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Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6：邊個有真憑實據？

目前未有足夠公開資料支持「總冠軍」講法；各家 benchmark 資料唔齊，唔適合硬排一個絕對第一 [4][22][32][37]。 Claude Opus 4.7 第一手文件最完整：Anthropic 文件列出 1M context，並指標準 API 收費下無長上下文加價 [1][3]；DeepSeek V4 則有最清晰價目表 [30]。

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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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前沿模型比較好容易變成「邊個最勁」嘅賽馬旁述。但如果你係要揀 API、做產品路線、或者寫採購評估，問題應該更實際：邊啲講法有足夠證據支持？

呢次比較 Claude Opus 4.7、GPT-5.5、DeepSeek V4 同 Kimi K2.6，答案唔係一句「某某完勝」咁簡單。公開證據非常唔平均：Anthropic 對 Claude Opus 4.7 有最清楚官方文件；DeepSeek 有最具體價目同規格表；OpenAI 確認 GPT-5.5 存在同 API 可用，但可見官方片段未足以完整比較；Moonshot 對 Kimi K2.6 嘅方向定位清楚，不過好多精確規格仍依賴第三方或用戶生成資料。

先講結論

  • 未有一個模型可以憑現有公開資料證明「全方位最好」。 Claude Opus 4.7 有第三方文章列出 benchmark 類別但片段未見分數；OpenAI GPT-5.5 發布頁有 evaluations 區段但片段未見數字；Hugging Face 形容 DeepSeek V4 benchmark 具競爭力但未達 SOTA；Kimi 官方 blog 則建議用官方 API 重現 benchmark 結果 [4][22][32][37]。
  • Claude Opus 4.7 係官方文件最紮實嗰個。 Anthropic 稱佢係面向 coding 同 AI agents 嘅 hybrid reasoning model，具 1M context window；文件亦指 1M context 以標準 API 收費提供，無長上下文 premium [1][3]。
  • DeepSeek V4 嘅成本證據最清楚。 DeepSeek 價格頁列出 1M context、384K 最大輸出、JSON output、tool calls，以及 cache hit、cache miss、output token 價格 [30]。
  • GPT-5.5 已確認，但官方可見資料未夠完整。 OpenAI API 文件列出 gpt-5.5 同 gpt-5.5-2026-04-23，亦標示 long context；OpenAI 發布頁指 2026 年 4 月 24 日更新後 GPT-5.5 同 GPT-5.5 Pro 已可經 API 使用 [13][22]。
  • Kimi K2.6 值得留意，但要再核實細節。 Moonshot 網站突出 K2.6 原生多模態、coding 能力同 agent performance；Kimi blog 則建議用官方 API 重現官方 benchmark 結果 [37][43]。

一眼比較

模型較有力證據主要保留
Claude Opus 4.7Anthropic 稱其為面向 coding 同 AI agents 嘅 hybrid reasoning model，具 1M context；文件指 1M context 以標準 API 收費提供，無長上下文 premium [1][3]。可見 Vellum 片段列出多個 benchmark 類別，但未見足夠分數作直接排名；128K output 同每百萬 token $5/$25 等資料主要來自第三方 [4][5]。
GPT-5.5OpenAI API 文件列出 gpt-5.5、gpt-5.5-2026-04-23，標示 long context 並展示 rate-limit tiers；發布頁指 GPT-5.5 / GPT-5.5 Pro 已在 API 可用 [13][22]。可見官方片段未列出精確 context size、output limit、定價、模態細節或 benchmark 數字；第三方資料只可作採購核實線索 [14][20][21]。
DeepSeek V4DeepSeek 價格頁列出 1M context、384K 最大輸出、JSON output、tool calls、beta chat-prefix completion、beta FIM completion 同明確 token 價格 [30]。Hugging Face 指 V4 Pro / Flash checkpoint 均有 1M-token context [32]。V4 Flash / Pro 命名同架構細節，有部分係第三方整理較清楚；Hugging Face 亦指 benchmark 具競爭力但非 SOTA [27][32]。
Kimi K2.6Moonshot 指 K2.6 原生多模態，主打 coding 同 agent performance；Kimi blog 建議以官方 API 重現 Kimi-K2.6 benchmark [37][43]。精確 context、output、價格同 open-weight 狀態，多數仍由第三方或用戶生成頁面支持，置信度較低 [38][41][42][45]。

