Claude Opus 5 vs Sonnet 5: Which Model to Choose for Your Task

In short:

• Sonnet 5 and Opus 5 are not competitors, but two steps in the same lineup: Sonnet 5 was released on June 30, 2026, as the "most agentic Sonnet" at a mid-tier price, while Opus 5 was released on July 24, 2026, as the top-tier model for daily use.
• The main difference in numbers: $2/$10 (introductory price until August 31, then $3/$15) for Sonnet 5 versus a fixed $5/$25 for Opus 5 – but the actual savings on text-heavy tasks are less than nominal due to Sonnet 5's updated tokenizer.
• On SWE-bench Pro, the gap is significant (79.2% for Opus 5 vs. 63.2% for Sonnet 5), while on Terminal-Bench 2.1, Opus 5 leads by only ~9 p.p. (89.1% vs. 80.4%).
• Both models support Zero Data Retention – unlike Fable 5, where 30-day retention is mandatory.
• The practical strategy for production is not "which to choose forever," but routing: Sonnet 5 by default, escalating to Opus 5 for complex or expensive-in-case-of-error scenarios.

Contents

• In short: what is the main difference between Opus 5 and Sonnet 5
• Pricing: $5/$25 vs. $2/$10
• Benchmarks: where Opus 5 wins by a significant margin, and where the gap is minimal
• Effort dial in both models: how the practical effect differs
• Agentic tasks: short cycles vs. long autonomous sessions
• Working with large context and codebases
• Speed and self-correction
• The hidden cost of tokenization
• Knowledge cutoff: a four-month difference
• Enterprise scenarios
• Practical strategy: routing
• Who is Sonnet 5 for, and who is Opus 5 for
• Summary table
• Conclusion

In short: what is the main difference between Opus 5 and Sonnet 5

Claude Sonnet 5 was released on June 30, 2026, and immediately became the default model on Free and Pro plans – Anthropic directly called it the "most agentic Sonnet" since the inception of the line (DataCamp). Claude Opus 5 appeared three and a half weeks later, on July 24, 2026, and became the default model on Max and the most powerful option on Pro.

In short: Sonnet 5 is the model you run by default for most daily tasks because it is fast, cheap, and sufficiently agentic. Opus 5 is the model you escalate to when the task is ambiguous, expensive in case of error, or requires real reasoning rather than just executing a clear plan.

Pricing: $5/$25 vs. $2/$10

The nominal price difference looks impressive: Sonnet 5 costs $2 per million input and $10 per million output tokens under the intro rate, which is valid until August 31, 2026, after which it will rise to the standard $3/$15 (DataCamp). Opus 5 costs a fixed $5/$25 – this is the same price as Opus 4.8, with no intro period.

Under the standard tariff (from September 1), Opus 5 costs approximately 1.67 times more than Sonnet 5 – for both input and output tokens. However, this figure describes the price per token, not the price per completed task – and that's a completely different conversation, which we will return to in the section on the hidden cost of tokenization below.

Benchmarks: where Opus 5 wins by a significant margin, and where the gap is minimal

Anthropic has not published an official direct comparison of Opus 5 and Sonnet 5 – each release post compares its model to the previous one and to Opus 4.8 separately. We are compiling the available figures into one table where the comparison is correct (the same benchmark version for both models):

Important: I have intentionally excluded OSWorld from this table – Sonnet 5 was tested on OSWorld-Verified (81.2%), and Opus 5 on the newer OSWorld 2.0 version (70.6%). These are different benchmark versions with different methodologies, and a direct comparison of figures between them would be misleading – despite the fact that nominally Sonnet 5's figure is even higher.

The pattern that emerges is that the gap is largest precisely on tasks of real refactoring and long chains of working with third-party code (SWE-bench Pro, SWE-bench Verified) – that is, where the depth of reasoning is important. On tasks of executing clearly defined CLI sequences (Terminal-Bench), the gap is noticeably smaller – Sonnet 5 is indeed "catching up" to the Opus level here, as Anthropic promised at release.

Effort dial in both models: how the practical effect differs

Both models have an adjustable "effort" level, but not the same range. Sonnet 5 offers four levels – low, medium, high, xhigh (MarkTechPost). Opus 5 has five – with max added. At first glance, this looks like just "one more level," but the practical difference is more important: at xhigh, Sonnet 5 sometimes costs more than Opus 4.8 for comparable quality – meaning the upper limit of effort in Sonnet 5 is the point where the economic sense of switching to Sonnet instead of Opus is already lost.

The practical conclusion from this for routing logic: if your task regularly requires xhigh on Sonnet 5 to get an acceptable result, it's a signal to switch to Opus 5 with a lower effort level (e.g., medium), rather than continuing to increase the effort in Sonnet 5. This often turns out to be both cheaper and faster.

Agentic tasks: short cycles vs. long autonomous sessions

Sonnet 5 handles short, well-defined agentic cycles well: a five-step workflow with clear instructions will perform stably. But a twenty-step task with ambiguous input data is a scenario where Opus 5 is a safer choice, as it maintains coherence over more steps and recovers better from errors in extended tasks.

There's a direct economic caveat here: a long Opus 5 session can cost 2-3 times more than a similar session on Sonnet 5, as agentic workflows quickly "eat" tokens regardless of the model. For agents working without human supervision, this is a justified premium for reducing the risk of an expensive error. For short, supervised controlled cycles, Sonnet 5 saves budget without a noticeable loss in quality.

Working with large context and codebases

Both models have the same context window – 1 million tokens, enough to load an entire medium-sized codebase into a single prompt. Formal equality here doesn't mean identical quality of work with this volume – it rather means that the window size is no longer a differentiator between models of this Anthropic generation, unlike, for example, Gemini 3.6 Flash with an output limit twice as small as both Claude models.

