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Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6：證據導向比較

目前沒有足夠公開證據能支持「單一總冠軍」。Claude Opus 4.7 的官方文件最完整，DeepSeek V4 的價格與輸出規格最清楚。 若重視官方確認的 1M 上下文、程式與代理工作，可先測 Claude；若重視成本、長上下文與大輸出，可先測 DeepSeek。

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Abstract editorial comparison of Claude Opus 4.7, GPT-5.5, DeepSeek V4, and Kimi K2.6 AI models
Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6: Evidence, Not HypeAn evidence-first look at four 2026 AI models across context, pricing, benchmarks, coding, and agent use cases.
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Create a landscape editorial hero image for this Studio Global article: Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6: Evidence, Not Hype. Article summary: As of the April 2026 sources reviewed, there is no defensible overall winner: Claude Opus 4.7 is the best documented with an official 1M context window, while DeepSeek V4 has the clearest pricing rows; GPT 5.5 and Kim.... Topic tags: ai, llm, ai models, openai, anthropic. Reference image context from search candidates: Reference image 1: visual subject "[Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison](https://www.youtube.com/watch?v=M90iB4hpenI). ![Image 4](https://www.youtube.com/watch?v=M90iB4hpenI). [](https://www.youtube.com" source context "Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison - YouTube" Reference image 2: visual subject "[Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison](https://www.youtube.com/watch?v=M90

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前沿模型比較很容易變成「誰第一」的排行榜。但對開發團隊、採購單位或產品負責人來說，更實際的問題是：哪些資訊已被可靠來源支持，哪些仍只是待查證線索？

以目前可檢視到的資料來看，四款模型的公開證據並不平均。Anthropic 對 Claude Opus 4.7 提供了最清楚的官方文件，包括 1M 上下文視窗，以及不收長上下文加價的說明 [1][3]。DeepSeek 的 API 價格頁則提供最具體的規格與費率列，包括 1M 上下文、384K 最大輸出、JSON output、tool calls 與 token 價格 [30]。OpenAI 已在 API 文件與發布頁確認 GPT-5.5，但目前可見官方片段不足以完整比較其價格、上下文、基準分數與模態能力 [13][22]。Moonshot 則把 Kimi K2.6 定位在原生多模態、程式能力與 Agent 表現，但本文資料中不少精確規格與商業資訊仍來自第三方或使用者生成頁面 [37][38][41][42][43][45]。

先講結論

  • 沒有足夠證據支持「總體最佳模型」。 目前公開資料不是同一套評測、同一口徑：Vellum 的 Claude Opus 4.7 摘要列出評測類別但未顯示可直接比較的分數；OpenAI 的 GPT-5.5 發布頁有 evaluations 區塊但片段未列數字；Hugging Face 稱 DeepSeek V4 具競爭力但不是 SOTA；Kimi 官方部落格則建議用官方 API 重現 Kimi-K2.6 官方基準結果 [4][22][32][37]。
  • Claude Opus 4.7 的一手資料最扎實。 Anthropic 稱它是面向 coding 與 AI agents 的 hybrid reasoning model，並具備 1M context window；文件也說 1M 上下文以標準 API 價格提供，沒有長上下文加價 [1][3]。
  • DeepSeek V4 的成本證據最清楚。 DeepSeek 價格頁列出 cache-hit、cache-miss 與 output token 的具體價格，同頁也標示 1M 上下文與 384K 最大輸出 [30]。
  • GPT-5.5 已確認，但官方片段不足以完整排名。 OpenAI API 文件列出 gpt-5.5 與 gpt-5.5-2026-04-23，發布頁也說 2026 年 4 月 24 日更新後 GPT-5.5 與 GPT-5.5 Pro 已可在 API 使用；但目前片段沒有足夠資訊可比較所有維度 [13][22]。
  • Kimi K2.6 值得關注，但規格仍需更多直接驗證。 Moonshot 官網站強調 K2.6 的原生多模態、程式能力與 Agent 表現；Kimi 部落格也建議使用官方 API 重現官方 benchmark 結果 [37][43]。