Claude Opus 4.7：官方文件最完整

Claude Opus 4.7 喺呢四個模型入面，第一手證據最齊。Anthropic 形容佢係一個 hybrid reasoning model，主打 coding 同 AI agents，並具備 1M context window [3]。Anthropic 產品頁亦指 Opus 4.7 喺 coding、vision、複雜多步任務同專業知識工作方面有更強表現 [3]。

最有實際採購價值嘅位係長上下文。Anthropic 文件寫明 Claude Opus 4.7 提供 1M context window，而且用標準 API pricing，無 long-context premium [1]。同一份文件亦指模型喺知識工作任務有明顯進步，特別係需要視覺檢查自己輸出嘅場景，例如 .docx redlining、.pptx editing、圖表同 figure analysis [1]。

不過，benchmark 仍要小心。Vellum 文章片段列出 coding、agentic capabilities、finance、reasoning、multimodal / vision、search、安全等類別，但可見片段未提供足夠分數，不能直接話 Claude 一定贏 GPT-5.5、DeepSeek V4 或 Kimi K2.6 [4]。

GPT-5.5：存在同 API 狀態已確認，但規格未夠透明

GPT-5.5 係可以放入候選清單嘅模型。OpenAI API 文件列出 gpt-5.5 同日期版本 gpt-5.5-2026-04-23，標示 long context，並顯示分級 rate limit 資料 [13]。OpenAI 發布頁日期為 2026 年 4 月 23 日，並指 4 月 24 日更新後 GPT-5.5 同 GPT-5.5 Pro 已可經 API 使用 [22]。

問題係：呢啲資料只足以確認「有呢個模型」同「API 路徑存在」，未足以完整比較。可見官方片段未列明精確 context size、output limit、價格、benchmark 分數、模態能力、coding 表現或 latency [13][22]。

第三方資料有補充，但唔應該當成 OpenAI 官方規格。DesignForOnline 報稱 GPT-5.5 價格為每百萬 input token $5、output token $30 [14]；LLM Stats 報稱 API context 為 1M input / 128K output，並支援 text + image input、text output [20][21]。呢啲可作供應商查證清單，但唔應該單靠佢哋落採購結論。

DeepSeek V4：價錢同輸出上限最易核對

DeepSeek 最大優勢係價目表夠具體。DeepSeek API 價格頁列出 1M context length、384K maximum output、JSON output、tool calls、beta chat-prefix completion 同 beta FIM completion [30]。同頁亦列出 cache-hit input、cache-miss input 同 output token 價格，包括 cache-hit input $0.028 / $0.03625、cache-miss input $0.14 / $0.435、output $0.28 / $0.87；片段亦見 limited-time discount 同劃線原價 [30]。

V4 具體版本方面，EvoLink 指截至 2026 年 4 月 24 日，DeepSeek 官方 API docs 已列 deepseek-v4-flash 同 deepseek-v4-pro，並發布官方價格、1M context 同 384K max output [27]。Hugging Face 則指 DeepSeek 發布 V4，包含兩個 MoE checkpoints：DeepSeek-V4-Pro 為 1.6T total parameters、49B active；DeepSeek-V4-Flash 為 284B total、13B active；兩者均有 1M-token context window [32]。但同一段亦講明 benchmark 係 competitive，但唔係 state of the art [32]。

