[{"data":1,"prerenderedAt":28},["ShallowReactive",2],{"nr-en-claude-code-review-effort-levels":3},{"slug":4,"title":5,"dek":6,"date":7,"time":8,"publishedAt":9,"updated":10,"updatedAt":10,"dateFmt":11,"updatedFmt":10,"kind":12,"tier":13,"author":14,"authorName":15,"topics":16,"tracker":21,"trackerLabel":22,"headlineStat":10,"image":23,"ogImage":24,"imageAlt":5,"csv":10,"minutes":25,"words":26,"html":27},"claude-code-review-effort-levels","Claude Code: \u002Fcode-review Gets Effort-Level Feature","Anthropic has added effort levels to its code review function. According to the company's official announcement, the lowest setting delivers better findings at a fraction of the token cost.","2026-07-17","08:48","2026-07-17T08:48:00+02:00","","July 17, 2026","news","standard","ideal-syka","Ideal Syka",[17,18,19,20],"Claude","Code Analysis","Developer Tools","Product Update","\u002Fki-preis","Token Efficiency","\u002Fnewsroom\u002Fimg\u002Fclaude-code-review-effort-levels.webp","\u002Fog-nr\u002Fclaude-code-review-effort-levels.en.png",1,198,"\u003Cp>Anthropic has extended Claude Code's code review function with an effort-level control. As the company announced via its official bot, reviews are rewritten at each effort level – this is not a simple filtering mechanism but rather a difference in analysis depth.\u003C\u002Fp>\n\u003Ch2>At a glance\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Low-effort mode\u003C\u002Fstrong> delivers better findings than other code review tools according to Anthropic, while consuming only a \u003Cstrong>fraction of the token cost\u003C\u002Fstrong>\u003C\u002Fli>\n\u003Cli>\u003Cstrong>High-effort mode\u003C\u002Fstrong> delivers significantly higher recall rates for deeper analysis, according to Anthropic\u003C\u002Fli>\n\u003Cli>Users can choose their own tradeoff between cost and analysis depth\u003C\u002Fli>\n\u003Cli>The feature is available via the \u003Ccode>\u002Fcode-review\u003C\u002Fcode> command in Claude Code\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>What this means\u003C\u002Fh2>\n\u003Cp>The effort levels address a classic tension in AI-assisted code analysis: thoroughness versus cost efficiency. For German development teams, the low-effort setting could be interesting if it truly delivers better results than established tools at lower token consumption – that would fundamentally change the cost calculation for AI-driven reviews. What remains unclear is how the two modes differ in practice and which error classes each captures.\u003C\u002Fp>\n\u003Ch2>Sources\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fbsky.app\u002Fprofile\u002Fanthropicbot.bsky.social\u002Fpost\u002F3mqrymdk37624\">Anthropic {bot}\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cem>Editorially owned by \u003Ca href=\"\u002Fen\u002Fautor\u002Fideal-syka\">Ideal Syka\u003C\u002Fa>. Sources and method: \u003Ca href=\"\u002Fen\u002Fredaktion\">Newsroom & method\u003C\u002Fa>. Tips and corrections: \u003Ca href=\"mailto:ai@i6eal.de\">ai@i6eal.de\u003C\u002Fa>.\u003C\u002Fem>\u003C\u002Fp>\n",1784276793216]