一張表看證據強弱

模型目前最可靠的事實主要注意事項
Claude Opus 4.7Anthropic 稱其為面向 coding 與 AI agents 的 hybrid reasoning model，具 1M context window；文件說 1M context 以標準 API 價格提供，無長上下文加價 [1][3]。Vellum 摘要列出 benchmark 類別，但片段沒有可直接排名的分數；128K output 與每百萬 input/output token 為 5/25 美元的說法屬第三方資訊，應視為次級證據 [4][5]。
GPT-5.5OpenAI API 文件列出 gpt-5.5 與 gpt-5.5-2026-04-23，標示 long context 與分級 rate limit；OpenAI 發布頁稱 2026 年 4 月 24 日更新後 GPT-5.5 與 GPT-5.5 Pro 已可在 API 使用 [13][22]。目前官方片段未列出精確 context size、output limit、pricing、modality details 或 benchmark 數字；第三方有補充，但可信度低於 OpenAI 官方文件 [14][20][21]。
DeepSeek V4DeepSeek 價格頁顯示 1M context、384K maximum output、JSON output、tool calls、beta chat-prefix completion、beta FIM completion 與具體 token 價格 [30]。Hugging Face 稱 DeepSeek 發布 V4 Pro 與 V4 Flash checkpoints，兩者皆為 1M-token context [32]。V4 Flash/Pro 命名與架構細節在第三方摘要中更清楚；Hugging Face 同時形容其 benchmark 數字具競爭力但非 SOTA [27][32]。
Kimi K2.6Moonshot 官網站稱 K2.6 為原生多模態模型，強調 coding capabilities 與 Agent performance；Kimi 部落格說官方 Kimi-K2.6 benchmark 應使用官方 API 重現 [37][43]。精確 context length、output length、pricing 與 open-weight 狀態，在本文資料中多由第三方或使用者生成片段支持，而非完整一手廠商文件 [38][41][42][45]。

Claude Opus 4.7：官方文件最完整

Claude Opus 4.7 是這次比較中一手資料最完整的模型。Anthropic 將它描述為推進 coding 與 AI agents 前沿的 hybrid reasoning model，產品頁也列出 1M context window [3]。Anthropic 另稱，Opus 4.7 在 coding、vision 與複雜多步驟任務上有更強表現，並在專業知識工作中有更好結果 [3]。

最明確的差異點是長上下文。Anthropic 文件說 Claude Opus 4.7 提供 1M context window，且以標準 API 價格供應，沒有 long-context premium [1]。同份文件也指出，它在知識工作任務有明顯提升，尤其是需要模型視覺檢查自身輸出的情境，例如 .docx 修訂、.pptx 編輯、圖表分析與圖像分析 [1]。

第三方資料可作規劃參考，但不應與官方聲明混為一談。Caylent 稱 Opus 4.7 最高支援 128K output tokens，並沿用 Opus 標準價格：每百萬 input tokens 5 美元、每百萬 output tokens 25 美元 [5]。這對成本試算有用，但本文中最強的一手價格證據，仍是 Anthropic 對「不收長上下文加價」的說明 [1]。

GPT-5.5：已確認存在，但官方細節還不夠

GPT-5.5 已足以放進採購或技術評估清單。OpenAI API 文件列出 gpt-5.5 與日期版本 gpt-5.5-2026-04-23，並標示 long context 與 rate-limit tiers [13]。OpenAI 發布頁日期為 2026 年 4 月 23 日，並在 2026 年 4 月 24 日更新中表示 GPT-5.5 與 GPT-5.5 Pro 已可在 API 使用 [22]。

但這只能確認 API 狀態，還不足以負責任地把 GPT-5.5 排在其他三款模型之前或之後。本文可見的 OpenAI 官方片段沒有列出精確 context size、output limit、pricing、benchmark scores、modality details、coding performance 或 latency [13][22]。

第三方頁面補上了一些線索，但它們不等同於 OpenAI 官方文件。DesignForOnline 稱 GPT-5.5 價格為每百萬 input tokens 5 美元、每百萬 output tokens 30 美元 [14]。LLM Stats 稱其 API context window 為 1M input / 128K output，並支援 text 與 image input、text output [20][21]。這些資訊適合列入供應商確認清單，但不宜當成最終一手證據。