實務上，如果你第一關係成本、長上下文、大輸出、JSON output 或 tool-call support，DeepSeek V4 應該早測。不過，平同長 context 唔等於自動最穩；質素、可靠度、安全、latency、tool-use 成功率，仍然要用自己 workload 測。

Kimi K2.6：定位吸引，但好多數字要再核實

Kimi K2.6 嘅方向同市場定位幾清楚，但可核實規格相對薄弱。Moonshot 網站指 K2.6 係 natively multimodal model，並突出 coding capabilities 同 Agent performance [43]。Kimi 技術 blog 片段亦指，如要重現官方 Kimi-K2.6 benchmark results，建議使用官方 API，第三方 provider 則參考 Kimi Vendor Verifier [37]。

較精確嘅 Kimi 數字多數來自第三方。LLM Stats 指 Kimi K2.6 input context 為 262,144 tokens，並可輸出最多 262,144 tokens [42]。DesignForOnline 指 Kimi K2.6 有 262K context、vision、tool use、function calling，價格由每百萬 token $0.7500 起 [41]。Atlas Cloud 則列 Kimi K2.6 API pricing 由每百萬 token $0.95 起 [38]。另有 LinkedIn 文章稱 Kimi K2.6 為 open-weight model，但呢類用戶生成證據置信度較低，最好等 Moonshot 直接確認 license terms [45]。

所以，Kimi K2.6 值得用於多模態 coding 同 agent workflow 測試；但如要上 production，應先向 Moonshot 或官方 API 來源核實 license、context length、output limits、pricing、benchmark methodology 同 provider compatibility [37][43]。

點解而家唔應該封「benchmark 冠軍」？

因為資料唔係同一把尺。Claude Opus 4.7 可見第三方摘要列出好多 benchmark 類別，但無足夠分數 [4]。OpenAI GPT-5.5 發布頁有 evaluations 區段，但片段未顯示數字 [22]。Hugging Face 指 DeepSeek V4 benchmark competitive，但非 SOTA [32]。Kimi 官方 blog 只提到可用官方 API 重現 Kimi-K2.6 benchmark，片段未直接列出結果 [37]。

而模型排名好視乎工作類型：coding、長上下文檢索、多模態文件分析、工具調用可靠度、agent planning、latency、cache hit / cache miss 後嘅實際成本，全部都係唔同考試。無同一套 benchmark、同一組 prompt、同一成本假設，就話某一個「全面最好」，更似 marketing 多過工程判斷。

如果要測，邊個先？

  • 先測 Claude Opus 4.7：如果你最重視官方清楚列明嘅 1M context、coding、AI agents、vision、複雜多步任務同知識工作改善 [1][3]。
  • 先測 GPT-5.5：如果你產品已深度用 OpenAI infrastructure，而你主要想驗證 gpt-5.5 API 路徑同現有系統整合 [13][22]。
  • 先測 DeepSeek V4：如果你第一輪篩選係成本、長 context、最大輸出、JSON output 或 tool-call support；DeepSeek 價格頁係今次最具體嘅成本來源 [30]。
  • 先測 Kimi K2.6：如果你想追 Moonshot 喺多模態、coding 同 agent 方向嘅新模型，但要另外核實 context、pricing、output、license 同 provider 細節 [37][38][41][42][43][45]。

實用評估方法

唔好淨係睇 leaderboard。最好用你自己嘅任務做 bake-off：同一批 prompts、同一批工具、同一個 context size、同一批文件輸入、同一套評分 rubrics。至少記錄五件事：任務成功率、tool-call 可靠度、長上下文準確度、latency、以及連 cache hit / cache miss 一齊計嘅完整 token 成本。

對 DeepSeek，要分開 cache-hit 同 cache-miss 成本，因為官方價格頁清楚拆開呢幾行 [30]。對 GPT-5.5，要分清 OpenAI 已確認資料同第三方 context / pricing claims，等官方文件補齊先作最終比較 [13][14][20][21][22]。對 Kimi K2.6，就要將 provider listing 同用戶生成 open-weight 講法當成線索，而唔係採購結論 [37][38][41][42][45]。

