prompting-claude-sonnet-5 changedbuild-with-claude/prompt-engineering/prompting-claude-sonnet-5
Nearest release: v2.1.312, published under an hour before this site recorded the change. Shown because the two are within 24 hours of each other. Nothing here says the release caused the edit.
Recorded here
Lines+12added
Lines−12removed
From line
4
where the diff opens
First seen
14 Aug 2026
this site's first read of the page
Recorded edits12to this page, all time
The whole hunk
from line 4, old and new numbered
/
from line 4
44description: Behavioral differences and prompting patterns for Claude Sonnet 5, covering effort, adaptive thinking defaults, and tool use.
55---
66
7This guide covers the prompting patterns specific to Claude Sonnet 5. For the model's specifications, see [Claude Sonnet 5](https://platform.claude.com/docs/en/models/sonnet-5/overview). For techniques that apply across all current Claude models, see [Prompting best practices](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices).
7This guide covers the prompting patterns specific to Claude Sonnet 5. For the model’s specifications, see [Claude Sonnet 5](https://platform.claude.com/docs/en/models/sonnet-5/overview). For techniques that apply across all current Claude models, see [Prompting best practices](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices).
88
99Claude Sonnet 5 has particular strengths in coding and agentic tasks. It performs well out of the box on existing Claude Sonnet 4.6 prompts. The patterns in this guide cover the behaviors that most often require tuning.
1010
from line 22
2222
2323## Calibrating effort and thinking depth
2424
25The [effort parameter](https://platform.claude.com/docs/en/build-with-claude/effort) allows you to tune Claude's intelligence versus token spend, trading off capability for faster speed and lower costs. On Claude Sonnet 5, effort defaults to `high`, the same as on Claude Sonnet 4.6. For the hardest coding and agentic tasks, raise effort to `xhigh`. Experiment with other effort levels to further tune token usage and intelligence:
25The [effort parameter](https://platform.claude.com/docs/en/build-with-claude/effort) allows you to tune Claude’s intelligence versus token spend, trading off capability for faster speed and lower costs. On Claude Sonnet 5, effort defaults to `high`, the same as on Claude Sonnet 4.6. For the hardest coding and agentic tasks, raise effort to `xhigh`. Experiment with other effort levels to further tune token usage and intelligence:
2626
2727* **`max`:** Absolute maximum capability with no constraints on token spending.
2828* **`xhigh`:** Extra high effort is the recommended setting for the hardest coding and agentic use cases.
from line 42
4242
4343On Claude Sonnet 5, [adaptive thinking](https://platform.claude.com/docs/en/build-with-claude/thinking) is on by default. Requests without a `thinking` field run with adaptive thinking. This is a change from Claude Sonnet 4.6, where the same requests ran without thinking. To turn thinking off entirely, pass `thinking: {type: "disabled"}`. Because `max_tokens` is a hard limit on total output (thinking plus response text), revisit it for workloads that ran without thinking on Claude Sonnet 4.6. If you were previously using thinking off with Claude Sonnet 4.6, try thinking on with lower effort levels for Claude Sonnet 5.
4444
45The triggering behavior for adaptive thinking is steerable. If you find the model emitting thinking blocks more often than you'd like, which can occur with large or complex system prompts, add guidance to steer it. As always, measure the effect of any prompting changes on performance. Example:
45The triggering behavior for adaptive thinking is steerable. If you find the model emitting thinking blocks more often than you’d like, which can occur with large or complex system prompts, add guidance to steer it. As always, measure the effect of any prompting changes on performance. Example:
4646
4747```text wrap
4848Thinking adds latency and should only be used when it will meaningfully improve answer quality, typically for problems that require multistep reasoning. When in doubt, respond directly.
4949```
5050
51Conversely, if you're running hard workloads at `medium` and seeing under-thinking, the first lever is to raise effort. If you need finer control, prompt for it directly.
51Conversely, if you’re running hard workloads at `medium` and seeing under-thinking, the first lever is to raise effort. If you need finer control, prompt for it directly.
