Capital & Compute
· ai· pricing· models

Claude Opus 5 vs Sonnet 5: Which Model Should You Use?

Claude Opus 5 is stronger, but Sonnet 5 costs 40% less at standard rates. Compare coding, reasoning, speed, context, and when upgrading to Opus pays off.

By Capital & Compute

Claude Sonnet 5 is the better default for most coding, writing, research, and automation. Claude Opus 5 is the stronger model, but it earns its higher price mainly on ambiguous, long-horizon, or high-consequence work where better judgment can prevent a failed attempt. Start with Sonnet, then route the task to Opus when the cost of being wrong is greater than the model premium.

That is the short answer to Claude Opus vs Sonnet in August 2026. The two models share a 1-million-token context window, 128,000-token maximum output, adaptive thinking, and the same default high effort setting. The useful differences are capability, speed, tool availability, and what each finished task actually costs.

Claude Opus 5
stronger reasoning and agency
VS
Claude Sonnet 5
faster, cheaper daily driver
$5 / $25
Input / output
$3 / $15 standard
none
Intro price
$2 / $10 through Aug 31
63
AA Intelligence Index
55
55.4 tok/s
Measured output speed
72.0 tok/s
1M tokens
Context window
1M tokens
128k tokens
Maximum output
128k tokens

Claude Opus 5 vs Sonnet 5 at a glance

The cleanest distinction comes from Anthropic’s model-selection documentation. Anthropic positions Opus 5 for complex agentic coding, multi-hour autonomous work, large refactors, systems engineering, and advanced research. It positions Sonnet 5 as frontier intelligence at scale for code generation, data analysis, content, visual understanding, and tool use.

Difference Claude Opus 5 Claude Sonnet 5
Best role escalation model for the hardest work default model for everyday production
Standard input / output $5 / $25 per million tokens $3 / $15 per million tokens
Current input / output $5 / $25 $2 / $10 through August 31, 2026
Independent AA Index 63 at max effort 55 at max effort
Measured output speed 55.4 tokens per second 72.0 tokens per second
Context / max output 1M / 128k tokens 1M / 128k tokens
Reliable knowledge May 2026 January 2026
Web fetch on API Not available Available

The benchmark and speed figures are from the independent Artificial Analysis model pages for Opus 5 and Sonnet 5, read on the same day. The specifications, knowledge dates, and relative latency are from Anthropic’s models overview. A later knowledge cutoff is useful for some research, but it is not a general-purpose quality score.

How much more expensive is Opus than Sonnet?

Anthropic’s current API pricing lists Opus 5 at $5 per million input tokens and $25 per million output tokens. Sonnet 5 normally costs $3 and $15, so Opus is 1.67 times the price for an identical mix of input and output tokens. Put another way, Sonnet’s standard rates are 40% lower.

The gap is temporarily wider. Through August 31, 2026, Sonnet 5 has introductory rates of $2 input and $10 output. During that window, Opus is 2.5 times the token price and Sonnet is 60% cheaper. On September 1, Sonnet reverts to $3 / $15 automatically.

Prompt caching and batch processing preserve the same basic relationship. Cache hits cost $0.50 per million tokens on Opus and $0.30 on Sonnet at standard pricing, while the Batch API halves both models’ standard rates. Neither feature by itself changes which model is cheaper.

Is Opus 5 actually better than Sonnet 5?

Yes on broad measured capability, not on every dimension. At maximum effort, Artificial Analysis scores Opus 5 at 63 on its Intelligence Index and Sonnet 5 at 55. The index combines nine evaluations spanning agentic work, terminal coding, science, knowledge, and long-context reasoning. That eight-point gap is large enough to treat Opus as the stronger general model, while remembering that an aggregate cannot predict every private workload.

Sonnet wins on measured generation speed. Artificial Analysis recorded 72.0 output tokens per second for Sonnet against 55.4 for Opus, making Sonnet about 30% faster once output begins. For interactive coding, chat, and customer-facing workflows, that latency difference is something a user can feel.

The strangest result is cost per completed evaluation. Artificial Analysis reports that its full index run generated 100 million output tokens on Opus but 300 million on Sonnet. Despite Sonnet’s lower promotional token rate, the site records a total evaluation cost of $4,010.51 for Sonnet against $3,836.05 for Opus. That does not prove Opus is cheaper in production. It proves only that model behavior and thinking depth can overwhelm the rate card on a particular workload.

Anthropic’s own launch evidence points in the same general direction but needs a different label. The Opus 5 launch announcement describes state-of-the-art results on its coding, computer-use, automation, and knowledge-work evaluations. Those are vendor-reported launch results, not independent reproductions, so they support Anthropic’s positioning but do not replace a test on your work. The broader reason for separating those evidence grades is covered in the guide to whether AI benchmarks are reliable.

Which Claude model should you use for each task?

Claude Opus 5 versus Sonnet 5 task-routing matrixSix-row decision matrix. Sonnet 5 is preferred for routine coding, high-volume work, latency-sensitive interaction, and built-in web fetch. Opus 5 is preferred for architecture, difficult debugging, and long-horizon autonomous agents.AdvantageTrade-offDrawbackDimensionClaude Opus 5Claude Sonnet 5Routine coding and testsCapable, pricierBest defaultArchitecture and hard debuggingBest fitEscalate if stuckLong-horizon autonomous agentsBest fitCapableHigh-volume content and analysisCostly defaultBest fitLatency-sensitive interactionModerateFasterBuilt-in web fetch on APIUnavailableAvailable
Claude Opus 5 versus Sonnet 5 task-routing matrix
DimensionClaude Opus 5Claude Sonnet 5
Routine coding and testsCapable, pricierBest default
Architecture and hard debuggingBest fitEscalate if stuck
Long-horizon autonomous agentsBest fitCapable
High-volume content and analysisCostly defaultBest fit
Latency-sensitive interactionModerateFaster
Built-in web fetch on APIUnavailableAvailable
A practical routing matrix for Claude Opus 5 and Sonnet 5. Sonnet is the default for bounded, high-volume, and latency-sensitive work. Opus is the escalation path for ambiguous, long-horizon, or high-consequence tasks.Source: Capital & Compute synthesis from Anthropic model-selection and migration guidance