DeepSeek V4：價格與輸出規格最具體

DeepSeek 在這次比較中提供了最具體的成本表。其 API pricing 頁面列出 1M context length、384K maximum output、JSON output、tool calls、beta chat-prefix completion 與 beta FIM completion [30]。同頁也列出 token 價格：cache-hit input 為 0.028 與 0.03625 美元，cache-miss input 為 0.14 與 0.435 美元，output tokens 為 0.28 與 0.87 美元；片段同時顯示限時折扣說明與刪除線原價 [30]。

V4 版本本身也有支持資料，但部分較間接。EvoLink 稱截至 2026 年 4 月 24 日，DeepSeek 官方 API 文件列出 deepseek-v4-flash 與 deepseek-v4-pro，發布官方價格，並記載 1M context 與 384K max output [27]。Hugging Face 則稱 DeepSeek 發布了兩個 mixture-of-experts checkpoints：DeepSeek-V4-Pro 為 1.6T total parameters、49B active；DeepSeek-V4-Flash 為 284B total parameters、13B active [32]。Hugging Face 也說兩者都有 1M-token context window，benchmark 數字具競爭力但不是 SOTA [32]。

OpenRouter 的 V4 Pro 頁面另列 1,048,576-token context window，以及每百萬 input tokens 0.435 美元、每百萬 output tokens 0.87 美元 [31]。這有助於交叉比對 V4 Pro 的商業資訊，但由於 DeepSeek 官方頁面含有限時折扣語句，團隊在上線前仍應直接確認最新價格 [30][31]。

Kimi K2.6：定位清楚，精確規格仍需驗證

Kimi K2.6 的產品方向切中目前前沿模型需求，但本文資料中，其精確規格較少由一手文件完整支持。Moonshot 官網站稱 K2.6 是原生多模態模型，並強調 coding capabilities 與 Agent performance [43]。Kimi 技術部落格片段則表示，若要重現官方 Kimi-K2.6 benchmark results，建議使用官方 API；第三方 provider 則可參考 Kimi Vendor Verifier [37]。

較具體的 Kimi 數字，多數來自第三方。LLM Stats 稱 Kimi K2.6 的 input context window 為 262,144 tokens，且最高可產生 262,144 output tokens [42]。DesignForOnline 稱 Kimi K2.6 具 262K context、vision、tool use、function calling，價格從每百萬 tokens 0.7500 美元起 [41]。Atlas Cloud 則列出 Kimi K2.6 API 價格從每百萬 tokens 0.95 美元起 [38]。另有 LinkedIn 文章稱 Kimi K2.6 是 open-weight model，但這屬使用者生成證據，除非 Moonshot 直接確認授權條款，否則應以較低信心看待 [45]。

為什麼目前不能直接封王？

因為缺少完整、同口徑、可交叉比較的公開分數。Vellum 的 Claude Opus 4.7 摘要列出 coding、agentic、finance、reasoning、multimodal/vision、search 與 safety 等評測領域，但片段沒有實際分數 [4]。OpenAI 的 GPT-5.5 發布頁有 evaluations 結構，但片段未顯示數字 [22]。Hugging Face 說 DeepSeek V4 的 benchmark 具競爭力但不是 SOTA [32]。Kimi 官方部落格片段則提到使用官方 API 重現 Kimi-K2.6 benchmark results，卻未在片段中列出結果 [37]。

這點很重要。模型排名會隨任務改變：寫程式、長上下文檢索、多模態文件分析、tool-calling 穩定度、Agent 規劃、延遲，以及 cache hit 與 cache miss 下的實際成本，都是不同測試。若沒有同一套 benchmark 同時覆蓋四款模型，「最佳模型」更像行銷語，而不是證據結論。

