prompting-claude-opus-4-8 changedbuild-with-claude/prompt-engineering/prompting-claude-opus-4-8
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+13added
Lines−13removed
From line
14
where the diff opens
First seen
14 Aug 2026
this site's first read of the page
Recorded edits11to this page, all time
The whole hunk
from line 14, old and new numbered
/
from line 14
1414
1515## Response length and verbosity
1616
17Claude Opus 4.8 calibrates response length to its estimate of the task's complexity, rather than defaulting to a fixed verbosity. This usually means shorter answers on simple lookups and much longer ones on open-ended analysis.
17Claude Opus 4.8 calibrates response length to its estimate of the task’s complexity, rather than defaulting to a fixed verbosity. This usually means shorter answers on simple lookups and much longer ones on open-ended analysis.
1818
1919If your product depends on a certain style or verbosity of output, you might need to tune your prompts. As an example, to decrease verbosity, you might add:
2020
from line 26
2626
2727## Calibrating effort and thinking depth
2828
29The [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. Start with the `xhigh` effort level for coding and agentic use cases, and use a minimum of `high` effort for most intelligence-sensitive use cases. Experiment with other effort levels to further tune token usage and intelligence:
29The [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. Start with the `xhigh` effort level for coding and agentic use cases, and use a minimum of `high` effort for most intelligence-sensitive use cases. Experiment with other effort levels to further tune token usage and intelligence:
3030
3131* **`max`:** Max effort can deliver performance gains in some use cases, but might show diminishing returns from increased token usage. This setting can also sometimes be prone to overthinking. Test max effort for intelligence-demanding tasks.
3232* **`xhigh`:** Extra high effort is the best setting for most coding and agentic use cases.
from line 44
4444
4545Effort is likely to be more important for this model than for any prior Opus, so experiment with it actively when you upgrade.
4646
47On Claude Opus 4.8, thinking is off unless you explicitly set `thinking: {type: "adaptive"}`. The triggering behavior for [adaptive thinking](https://platform.claude.com/docs/en/build-with-claude/thinking) is steerable. If you find the model thinking 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:
47On Claude Opus 4.8, thinking is off unless you explicitly set `thinking: {type: "adaptive"}`. The triggering behavior for [adaptive thinking](https://platform.claude.com/docs/en/build-with-claude/thinking) is steerable. If you find the model thinking 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:
4848
4949```text wrap
5050Thinking 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.
5151```
5252
53Conversely, 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.
53Conversely, 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.
5454
5555<Note>
5656 If you are running Claude Opus 4.8 at `max` or `xhigh` effort, set a large max output token budget so the model has room to think and act across its subagents and tool calls. Start at 64k tokens and tune from there.
from line 62
6262
6363## User-facing progress updates
6464
65Claude Opus 4.8 provides more 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 Opus 4.8'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 Opus 4.8 provides more 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 Opus 4.8’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 Opus 4.8 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 less thrash, 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 Opus 4.8 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 less thrash, 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 92
9292
9393Claude Opus 4.8 has strong design instincts, with a consistent default house style: warm cream/off-white backgrounds (\~`#F4F1EA`), serif display type (Georgia, Fraunces, Playfair), italic word-accents, and a terracotta/amber accent. This reads well for editorial, hospitality, and portfolio briefs, but is a poor fit for dashboards, dev tools, fintech, healthcare, or enterprise apps. The default appears in slide decks and web UIs.
9494
95This default is persistent. Generic instructions ("don't use cream," "make it clean and minimal") tend to shift the model to a different fixed palette rather than producing variety. Two approaches work reliably:
95This default is persistent. Generic instructions (“don’t use cream,” “make it clean and minimal”) tend to shift the model to a different fixed palette rather than producing variety. Two approaches work reliably:
9696
9797**1. Specify a concrete alternative.** The model follows explicit specs precisely:
9898
from line 125
125125Before building, propose 4 distinct visual directions tailored to this brief (each as: bg hex / accent hex / typeface — one-line rationale). Ask the user to pick one, then implement only that direction.
126126```
127127
128Additionally, Claude Opus 4.8 requires less frontend design prompting than previous models to avoid generic patterns that users call the "AI slop" aesthetic. With earlier models, Anthropic recommended a lengthier prompt snippet in the [frontend-design skill](https://github.com/anthropics/claude-code/blob/main/plugins/frontend-design/skills/frontend-design/SKILL.md). However, Claude Opus 4.8 generates distinctive, creative front ends with more minimal prompting guidance. This prompt snippet works well with the preceding prompting advice for variety:
128Additionally, Claude Opus 4.8 requires less frontend design prompting than previous models to avoid generic patterns that users call the “AI slop” aesthetic. With earlier models, Anthropic recommended a lengthier prompt snippet in the [frontend-design skill](https://github.com/anthropics/claude-code/blob/main/plugins/frontend-design/skills/frontend-design/SKILL.md). However, Claude Opus 4.8 generates distinctive, creative front ends with more minimal prompting guidance. This prompt snippet works well with the preceding prompting advice for variety:
129129
130130```text wrap
131131<frontend_aesthetics>
from line 135
135135
136136## Interactive coding products
137137
138Claude Opus 4.8's token usage and behavior can differ between autonomous, asynchronous coding agents with a single user turn and interactive, synchronous coding agents with multiple user turns. Specifically, it tends to use more tokens in interactive settings, primarily because it reasons more after user turns. This can improve long-horizon coherence, instruction following, and coding capabilities in long, interactive coding sessions, but also comes with more token usage. 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.
138Claude Opus 4.8’s token usage and behavior can differ between autonomous, asynchronous coding agents with a single user turn and interactive, synchronous coding agents with multiple user turns. Specifically, it tends to use more tokens in interactive settings, primarily because it reasons more after user turns. This can improve long-horizon coherence, instruction following, and coding capabilities in long, interactive coding sessions, but also comes with more token usage. 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.
139139
140Of course, when 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. Because Claude Opus 4.8 is more autonomous than prior models, this usage pattern helps to maximize performance. In contrast, ambiguous or underspecified prompts conveyed progressively over multiple user turns tend to relatively reduce token efficiency and sometimes performance.
140Of course, when 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. Because Claude Opus 4.8 is more autonomous than prior models, this usage pattern helps to maximize performance. In contrast, ambiguous or underspecified prompts conveyed progressively over multiple user turns tend to relatively reduce token efficiency and sometimes performance.
141141
142142## Code review harnesses
143143
144Claude Opus 4.8 is meaningfully better at finding bugs than prior models, and has both higher recall and precision in internal evals. However, if your code-review harness was tuned for an earlier model, you might initially see lower recall. 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 Opus 4.8 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.
144Claude Opus 4.8 is meaningfully better at finding bugs than prior models, and has both higher recall and precision in internal evals. However, if your code-review harness was tuned for an earlier model, you might initially see lower recall. 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 Opus 4.8 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.
145145
146146Some recommended prompt language:
147147
from line 151
151151
152152This 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.
153153
154If 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."
154If 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.”
155155
156156Iterate on prompts against a subset of your evals or test cases to validate recall or F1 score gains.
157157
from line 159
159159
160160Claude Opus 4.8 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 Opus 4.8 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.
161161
162For 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.
162For 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.
163163
No line in this hunk matches that.