Choose Sonnet 5 for the default lane

Sonnet is the practical starting point when a task is clearly scoped and failures are cheap to detect. That includes implementing a known design, writing tests, routine refactors, first-pass research, summarization, data extraction, content production, and tool-driven automation with a short feedback loop. It is also the better fit when response speed or request volume matters.

For Claude Code, this means Sonnet should handle most implementation work. The Claude Code cost-per-task model puts a representative multi-file session near $1.46 on Sonnet at standard rates, assuming heavy prompt-cache reuse. The AI coding cost calculator lets you replace those assumptions with your own context, output, and cache mix.

Choose Opus 5 for the escalation lane

Opus earns the premium when the work is hard to specify, hard to verify, or expensive to redo. Examples include diagnosing an intermittent failure across several systems, choosing architecture under conflicting constraints, reviewing a risky migration, completing a long autonomous agent run, or synthesizing evidence where a plausible but wrong answer would survive a quick review.

The point is not that Opus should touch every important task. It is that judgment has economic value when it prevents a bad branch of work. If an Opus attempt costing 1.67 times as much avoids two Sonnet attempts, the expensive model was the cheaper choice.

What is the difference in the API and Claude Code?

The migration is mostly a model-name change. Anthropic’s Opus 5 migration guide says both models use the same Messages API shape, default to adaptive thinking at high effort, support 1 million context tokens and 128,000 output tokens, and omit Priority Tier. Both reject manual extended-thinking budgets, assistant prefills, and non-default sampling parameters.

There are two practical exceptions. Sonnet 5 supports Anthropic’s server-side web fetch tool while Opus 5 does not. Opus 5 accepts mid-conversation system messages in placements that Sonnet does not. If either behavior sits in your production path, the models are not drop-in substitutes even though their basic request and response shapes match.

Inside Claude Code, both default to high effort. Start Sonnet there for normal implementation. Move a task to Opus when the agent is repeatedly treating symptoms instead of causes, losing a long plan, missing cross-file constraints, or producing work that takes too much human review. For the most demanding Opus work, Anthropic recommends testing xhigh effort. Higher effort spends more thinking tokens, so evaluate quality and cost together.

A routing policy that avoids overspending

The useful policy is simple enough to automate:

  1. Start every known, reversible task on Sonnet 5. Define the acceptance test before the run.
  2. Escalate on evidence, not prestige. Move to Opus after a failed evaluation, repeated tool loops, an unresolved root cause, or a task classified as high consequence.
  3. Move the task class, not all traffic. If Opus consistently wins on database migrations, route migrations to it. Keep boilerplate and routine tests on Sonnet.
  4. Record cost per accepted result. Token cost, retries, latency, and human review time belong in the same comparison.

That last step is the one most model comparisons omit. A rate card can tell you the minimum cost of a request. It cannot tell you how many requests, tool calls, or review cycles it takes to reach an acceptable result. The site’s side-by-side model comparison tool handles the rate-card layer; your own evaluation set has to supply the success rate.

Frequently asked questions

Is Claude Opus 5 better than Sonnet 5?
Opus 5 is the stronger general model. At maximum effort it scores 63 on the independent Artificial Analysis Intelligence Index against Sonnet 5 at 55. Sonnet is faster and cheaper, so stronger does not automatically mean better for routine or high-volume work.
How much more expensive is Claude Opus 5 than Sonnet 5?
At standard API rates, Opus 5 costs $5 per million input tokens and $25 per million output tokens versus Sonnet 5 at $3 and $15, making Opus about 1.67 times the token price. Through August 31, 2026, Sonnet costs $2 and $10, making Opus 2.5 times the promotional rate.
Should I use Opus or Sonnet for Claude Code?
Start with Sonnet 5 for routine implementation, tests, and bounded refactors. Switch to Opus 5 for architecture, difficult root-cause debugging, long autonomous runs, or tasks that repeatedly fail evaluation on Sonnet.
Do Claude Opus 5 and Sonnet 5 have the same context window?
Yes. Both have a 1-million-token context window and a 128,000-token maximum output on the synchronous Messages API. They also share adaptive thinking and a default high effort setting.
Is Sonnet 5 faster than Opus 5?
Yes in the current independent measurement. Artificial Analysis recorded 72.0 output tokens per second for Sonnet 5 and 55.4 for Opus 5, making Sonnet about 30% faster once generation begins.

Bottom line

The best Claude Opus vs Sonnet policy is not to pick one model forever. Make Sonnet 5 the default and Opus 5 the escalation path. Sonnet buys lower latency and 40% lower standard token rates without giving up the shared 1M context or core agent features. Opus buys a meaningful capability advantage when the task demands deeper reasoning, longer autonomy, or more reliable self-checking.

If the work is clear and easy to test, choose Sonnet. If the work is ambiguous and failure is expensive, choose Opus. Then verify the decision with cost per accepted task rather than loyalty to a model tier.

Sources

Get each breakdown before it makes the rounds

You get one email when a new source-backed analysis goes live: what AI agents actually cost, which models are worth running, and what the benchmarks really mean. No hype.

No spam. Unsubscribe anytime.

← Back to Models & benchmarks