你該先測哪一款？

  • 先測 Claude Opus 4.7：如果你重視官方明確文件支持的 1M context、coding、AI agents、vision、複雜多步驟工作與知識工作提升 [1][3]。
  • 先測 GPT-5.5：如果你的產品已建在 OpenAI 基礎設施上，且主要需求是驗證已文件化的 gpt-5.5 API 路徑 [13][22]。
  • 先測 DeepSeek V4：如果第一道篩選條件是成本、長上下文、最大輸出、JSON output 或 tool-call support；DeepSeek 的價格頁是本文中最具體的成本來源 [30]。
  • 先測 Kimi K2.6：如果你看重 Moonshot 的多模態 coding 與 Agent 方向，但要另外確認 context、pricing、output、license 與 provider 細節 [37][38][41][42][43][45]。

實務評估建議

上線前不要只看榜單。更可靠的做法，是用同一組 prompts、tools、context sizes、file inputs 與 scoring rubrics 做任務型 bake-off。至少追蹤五件事：任務成功率、tool-call reliability、long-context accuracy、latency，以及完全計入快取與輸出的 token cost。

對 DeepSeek，要把 cache-hit 與 cache-miss 成本分開算，因為官方價格頁明確拆成不同列 [30]。對 GPT-5.5，要把 OpenAI 已確認的 API 資訊，與第三方 context/pricing 說法分開標示，直到官方文件補足細節 [13][14][20][21][22]。對 Kimi K2.6，provider listings 與使用者生成的 open-weight 說法應視為待確認線索，不宜直接當成採購依據 [37][38][41][42][45]。

最後判斷

若只看證據、暫時不看聲量，Claude Opus 4.7 是本文中官方文件最清楚的旗艦模型，尤其在 1M context、coding、AI agents 與 knowledge-work claims 上 [1][3]。DeepSeek V4 擁有最強的價格證據，也有可信的長上下文資訊，但 V4 Flash/Pro 的架構與命名細節有一部分仍仰賴第三方摘要補足 [27][30][32]。GPT-5.5 已由 OpenAI 的 API 與發布資料確認，但目前官方片段太少，不足以完整比較性能 [13][22]。Kimi K2.6 在多模態、coding 與 Agent 用途中有可信官方定位，但許多精確技術與商業資訊仍需要更強的一手確認 [37][38][41][42][43][45]。

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重點整理

  • 目前沒有足夠公開證據能支持「單一總冠軍」。Claude Opus 4.7 的官方文件最完整，DeepSeek V4 的價格與輸出規格最清楚。
  • 若重視官方確認的 1M 上下文、程式與代理工作，可先測 Claude；若重視成本、長上下文與大輸出，可先測 DeepSeek。
  • GPT 5.5 已出現在 OpenAI API 與發布頁，但官方片段細節不足；Kimi K2.6 的多模態、程式與 Agent 定位明確，但許多規格仍需向官方驗證。

大家也會問

「Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6：證據導向比較」的簡短答案是什麼？

目前沒有足夠公開證據能支持「單一總冠軍」。Claude Opus 4.7 的官方文件最完整，DeepSeek V4 的價格與輸出規格最清楚。

最值得優先驗證的重點是什麼？

目前沒有足夠公開證據能支持「單一總冠軍」。Claude Opus 4.7 的官方文件最完整，DeepSeek V4 的價格與輸出規格最清楚。 若重視官方確認的 1M 上下文、程式與代理工作，可先測 Claude；若重視成本、長上下文與大輸出，可先測 DeepSeek。

接下來在實務上該怎麼做？

GPT 5.5 已出現在 OpenAI API 與發布頁，但官方片段細節不足；Kimi K2.6 的多模態、程式與 Agent 定位明確，但許多規格仍需向官方驗證。

下一步適合探索哪個相關主題？

繼續閱讀「Claude Opus 4.7、GPT-5.5、DeepSeek V4、Kimi K2.6 基準比較：2026 年誰最值得信？」，從另一個角度查看更多引用來源。

開啟相關頁面

我應該拿這個和什麼比較？

將這個答案與「DeepSeek V4 工程解析：1M 上下文、MoE 與 API 遷移重點」交叉比對。

開啟相關頁面

繼續深入研究

Comparativa de benchmarks 2026 entre Claude Opus 4.7, GPT-5.5, DeepSeek V4 y Kimi K2.6