5252
5353Manual extended thinking (`thinking: {type: "enabled", budget_tokens: N}`) is not supported on Claude Sonnet 5 and returns a 400 error. It was deprecated on Claude Sonnet 4.6 and is now removed. Use adaptive thinking with the effort parameter instead.
5454
from line 62
6262
6363## User-facing progress updates
6464
65Claude Sonnet 5 provides regular, higher-quality updates to the user throughout long agentic traces. If you've added scaffolding to force interim status messages ("After every 3 tool calls, summarize progress"), try removing it. If you find that the length or contents of Claude Sonnet 5's user-facing updates are not well-calibrated to your use case, explicitly describe what these updates should look like in the prompt and provide examples.
65Claude Sonnet 5 provides regular, higher-quality updates to the user throughout long agentic traces. If you’ve added scaffolding to force interim status messages (“After every 3 tool calls, summarize progress”), try removing it. If you find that the length or contents of Claude Sonnet 5’s user-facing updates are not well-calibrated to your use case, explicitly describe what these updates should look like in the prompt and provide examples.
6666
6767## More literal instruction following
6868
69Claude Sonnet 5 interprets prompts literally and explicitly, particularly at lower effort levels. It does not silently generalize an instruction from one item to another, and it does not infer requests you didn't make. The upside of this literalism is precision, and it generally performs better for API use cases with carefully tuned prompts, structured extraction, and pipelines where you want predictable behavior. If you need Claude to apply an instruction broadly, state the scope explicitly (for example, "Apply this formatting to every section, not just the first one").
69Claude Sonnet 5 interprets prompts literally and explicitly, particularly at lower effort levels. It does not silently generalize an instruction from one item to another, and it does not infer requests you didn’t make. The upside of this literalism is precision, and it generally performs better for API use cases with carefully tuned prompts, structured extraction, and pipelines where you want predictable behavior. If you need Claude to apply an instruction broadly, state the scope explicitly (for example, “Apply this formatting to every section, not just the first one”).
7070
7171## Tone and writing style
7272
from line 84
8484
8585Claude Sonnet 5 might settle into a consistent default visual style on open-ended frontend and design briefs. A default house style can read well for some briefs but be a poor fit for dashboards, dev tools, fintech, healthcare, or enterprise apps.
8686
87Generic instructions ("don't use that color," "make it clean and minimal") tend to shift the model to a different fixed palette rather than producing variety. Two approaches work reliably:
87Generic instructions (“don’t use that color,” “make it clean and minimal”) tend to shift the model to a different fixed palette rather than producing variety. Two approaches work reliably:
8888
8989**1. Specify a concrete alternative.** The model follows explicit specs precisely:
9090
from line 117
117117Before building, propose 4 distinct visual directions tailored to this brief (each as: bg hex / accent hex / typeface, plus a one-line rationale). Ask the user to pick one, then implement only that direction.
118118```
119119
120To steer away from generic patterns that users call the "AI slop" aesthetic, you can include a short directive in your system prompt. The [frontend-design skill](https://github.com/anthropics/claude-code/blob/main/plugins/frontend-design/skills/frontend-design/SKILL.md) provides a fuller treatment, but this snippet works well alongside the preceding variety approaches:
120To steer away from generic patterns that users call the “AI slop” aesthetic, you can include a short directive in your system prompt. The [frontend-design skill](https://github.com/anthropics/claude-code/blob/main/plugins/frontend-design/skills/frontend-design/SKILL.md) provides a fuller treatment, but this snippet works well alongside the preceding variety approaches:
121121
122122```text wrap
123123<frontend_aesthetics>
from line 129
129129
130130Token usage and behavior can differ between autonomous, asynchronous coding agents with a single user turn and interactive, synchronous coding agents with multiple user turns. To maximize both performance and token efficiency in coding products, use `xhigh` or `high` effort, add autonomous features such as an auto mode, and reduce the number of human interactions required from your users.
