus 4.7 — there is no intermediate Sonnet release between 4.6 and Opus. Check Anthropic's model documentation for the current model list.
Can I switch between Sonnet and Opus mid-conversation?
Yes. The model parameter is per API call. You can use Sonnet for routine turns and Opus for specific steps in an agent loop. The conversation history format is identical across models.
Does prompt caching work the same way across both models?
Yes, but the cache is model-specific. A cached prefix for Sonnet 4.6 does not transfer to Opus 4.7. If you switch models within a session, the prefix must be rewarmed on the new model at cache-write rates. Budget for this if you route the same long system prompt across both tiers.
When would I use Haiku 4.5 instead of Sonnet 4.6?
Haiku 4.5 ($1/$5) is appropriate for tasks where the answer is short, deterministic, and does not require multi-step reasoning: classification, short extraction, translation, formatting. If you have a high-volume pipeline (millions of requests/month) on simple tasks, Haiku cuts cost by 67% vs Sonnet with negligible quality tradeoff.
What is Opus 4.1, and how does it compare?
Claude Opus 4.1 is a legacy model. Its legacy pricing was significantly higher than current Opus 4.7 rates ($5/$25 per 1M tokens). If you are still running Opus 4.1, migrating to Opus 4.7 saves 67% at the API line. There is no reason to stay on 4.1.
Related guides:
- Claude Haiku use cases guide — when Haiku is the right call
- Claude API cost monitoring guide — dashboards and alerts for model-tier routing
- Prompt caching cost benchmark — stacking caching with model tiering
→ Cost Optimization Masterclass — $59
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