Claude Opus 4.7、GPT-5.5、DeepSeek V4、Kimi K2.6 基準比較：2026 年誰最值得信？

Claude Opus 4.7、GPT-5.5、DeepSeek V4、Kimi K2.6：2026 基準測試與決策結論

DeepSeek V4 工程架构示意图，包含 1M 上下文、MoE 专家路由和 API 服务化元素

DeepSeek V4 工程解析：1M 上下文、MoE 與 API 遷移重點

DeepSeek V4 工程解析：1M 上下文、MoE 與 API 遷移重點

木材市場與需求彈性概念的編輯插圖

Northwest vs. Southeast Timber 彈性題解：為什麼是 larger; larger

Northwest 與 Southeast Timber：替代性越高，為什麼答案是 larger; larger？

抽象 AI 晶片與多個聊天機器人平台競爭的示意圖，象徵 DeepSeek 挑戰 OpenAI、Claude、Gemini 與 Grok

DeepSeek 會擊敗 OpenAI、Claude、Gemini、Grok 嗎？關鍵不是榜單，而是成本與信任

DeepSeek 會擊敗 OpenAI、Claude、Gemini、Grok 嗎？關鍵不是榜單，而是成本與信任

研究對話

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研究問題

Research & compare Claude Opus 4.7, GPT-5.5, DeepSeek V4, Kimi K2.6 as comprehensively as possible

Studio Global AI40 個來源

附引用的答案

Based only on the provided evidence, Claude Opus 4.7 is the only model with enough sourced detail to compare meaningfully. GPT-5.5 is confirmed as an OpenAI API model, Kimi K2.6 has only a limited third-party snippet, and DeepSeek V4 has no supporting evidence in the provided material, so any detailed ranking across all four would be unsupported.

Key findings

  • Claude Opus 4.7 is described by Anthropic as a “hybrid reasoning model” focused on coding and AI agents, with a 1M context window [4].
  • Anthropic’s documentation says Claude Opus 4.7 provides a 1M context window at standard API pricing with no long-context premium [2].
  • Anthropic’s documentation also says Claude Opus 4.7 shows “meaningful gains” on knowledge-worker tasks [2].
  • A third-party technical writeup describes Claude Opus 4.7 as Anthropic’s most capable generally available model for coding, enterprise workflows, multimodal reasoning, financial analysis, life sciences, cybersecurity, and long-running agentic work [6].
  • GPT-5.5 is listed in OpenAI’s API documentation as gpt-5.5, with a dated model version gpt-5.5-2026-04-23 [1].
  • The provided GPT-5.5 evidence only confirms API model listing and mentions rate limits; it does not provide benchmark results, modality details, context length, pricing, or capability claims [1].
  • Kimi K2.6 is described in one LinkedIn article snippet as an open-weight model from Moonshot AI, positioned against GPT-5.4 and Claude Opus 4.6 on coding benchmarks [45].
  • The provided evidence contains no source for DeepSeek V4. Insufficient evidence.
  • There is insufficient evidence to make a defensible overall ranking among Claude Opus 4.7, GPT-5.5, DeepSeek V4, and Kimi K2.6.