最後判斷

按「證據」而唔係「聲量」計，Claude Opus 4.7 係今次最清楚有官方文件支持嘅旗艦模型，尤其係 1M context、coding、AI agents 同知識工作相關主張 [1][3]。DeepSeek V4 有最強價格證據，長上下文同大輸出資料亦具體，但部分 V4 Flash / Pro 架構同命名細節仍較依賴第三方整理 [27][30][32]。GPT-5.5 已由 OpenAI API 文件同發布頁確認，但可見官方片段不足以支持完整 performance 排名 [13][22]。Kimi K2.6 喺多模態、coding 同 agent 用例上定位可信，但精確技術同商業條款仍需要更強第一手確認 [37][38][41][42][43][45]。

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重點

  • 目前未有足夠公開資料支持「總冠軍」講法；各家 benchmark 資料唔齊，唔適合硬排一個絕對第一 [4][22][32][37]。
  • Claude Opus 4.7 第一手文件最完整：Anthropic 文件列出 1M context，並指標準 API 收費下無長上下文加價 [1][3]；DeepSeek V4 則有最清晰價目表 [30]。
  • GPT 5.5 已在 OpenAI API 與發布頁確認，但官方片段未列完整規格 [13][22]；Kimi K2.6 多模態、編程與 agent 定位明確，但好多細節仍要再向官方核實 [37][43]。

人們還問

「Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6：邊個有真憑實據？」的簡短答案是什麼？

目前未有足夠公開資料支持「總冠軍」講法；各家 benchmark 資料唔齊，唔適合硬排一個絕對第一 [4][22][32][37]。

首先要驗證的關鍵點是什麼？

目前未有足夠公開資料支持「總冠軍」講法；各家 benchmark 資料唔齊，唔適合硬排一個絕對第一 [4][22][32][37]。 Claude Opus 4.7 第一手文件最完整：Anthropic 文件列出 1M context，並指標準 API 收費下無長上下文加價 [1][3]；DeepSeek V4 則有最清晰價目表 [30]。

接下來在實務上我該做什麼？

GPT 5.5 已在 OpenAI API 與發布頁確認，但官方片段未列完整規格 [13][22]；Kimi K2.6 多模態、編程與 agent 定位明確，但好多細節仍要再向官方核實 [37][43]。

接下來我應該探索哪個相關主題？

繼續“Claude Opus 4.7、GPT-5.5、DeepSeek V4、Kimi K2.6：2026 Benchmark 點睇先唔會睇錯”以獲得另一個角度和額外的引用。

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我應該將其與什麼進行比較？

對照「DeepSeek V4 唔止 1M context：MoE、長上下文同 API 遷移重點」交叉檢查此答案。

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繼續你的研究

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 K2.6：2026 Benchmark 點睇先唔會睇錯

Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6：2026 Benchmark 實證比較

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 唔止 1M context：MoE、長上下文同 API 遷移重點

DeepSeek V4 唔止 1M context：MoE、長上下文同 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：替代性越高，點解係 larger; larger？

Northwest 與 Southeast Timber：替代性高，點解兩格都填 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 會唔會打低 OpenAI、Claude、Gemini、Grok？真正關鍵係成本同信任

DeepSeek 會唔會打低 OpenAI、Claude、Gemini、Grok？

研究對話

你

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 來源

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...

  • [30] Models & Pricing - DeepSeek API Docsapi-docs.deepseek.com

    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...

  • [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...

  • [37] Kimi K2.6 Tech Blog: Advancing Open-Source Codingkimi.com

    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.

  • [41] MoonshotAI: Kimi K2.6 Reviewdesignforonline.com

    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 ...

  • [45] Moonshot AI Unveils Kimi K2.6, an Open-Weight Model Built for ...linkedin.com

    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