One-line definition

Opus 4.6 is Claude's highest-capability model built for deep, multi-step reasoning. Sonnet 4.6 is Claude's practical workhorse — balanced across speed, cost, and quality for everyday use.

Why do two models exist?

It might seem logical to always use the most powerful model available. In practice, that approach creates two problems.

For fast, repetitive tasks — drafting emails, summarizing documents, reviewing code — running the heaviest model means paying more and waiting longer for results that a lighter model could match. On the other hand, for tasks requiring sustained logical chains — legal analysis, research synthesis, complex architecture design — a mid-tier model can produce noticeably weaker output.

Anthropic ships both models for one reason: to let you match the model to the complexity of the task, not the other way around.

How the two models differ under the hood

Opus 4.6 and Sonnet 4.6 share the same Claude 4 lineage but differ in scale and optimization target.

Model size: Opus 4.6 has more parameters, giving it greater capacity to capture complex patterns and maintain coherence across long reasoning chains.
Reasoning depth: Opus 4.6 holds up better on tasks requiring chained logic — multi-step deductions, cross-referencing long documents, or catching subtle contradictions.
Response speed: Sonnet 4.6 generates responses faster, making it the better fit for latency-sensitive environments like conversational interfaces.
Cost structure: Sonnet 4.6 carries significantly lower per-token pricing on both input and output, making it the default choice for high-volume pipelines.

The key insight: this is not a ranking — it is a specialization. Each model is optimized for a different type of work.

The three misconceptions people bring to these models

Misconception 1: Opus 4.6 always produces better output

Reality: For well-structured, repetitive tasks — templated writing, basic summaries, code formatting — the quality gap between Sonnet 4.6 and Opus 4.6 is minimal. Sonnet 4.6 handles these just as well while being faster and cheaper. "More expensive = better results" only holds when the task genuinely demands deep reasoning.

Misconception 2: Sonnet 4.6 is a cut-down version of Opus 4.6

Reality: Sonnet 4.6 is not a trimmed Opus 4.6 — it is a separately optimized model targeting speed and cost efficiency. For real-time applications, conversational products, and batch processing pipelines, Sonnet 4.6 is often the more appropriate choice, not a compromise. The models serve different primary use cases.

Misconception 3: Individual users can always get by with Sonnet 4.6

Reality: Task complexity, not user type, is the right criterion. Individuals working on long-form research writing, detailed contract review, or multi-chapter creative projects can clearly feel the difference Opus 4.6 makes. The "enterprise = Opus, personal = Sonnet" framing is a common shortcut that doesn't hold up in practice.

Real-world usage scenarios

Scenario 1: Sonnet 4.6 — high-throughput, speed-sensitive work

Email and document drafts: Quickly generating structured, templated content
Code explanation and basic review: Describing what code does or spotting simple bugs
Batch summarization pipelines: Processing large volumes of articles or reports
Conversational and real-time interfaces: Any product where response latency directly affects UX

Sonnet 4.6 keeps costs manageable and response times low while delivering output quality that is entirely sufficient for most day-to-day tasks.

Scenario 2: Opus 4.6 — deep reasoning, low error tolerance

Legal and contract document analysis: Cross-referencing multiple clauses to identify risks
Long-form research and report writing: Synthesizing many sources while keeping logical flow consistent
Complex codebase refactoring and architecture design: Handling structural changes across multiple files and components
Mathematical reasoning and scientific problem-solving: Step-by-step logic where each step affects the next

Opus 4.6 is the right call when errors are costly, reasoning chains are long, and the quality of output is worth the extra spend.

Scenario 3: Hybrid strategy — combining both models

The most cost-effective real-world approach is to split roles rather than commit to one model for everything.

Step 1: Sonnet 4.6 generates the draft → Step 2: Opus 4.6 reviews and refines
Bulk data processing with Sonnet 4.6; final decision-support output with Opus 4.6
API products: standard user requests on Sonnet 4.6, premium features on Opus 4.6

This pattern cuts total API spend significantly while reserving Opus 4.6 for the steps where it genuinely moves the needle.

Opus 4.6 vs Sonnet 4.6 at a glance

Criterion	Opus 4.6	Sonnet 4.6
Reasoning depth	Highest (complex multi-step chains)	High (general to moderate complexity)
Response speed	Slower	Faster
API cost	Higher	Significantly lower
Best-fit tasks	Deep analysis, expert domains, complex code	General work, real-time chat, batch pipelines
Long-context consistency	Excellent	Good, with potential degradation at extreme lengths
Recommended environment	Single high-stakes tasks requiring peak output	Repeated tasks where speed and cost efficiency matter

Choosing rule: If your task has a narrow margin for error and requires sustained multi-step reasoning, use Opus 4.6. If throughput, speed, and cost efficiency are the priority, use Sonnet 4.6.

Key action summary

Item	Guideline
Default to Sonnet 4.6 when	Drafting, summarizing, chatbots, real-time responses, cost-sensitive pipelines
Switch to Opus 4.6 when	Deep analysis, expert domains, long-context consistency is critical
Hybrid approach	Sonnet 4.6 for drafts, Opus 4.6 for final review and refinement
Cost control	Maximize Sonnet 4.6 usage; limit Opus 4.6 to tasks that clearly need it
Upgrade signal	Repeated output quality issues or reasoning errors are the cue to move to Opus 4.6

Frequently asked questions

Q1. Can I choose the model in Claude.ai?

On the Claude.ai Pro plan, you can switch between Opus 4.6 and Sonnet 4.6 within a conversation. The free plan runs on Sonnet 4.6 by default. When using the API, you specify the model explicitly in the model parameter of each request.

Q2. Can better prompting close the gap between Sonnet 4.6 and Opus 4.6?

For many tasks, yes. Clear instructions, step-by-step reasoning prompts, and well-structured context can significantly raise Sonnet 4.6's output quality. That said, the reasoning capacity difference that comes from model scale cannot be fully bridged through prompting alone. Good prompting raises the ceiling for Sonnet 4.6 — but at some complexity threshold, Opus 4.6 is the right move.

Q3. Where should I start if I'm unsure which model to use?

Start with Sonnet 4.6. Run your actual tasks, note where the output falls short, and document those cases. Then apply Opus 4.6 specifically to those task types and compare quality against the added cost. Building selection criteria from real data is far more reliable than guessing upfront.

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