131131
132When limiting the number of required user interactions, it's important to specify the task, intent, and relevant constraints upfront in the first human turn. Providing well-specified, clear, and accurate task descriptions upfront can help maximize autonomy and intelligence while minimizing extra token usage after user turns. In contrast, ambiguous or underspecified prompts conveyed progressively over multiple user turns tend to relatively reduce token efficiency and sometimes performance.
132When limiting the number of required user interactions, it’s important to specify the task, intent, and relevant constraints upfront in the first human turn. Providing well-specified, clear, and accurate task descriptions upfront can help maximize autonomy and intelligence while minimizing extra token usage after user turns. In contrast, ambiguous or underspecified prompts conveyed progressively over multiple user turns tend to relatively reduce token efficiency and sometimes performance.
133133
134134## Code review harnesses
135135
136If your code-review harness was tuned for an earlier model, you might initially see lower recall on Claude Sonnet 5. This is likely a harness effect, not a capability regression. When a review prompt says things such as "only report high-severity issues," "be conservative," or "don't nitpick," Claude Sonnet 5 might follow that instruction more faithfully than earlier models did: it might investigate the code just as thoroughly, identify the bugs, and then not report findings it determines to be below your stated bar. This can show up as the model doing the same depth of investigation but converting fewer investigations into reported findings, especially on lower-severity bugs. Precision typically rises, but measured recall can fall even though the model's underlying bug-finding ability has improved.
136If your code-review harness was tuned for an earlier model, you might initially see lower recall on Claude Sonnet 5. This is likely a harness effect, not a capability regression. When a review prompt says things such as “only report high-severity issues,” “be conservative,” or “don’t nitpick,” Claude Sonnet 5 might follow that instruction more faithfully than earlier models did: it might investigate the code just as thoroughly, identify the bugs, and then not report findings it determines to be below your stated bar. This can show up as the model doing the same depth of investigation but converting fewer investigations into reported findings, especially on lower-severity bugs. Precision typically rises, but measured recall can fall even though the model’s underlying bug-finding ability has improved.
137137
138138Some recommended prompt language:
139139
from line 143
143143
144144This prompt can be used without having an actual second step, but moving confidence filtering out of the finding step often helps. If your harness has a separate verification, deduplication, or ranking stage, tell the model explicitly that its job at the finding stage is coverage rather than filtering.
145145
146If you do want the model to self-filter in a single pass, be concrete about what the reporting threshold is rather than using qualitative terms such as "important": for example, "report any bugs that could cause incorrect behavior, a test failure, or a misleading result; only omit nits like pure style or naming preferences."
146If you do want the model to self-filter in a single pass, be concrete about what the reporting threshold is rather than using qualitative terms such as “important”: for example, “report any bugs that could cause incorrect behavior, a test failure, or a misleading result; only omit nits like pure style or naming preferences.”
147147
148148Iterate on prompts against a subset of your evals or test cases to validate recall or F1 score gains.
149149
from line 151
151151
152152Claude Sonnet 5 supports the earlier `computer_20251124` tool version and, on the Claude API and Claude Models on Google Cloud (Google managed), the `computer_toolset_20260801` toolset. On those two platforms, Claude Sonnet 5 also supports the [browser use tool](https://platform.claude.com/docs/en/agents-and-tools/tool-use/browser-use-tool) (`browser_toolset_20260801`) for tasks inside webpages. [Computer use](https://platform.claude.com/docs/en/agents-and-tools/tool-use/computer-use-tool) capability works across resolutions, up to a maximum resolution of 2576px / 3.75MP. Internal computer use testing shows that sending images at 1080p provides a good balance of performance and cost.
153153
154For particularly cost-sensitive workloads, 720p or 1366×768 are lower-cost options with strong performance. Conduct your own testing to find the ideal settings for your use case; experimenting with effort settings can also help tune the model's behavior.
154For particularly cost-sensitive workloads, 720p or 1366×768 are lower-cost options with strong performance. Conduct your own testing to find the ideal settings for your use case; experimenting with effort settings can also help tune the model’s behavior.
155155
No line in this hunk matches that.