Comparison table

CategoryClaude Opus 4.7GPT-5.5DeepSeek V4Kimi K2.6
Evidence strengthStrongest among the four, with official Anthropic sources plus third-party analysis [2][4][6]Limited official OpenAI API evidence [1]No provided evidenceVery limited third-party evidence [45]
ProviderAnthropic [4]OpenAI [1]Insufficient evidenceMoonshot AI, according to the provided LinkedIn snippet [45]
Model statusPublic Claude product/API access is referenced by Anthropic [4]Listed in OpenAI API docs as gpt-5.5 and gpt-5.5-2026-04-23 [1]Insufficient evidenceDescribed as released in the provided LinkedIn snippet [45]
Context window1M context window [2][4]Insufficient evidenceInsufficient evidenceInsufficient evidence
Pricing evidence1M context at standard API pricing with no long-context premium [2]Insufficient evidence beyond rate-limit reference [1]Insufficient evidenceInsufficient evidence
Output limitA third-party source says up to 128K output tokens [6]Insufficient evidenceInsufficient evidenceInsufficient evidence
CodingAnthropic positions it as frontier-level for coding, and a third-party source says it is strong for coding [4][6]Insufficient evidenceInsufficient evidencePositioned against GPT-5.4 and Claude Opus 4.6 on coding benchmarks, according to one LinkedIn snippet [45]
Agents / tool useAnthropic says it pushes the frontier for AI agents [4]Insufficient evidenceInsufficient evidenceInsufficient evidence
Knowledge workAnthropic says it has meaningful gains on knowledge-worker tasks [2]Insufficient evidenceInsufficient evidenceInsufficient evidence
Multimodal reasoningA third-party source lists multimodal reasoning as a target capability area [6]Insufficient evidenceInsufficient evidenceInsufficient evidence
Open weightsNo evidence that Claude Opus 4.7 is open-weightNo evidence that GPT-5.5 is open-weightInsufficient evidenceDescribed as open-weight in one LinkedIn snippet [45]
BenchmarksA Vellum article exists discussing Claude Opus 4.7 benchmarks, including coding, agentic, finance, reasoning, and search-related categories, but the provided snippet does not include specific scores [5]Insufficient evidenceInsufficient evidenceOnly a broad claim about positioning on coding benchmarks is provided [45]

Model-by-model assessment

Claude Opus 4.7

Claude Opus 4.7 has the clearest evidence base in the provided material. Anthropic describes it as a hybrid reasoning model that advances coding and AI-agent use cases and includes a 1M context window [4].

The most concrete differentiator is long context: Anthropic says Claude Opus 4.7 provides a 1M context window at standard API pricing with no long-context premium [2]. That makes it the only model in the provided evidence with a clearly documented context-window advantage [2][4].

Claude Opus 4.7 also has the broadest capability claims in the supplied sources. Anthropic says it shows meaningful gains on knowledge-worker tasks [2], while a third-party source positions it for coding, enterprise workflows, multimodal reasoning, financial analysis, life sciences, cybersecurity, and long-running agentic work [6].

However, the provided benchmark evidence is incomplete. A Vellum article is listed as explaining Claude Opus 4.7 benchmarks across categories such as SWE-bench Verified, SWE-bench Pro, Terminal-Bench 2.0, MCP-Atlas, Finance Agent v1.1, OSWorld-Verified, BrowseComp, and GPQA, but the provided snippet does not include actual scores or rankings [5].

GPT-5.5

GPT-5.5 is confirmed in the provided evidence as an OpenAI API model. The OpenAI API documentation snippet lists gpt-5.5 and a dated version, gpt-5.5-2026-04-23 [1].

The same OpenAI snippet mentions rate limits, but only in general terms: rate limits cap requests or token usage and depend on usage tier [1]. The snippet does not provide GPT-5.5’s context window, pricing, benchmark results, modalities, training cutoff, tool-use features, or coding performance [1].

Because the provided GPT-5.5 evidence is minimal, it cannot be fairly compared against Claude Opus 4.7 on capability, cost, latency, benchmarks, or agentic performance.

DeepSeek V4

No evidence for DeepSeek V4 is included in the provided source list. Insufficient evidence.

That means there is no sourced basis here to describe DeepSeek V4’s release status, context window, pricing, weights, architecture, benchmarks, coding performance, reasoning performance, or API availability.

Kimi K2.6

The only provided evidence for Kimi K2.6 is a LinkedIn article snippet saying Moonshot AI released Kimi K2.6 as an open-weight model [45]. The same snippet says it is positioned against GPT-5.4 and Claude Opus 4.6 on coding benchmarks [45].

That evidence is too thin for a robust evaluation. The snippet does not provide benchmark names, scores, methodology, context length, license terms, API availability, hardware requirements, pricing, safety evaluations, or comparisons against Claude Opus 4.7 or GPT-5.5 [45].