The practical difference manifests not in the window size, but in what happens when the context is almost completely filled and you also need to actively refactor code within it: here we return to the difference in SWE-bench Pro from the section above – Opus 5 is more stable precisely on tasks where you need to keep many files in mind simultaneously.

Speed and self-checking: why Opus 5 makes fewer mistakes, but it costs time

Self-checking its own work is a signature feature of Opus 5: the model tends to verify its own conclusions, build test harnesses when there's no ready data source for validation, and argue with instructions it deems incorrect, rather than blindly executing them. This is directly related to speed: at maximum effort, the time to the first token in Opus 5 reaches 66-68 seconds – deep thinking and self-checking cost real waiting time.

Sonnet 5, as a smaller model architecturally, is naturally faster at comparable effort levels – this aligns with general practice: a smaller model means less latency for the same level of "effort." But the flip side of the same coin is less built-in self-checking by default. For tasks with a clear success criterion (a test or linter checking the result), this is not critical. For tasks where there is no automatic verification and the result is only evaluated by a human post-factum, Opus 5's habit of self-checking saves review time, even if the generation of the response itself takes longer.

Hidden cost of tokenization: why Sonnet 5 isn't always 1.7x cheaper in practice

This is the least obvious, but one of the most important nuances in this comparison. Sonnet 5 received an updated tokenizer – the same type of update that Anthropic introduced in Opus 4.7. The same text can now convert into more tokens than before – approximately 1.0-1.35 times depending on the content type (DataCamp). Anthropic set the intro price so that the transition from Sonnet 4.6 would be approximately neutral in cost, not just a simple discount – meaning it's a conscious trade-off, not a side effect.

The practical consequence for comparison with Opus 5: on text-heavy tasks that tokenize poorly (dense natural text, code with many comments in multiple languages, structured data), the real price difference between models at standard rates narrows to about 23% instead of the nominal 67%, if the tokenization inflation in Sonnet 5 eats up part of the nominal savings. The inflation range is from 1.0x to 1.35x depending on your content, and this is an empirical question worth checking on your own data, not relying on the price list. If the quality of the solution on this path matters at all, a 23% premium is much easier to justify than a nominal 67%.

Knowledge cutoff: a four-month difference (May 2026 in Opus 5 vs. January 2026 in Sonnet 5)

According to aggregated data from independent model trackers, Opus 5 has a fresher knowledge cutoff – May 2026 – compared to January 2026 in Sonnet 5 (Coursiv). The four-month difference is logical given the release sequence: Sonnet 5 was trained and fixed earlier, Opus 5 was released three and a half weeks later and managed to capture fresher data.

Caveat: exact knowledge cutoff dates are a detail that different independent trackers sometimes record differently depending on methodology, and Anthropic doesn't always publish this directly in the main release post. If the cutoff date accuracy is critical for your product (e.g., legal or medical content where the date of the last regulatory update is important), I would recommend checking the relevance of the model's response with a separate query, rather than relying solely on the stated cutoff date.

Enterprise scenarios: compliance, retention, reliability on long sessions

Here's good news for both models: both Sonnet 5 and Opus 5 support Zero Data Retention (ZDR) – unlike Fable 5, where 30-day data retention is mandatory (Coursiv). This means the choice between Sonnet 5 and Opus 5 does not affect this aspect of compliance – the decision can be made solely on the basis of quality and cost.

Sonnet 5 comes with the same real-time cybersecurity classifiers by default as Opus 4.7/4.8, and according to Anthropic's pre-release testing, it is safer than Sonnet 4.6: it better refuses harmful requests, is more resistant to prompt injection, with a lower level of hallucinations and sycophancy (DataCamp). One organizational nuance to keep in mind: on some Enterprise plans, premium slots might by default remain tied to the older model even after the release of a newer one – before getting confused why the team is seeing different results, it's worth checking which specific model is assigned to your plan by default.

Practical strategy: routing – when to automatically escalate from Sonnet 5 to Opus 5

The most practical approach for production systems is not to choose one model forever, but to build routing: Sonnet 5 handles the main stream of requests, and the part that it itself marks as uncertain or cannot complete is automatically escalated to Opus 5.

• Keep Sonnet 5 as default for well-defined tasks with a clear readiness criterion: a feature with acceptance criteria, a known bug, comparison by template, a report in a defined structure.
• Escalate to Opus 5 when the task is ambiguous, difficult to verify automatically, or costly in case of error – a twenty-step exploratory agent with fuzzy input data is clearly closer to this category than the previous one.
• Use the effort dial on both models as an intermediate lever before full escalation – often, increasing the effort in Sonnet 5 from low to medium is cheaper than switching to Opus 5 immediately.
• Calculate cost per successful task, not per token – this metric, not the nominal price per million tokens, shows which model is actually more effective for a specific workflow.

Who Sonnet 5 is for, who Opus 5 is for

Summary Table

Conclusions

Having analyzed the available benchmarks and pricing for both models, I wouldn't recommend treating this comparison as a question of "which model is better" – they don't compete for the same spot in the stack. My practical recommendation: start with Sonnet 5 as the default for most tasks and enable simple escalation logic to Opus 5 where Sonnet 5 itself signals uncertainty or the task clearly goes beyond a short, well-defined cycle.

Two points I would advise checking yourself, rather than taking on faith from this or any other article: the actual tokenization inflation coefficient on your own content (it heavily depends on language and text structure) and the actual default rate on your Enterprise plan – I've already seen confusion in teams who thought they were working with a new model, but in reality, premium slots were still tied to an older one. These two simple checks save you from the most common mistakes when choosing between Sonnet 5 and Opus 5 in practice.