Claude Opus — Long-Context Reasoning Model

What claude opus is built for

Claude Opus sits at the top of the Anthropic model family. It was designed for tasks where reasoning depth and sustained coherence across very long inputs matter more than raw throughput. Think: multi-file code reviews where the model must hold the relationship between files in memory simultaneously, or an analysis of a lengthy legal document where every paragraph affects the conclusion. Those are the workloads that push smaller models off the rails and where Opus earns its price premium.

Opus also handles extended thinking paths better than its siblings. When you give it a task that requires working through a chain of logical steps — debugging a subtle concurrency issue, or designing an architecture that must satisfy several constraints at once — it tends to stay on track further into the reasoning chain before losing coherence. The difference is not visible on short tasks, which is why benchmarks that run short prompts often show Sonnet catching up. The gap opens on long, deeply nested problems.

When to choose Opus over Sonnet

The decision rule most engineering teams settle on: start on Sonnet, switch to Opus when the task clearly needs extended reasoning or a context window above what Sonnet offers. That profile captures a minority of everyday coding tasks — probably 10–20% for a typical squad — but those tasks are often the ones where model quality visibly changes the outcome.

Concrete triggers for reaching for Opus: reviewing an entire monorepo for a breaking API change; generating a migration plan for a large schema; summarising a long technical specification with specific action items; writing a system-design document that ties together several subsystems. For these tasks, the extra per-token cost is usually lower than the time cost of reviewing a Sonnet output that missed a cross-file dependency.

Where Sonnet wins: interactive pair programming, quick explanation of a function, generating a unit test for a well-defined method, writing a short utility. Sonnet runs faster and the output quality on narrow tasks is close enough that the difference rarely justifies the Opus price. The models overview page has a side-by-side table if you want to review the numbers.

Opus vs Sonnet — attribute comparison

The table below compares the two models on the attributes that matter most for day-to-day engineering decisions. Note that pricing figures shift over time; treat these as indicative ratios rather than fixed numbers, and verify current rates before budgeting a production workload.

Attribute	Opus value	Sonnet value
Context window	200k tokens	200k tokens
Reasoning depth	Highest in family	Strong, daily-use default
Typical latency	Higher (slower TTFT)	Lower (faster TTFT)
Relative input cost	~5× Sonnet	Baseline
Best fit	Deep analysis, long docs	Interactive coding, reviews
Extended thinking	Available	Available (lighter)
Caching support	Yes	Yes

Context window and token accounting

Both Opus and Sonnet carry a 200k-token context window in current releases, so raw capacity alone is no longer the deciding factor. What differs is how well each model uses that context. Opus maintains coherence and retrieves details from early in the context more reliably when the prompt is dense. For sparse prompts — where most of the context is padding or redundant background — Sonnet performs nearly identically at lower cost.

Practical advice: measure your typical prompt size before defaulting to Opus on the basis of context alone. If most of your prompts land under 50k tokens, Sonnet will serve you well and leave budget for the outlier sessions that genuinely need the larger model's headroom. The api-pricing page covers how prompt caching can reduce the effective per-token rate for repeated large contexts, which changes the cost calculation significantly for workloads that re-use a common preamble.

Using Opus via the Claude API

Model selection in the API is a single string in the request body. You pass the claude-opus model identifier to the model field and the rest of the request structure stays the same. Claude Code exposes this through a --model flag, so you can override the default from the command line without changing configuration files. Teams that want to lock specific workflows to Opus can set the model in a project-level config, which the CLI reads before any session-level override.

Rate limits apply per model tier, not per account globally, so bumping to Opus on a single task does not burn the rate budget for your Sonnet jobs. The Claude API reference page has the endpoint table and current rate-limit tiers. For research-grade context on large language model behaviour under extended contexts, the MIT CSAIL publications archive is a useful academic anchor.