Evidence notes

  • Official vendor sources support the strongest claims for Claude Opus 4.7 and GPT-5.5: Anthropic sources support Claude’s 1M context and positioning [2][4], and OpenAI’s API documentation supports the existence of GPT-5.5 as an API model [1].
  • Third-party sources add useful but weaker evidence for Claude Opus 4.7’s broader enterprise and domain positioning [6].
  • The Kimi K2.6 evidence is especially limited because it comes only from a LinkedIn article snippet, not an official Moonshot AI source or benchmark report [45].
  • The provided DeepSeek V4 evidence is absent. Insufficient evidence.

Limitations / uncertainty

  • There are no direct benchmark scores in the provided evidence for GPT-5.5, DeepSeek V4, or Kimi K2.6.
  • There are no apples-to-apples benchmark numbers across all four models.
  • There is no supplied evidence for latency, cost per token, availability by region, safety evaluations, tool-use reliability, coding benchmark scores, or real-world deployment performance across the four models.
  • Claims about Claude Opus 4.7 are much better supported than claims about the other three models because the evidence includes official Anthropic documentation [2][4].
  • Any definitive ranking such as “best overall,” “best coder,” “best reasoning model,” or “best value” would be unsupported by the provided evidence.

Summary

Claude Opus 4.7 appears strongest on the available evidence because it has official documentation for a 1M context window, no long-context premium, coding and agentic positioning, and knowledge-work improvements [2][4]. GPT-5.5 is confirmed as an OpenAI API model, but the provided evidence does not describe its capabilities beyond model listing and rate-limit context [1]. Kimi K2.6 is only lightly supported by a third-party snippet describing it as open-weight and coding-benchmark-oriented [45]. DeepSeek V4 cannot be assessed from the provided evidence.

來源

  • [1] What's new in Claude Opus 4.7platform.claude.com

    We suggest updating your max tokens parameters to give additional headroom, including compaction triggers. Claude Opus 4.7 provides a 1M context window at standard API pricing with no long-context premium. Capability improvements Knowledge work Claude Opus...

  • [3] Claude Opus 4.7 - Anthropicanthropic.com

    Skip to main contentSkip to footer []( Research Economic Futures Commitments Learn News Try Claude Claude Opus 4.7 Image 1: Claude Opus 4.7 Image 2: Claude Opus 4.7 Hybrid reasoning model that pushes the frontier for coding and AI agents, featuring a 1M con...

  • [4] Claude Opus 4.7 Benchmarks Explained - Vellumvellum.ai

    Apr 16, 2026•16 min•ByNicolas Zeeb Guides CONTENTS Key observations of reported benchmarks Coding capabilities SWE-bench Verified SWE-bench Pro Terminal-Bench 2.0 Agentic capabilities MCP-Atlas (Scaled tool use) Finance Agent v1.1 OSWorld-Verified (Computer...

  • [5] Claude Opus 4.7 Deep Dive: Capabilities, Migration, and the ...caylent.com

    At a spec level, Opus 4.7 is positioned as Anthropic’s most capable generally available model for coding, enterprise workflows, multimodal reasoning, financial analysis, life sciences, cybersecurity, and long-running agentic work. It supports a 1M context w...

  • [13] GPT-5.5 Model | OpenAI APIdevelopers.openai.com

    Image 3: gpt-5.5 gpt-5.5 gpt-5.5-2026-04-23 gpt-5.5-2026-04-23 Rate limits Rate limits ensure fair and reliable access to the API by placing specific caps on requests or tokens used within a given time period. Your usage tier determines how high these limit...

  • [14] GPT-5.5 (high) Review | Pricing, Benchmarks & Capabilities (2026)designforonline.com

    Pricing Token Type Cost per 1M tokens Cost per 1K tokens --- Input $5.00 $0.005000 Output $30.00 $0.030000 Leaderboard Categories Explore Related Models openai openai openai OpenAI Data sourced from OpenRouter API, Artificial Analysis and Hugging Face Open...

