Anthropic Advisor Strategy 實測：Sonnet 搭 Opus 顧問，省 85% 成本但效果如何？ | Anthropic Advisor Strategy Hands-On: Opus-Guided Sonnet Cuts Costs 85% — But How Good Is It?

By Kit 小克 | AI Tool Observer | 2026-04-11

🇹🇼 Anthropic Advisor Strategy 實測：Sonnet 搭 Opus 顧問，省 85% 成本但效果如何？

Anthropic 在 2026 年 4 月 9 日發布了一項全新的 API 功能：Advisor Strategy。簡單來說，就是讓便宜的模型（Sonnet 或 Haiku）在執行任務時，可以即時諮詢最強模型 Opus 4.6 的意見，一個 API call 就搞定。這不只是架構設計的巧思，更是 AI Agent 成本控制的重大突破。

Advisor Strategy 是什麼？為什麼開發者需要關注？

當你部署 AI Agent 時，最頭痛的問題之一就是成本 vs. 品質的取捨。用 Opus 跑所有任務太貴，用 Sonnet 又怕關鍵決策出錯。Advisor Strategy 的做法是：讓 Sonnet/Haiku 當「執行者」（executor），全程跑任務、呼叫工具、處理結果；當遇到自己搞不定的決策時，才去問 Opus「顧問」（advisor）。Opus 看過共享的上下文後，給出計畫、修正或停止訊號，執行者再繼續。

怎麼使用 Advisor Strategy？

實作上非常簡單，只需要三步：

• 在 Messages API request 加上 beta header：anthropic-beta: advisor-tool-2026-03-01
• 在 tools 陣列中加入 advisor tool，type 設為 advisor_20260301，name 設為 advisor，model 指定 claude-opus-4-6
• Sonnet/Haiku 會自動判斷何時需要諮詢 Opus，開發者不用手動觸發

實際效果好不好？官方數據怎麼說？

根據 Anthropic 公布的評測結果：

• Sonnet + Opus 顧問：SWE-bench Multilingual 提升 2.7 個百分點，每次 agentic task 成本降低 11.9%
• Haiku + Opus 顧問：BrowseComp 從單獨的 19.7% 跳到 41.2%，翻倍以上
• Haiku + Opus 顧問 vs. Sonnet 單獨：分數落後 29%，但成本便宜 85%

換句話說，如果你的場景可以接受略低於 Sonnet 的品質，用 Haiku + Opus 顧問能省下巨額費用。對於大量 Agent 並行的企業場景，這是實打實的成本革命。

適合什麼場景？

Advisor Strategy 特別適合以下情境：

• 大規模 Agent 部署：每天數千次 LLM 呼叫，成本敏感
• 混合複雜度任務：大部分工作簡單，但偶爾需要深度推理
• 瀏覽器操控、程式碼修改：BrowseComp 的提升顯示瀏覽類任務效果尤其明顯

我的觀點：好不好用，試了才知道

Advisor Strategy 的設計理念很聰明——不是讓你二選一，而是讓小模型在需要時借用大模型的腦袋。但實際效果高度依賴任務類型。如果你的 Agent 任務大部分是簡單工具呼叫，Haiku 本來就夠用，加 Opus 顧問可能只是多花錢。反過來說，如果你的 Agent 經常需要複雜推理但又受限預算，這功能幾乎是量身定做的。

目前還在 beta 階段，建議先在開發環境跑幾輪，比較有無 advisor 的品質差異和成本變化，再決定是否上線。

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🇺🇸 Anthropic Advisor Strategy Hands-On: Opus-Guided Sonnet Cuts Costs 85% — But How Good Is It?

On April 9, 2026, Anthropic shipped a new API feature called the Advisor Strategy. The idea is elegant: let a cheaper model (Sonnet or Haiku) run your agent tasks end-to-end, but give it a direct line to Opus 4.6 for guidance when it gets stuck — all within a single API call. For anyone running AI agents at scale, this could be a game-changer for cost management.

What Is the Advisor Strategy and Why Should You Care?

The core problem is familiar: Opus is brilliant but expensive. Sonnet is fast and cheap but sometimes drops the ball on complex reasoning. The Advisor Strategy bridges this gap. Sonnet or Haiku acts as the executor, handling tool calls, reading results, and iterating. When it hits a decision beyond its capability, it consults Opus as the advisor. Opus reviews the shared context and returns a plan, correction, or stop signal. The executor then resumes.

How Do You Implement the Advisor Strategy?

Implementation is straightforward — three steps:

• Add the beta header: anthropic-beta: advisor-tool-2026-03-01
• Include an advisor tool in your tools array with type advisor_20260301, name set to advisor, and model pointing to claude-opus-4-6
• The executor model automatically decides when to consult Opus — no manual triggering required

Does It Actually Work? What Do the Benchmarks Say?

According to Anthropic official evaluations:

• Sonnet + Opus advisor: +2.7 percentage points on SWE-bench Multilingual, 11.9% cost reduction per agentic task
• Haiku + Opus advisor: BrowseComp jumped from 19.7% to 41.2% — more than doubled
• Haiku + Opus advisor vs. Sonnet solo: 29% lower score, but 85% cheaper per task

The takeaway: if your use case tolerates slightly lower quality than Sonnet solo, Haiku with an Opus advisor delivers massive cost savings. For enterprises running thousands of daily agent calls, this is a genuine cost revolution.

What Are the Best Use Cases?

The Advisor Strategy shines in these scenarios:

• Large-scale agent deployments: Thousands of daily LLM calls where cost matters
• Mixed-complexity tasks: Mostly simple work with occasional deep reasoning needs
• Browser automation and code editing: The BrowseComp improvement suggests especially strong gains here

My Take: You Have to Try It to Know

The design philosophy is smart — instead of choosing between expensive-and-smart or cheap-and-limited, you let the small model borrow the big model brain when it needs to. But real-world results depend heavily on your task profile. If your agent mostly makes simple tool calls, Haiku alone is probably fine. If your agent frequently needs complex reasoning on a budget, this feature feels tailor-made.

It is still in beta. I recommend running comparison tests in your dev environment before committing to production.

Sources / 資料來源

• The Advisor Strategy: Give Sonnet an Intelligence Boost with Opus | Anthropic Blog
• Advisor Tool - Claude API Docs
• Anthropic Advisor Strategy: Get Opus Intelligence at Sonnet Prices | AI Tools Recap

常見問題 FAQ

Advisor Strategy 和直接用 Opus 有什麼差別？

Advisor Strategy 讓 Sonnet/Haiku 當主要執行者，只在遇到困難決策時才諮詢 Opus，大幅降低成本。直接用 Opus 則是每個 token 都用最貴的模型處理。

Advisor Strategy 目前支援哪些模型組合？

目前支援 Sonnet 或 Haiku 作為執行者，Opus 4.6 作為顧問。需要加上 beta header anthropic-beta: advisor-tool-2026-03-01 才能使用。

Haiku + Opus 顧問真的能省 85% 成本嗎？

根據 Anthropic 官方數據，Haiku + Opus 顧問相比 Sonnet 單獨使用，成本降低約 85%，但品質也會下降約 29%。是否划算取決於你的任務對品質的要求。

什麼時候該用 Advisor Strategy，什麼時候不該用？

適合大規模 Agent 部署、混合複雜度任務。不適合所有任務都需要頂級推理能力的場景，或是呼叫量很低、成本不敏感的專案。

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