  • [20] GPT-5.5 vs GPT-5.4: Pricing, Speed, Context, Benchmarks - LLM Statsllm-stats.com

    Spec GPT-5.4 GPT-5.5 --- Release date Mar 5, 2026 Apr 23, 2026 Model ID gpt-5.4 gpt-5.5 Standard input / output price $2.50 / $15.00 per 1M $5.00 / $30.00 per 1M Batch & Flex pricing 0.5× standard 0.5× standard Priority pricing 2.5× standard 2.5× standard A...

  • [21] GPT-5.5: Pricing, Benchmarks & Performance - LLM Statsllm-stats.com

    thinking:true Modalities In text image Out text Resources API ReferencePlaygroundBlog CallingBox The voice stack, already built Telephony, STT, TTS, and orchestration in one API. Give your AI agents a phone number and have them make calls for you. Start for...

  • [22] Introducing GPT-5.5 - OpenAIopenai.com

    Introducing GPT-5.5 OpenAI Skip to main content Log inTry ChatGPT(opens in a new window) Research Products Business Developers Company Foundation(opens in a new window) Try ChatGPT(opens in a new window)Login OpenAI Table of contents Model capabilities Next...

  • [27] DeepSeek V4 API Review 2026: Flash vs Pro Guide - EvoLink.AIevolink.ai

    As of April 24, 2026, DeepSeek's official API docs now list deepseek-v4-flash and deepseek-v4-pro , publish official pricing for both, and document 1M context plus 384K max output. Reuters separately reported on the same date that V4 launched in preview, wh...

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    See Thinking Mode for how to switch CONTEXT LENGTH 1M MAX OUTPUT MAXIMUM: 384K FEATURESJson Output✓✓ Tool Calls✓✓ Chat Prefix Completion（Beta）✓✓ FIM Completion（Beta）Non-thinking mode only Non-thinking mode only PRICING 1M INPUT TOKENS (CACHE HIT)$0.028$0.03...

  • [31] DeepSeek V4 Pro - API Pricing & Providersopenrouter.ai

    DeepSeek V4 Pro - API Pricing & Providers OpenRouter Skip to content OpenRouter / FusionModelsChatRankingsAppsEnterprisePricingDocs Sign Up Sign Up DeepSeek: DeepSeek V4 Pro deepseek/deepseek-v4-pro ChatCompare Released Apr 24, 2026 1,048,576 context$0.435/...

  • [32] DeepSeek-V4: a million-token context that agents can actually usehuggingface.co

    DeepSeek released V4 today. Two MoE checkpoints are on the Hub: DeepSeek-V4-Pro at 1.6T total parameters with 49B active, and DeepSeek-V4-Flash at 284B total with 13B active. Both have a 1M-token context window. The benchmark numbers are competitive, but no...

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    To reproduce official Kimi-K2.6 benchmark results, we recommend using the official API. For third-party providers, refer to Kimi Vendor Verifier (KVV) to ...

  • [38] Kimi K2.6 API by MOONSHOTAI - Competitive Pricing - Atlas Cloudatlascloud.ai

    Kimi K2.6 API - competitive pricing, transparent rates. Starting from $0.95/1M tokens. Unified API access, OpenAI-compatible endpoints, real-time inference.

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    MoonshotAI: Kimi K2.6 by MoonshotAI. 262K context, from $0.7500/1M tokens, vision, tool use, function calling. See benchmarks, comparisons ... 3 days ago

  • [42] Kimi K2.6: Pricing, Benchmarks & Performance - LLM Statsllm-stats.com

    Kimi K2.6 has a context window of 262,144 tokens for input and can generate up to 262,144 tokens of output. The best provider for maximum ... 6 days ago

  • [43] Moonshot AImoonshot.ai

    K2.6 is a natively multimodal model, powerful coding capabilities, and Agent performance — multiple modes, your choice. Explore Features. Discover Kimi ...

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    Moonshot AI has released Kimi K2.6 as an open-weight model, positioning it directly against GPT-5.4 and Claude Opus 4.6 on coding benchmarks ... 6 days ago

Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6：證據導向比較 | 深度研究 | Studio Global