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GPT-5.5 vs Claude Opus 4.7 vs DeepSeek V4 vs Kimi K2.6：邊個最值得用？

整體 Intelligence Index 方面，Artificial Analysis 列 GPT 5.5 xhigh 60、GPT 5.5 high 59，領先 Claude Opus 4.7 Adaptive Reasoning Max Effort 的 57。[2] 共享 benchmark 係分庭抗禮：Claude Opus 4.7 贏 GPQA Diamond、HLE no tools、SWE Bench Pro、MCP Atlas；GPT 5.5／GPT 5.5 Pro 贏 Terminal Bench 2.0、BrowseComp 同 HLE with tools（Pro）。[16] 成本敏感就要睇 DeepS...

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Editorial illustration comparing GPT-5.5, Claude Opus 4.7, DeepSeek V4, and Kimi K2.6 AI models
GPT-5.5 vs Claude Opus 4.7 vs DeepSeek V4 vs Kimi K2.6: Benchmarks, Pricing, and Best Use CasesA practical comparison of leading AI models depends on the benchmark, variant, reasoning setting, and API price.
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Create a landscape editorial hero image for this Studio Global article: GPT-5.5 vs Claude Opus 4.7 vs DeepSeek V4 vs Kimi K2.6: Benchmarks, Pricing, and Best Use Cases. Article summary: There is no universal winner: GPT 5.5 leads the available Artificial Analysis Intelligence Index at 60/59, Claude Opus 4.7 wins several shared VentureBeat reasoning and SWE rows, and DeepSeek V4 is the price value out.... Topic tags: ai, llm, ai benchmarks, 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://ww

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比較前線 AI 模型，最易中伏嘅位係將單一 benchmark 當成「總冠軍」。比較穩陣嘅讀法係：GPT-5.5 有最強整體排名信號，Claude Opus 4.7 喺多個硬推理同軟件工程項目跑出，DeepSeek V4 嘅 API 成本優勢最清楚，而 Kimi K2.6 喺 coding 同 agentic 工作上值得留意，但同 GPT-5.5、Opus 4.7 直接對打嘅證據較少。[2][16][15][18][19]

快速結論

你最重視…較有根據嘅選擇點解
整體智能排名信號GPT-5.5Artificial Analysis 將 GPT-5.5 xhigh 列為 60、GPT-5.5 high 列為 59，高過 Claude Opus 4.7 Adaptive Reasoning Max Effort 的 57。[2]
硬推理、軟件工程Claude Opus 4.7，GPT-5.5 緊隨其後VentureBeat 共享表格入面，Claude 領先 GPQA Diamond、HLE no-tools、SWE-Bench Pro、MCP Atlas；GPT-5.5 贏 Terminal-Bench 2.0 同基本 BrowseComp，GPT-5.5 Pro 則喺有列出嘅 HLE with tools 同 BrowseComp 領先。[16]
旗艦級 API 成本DeepSeek V4Mashable 列 DeepSeek V4 為每 100 萬 input tokens $1.74、output tokens $3.48，低過 GPT-5.5 的 $5／$30 同 Claude Opus 4.7 的 $5／$25。[15]
已披露 coding／競賽編程數據DeepSeek V4 ProTogether AI 列 DeepSeek V4 Pro 為 93.5% LiveCodeBench、Codeforces 3206、80.6% SWE-Bench Verified、76.2% SWE-Bench Multilingual。[25]
Kimi K2.6 評估有潛力，但未算定案Kimi K2.6 有 coding 同 agentic 數據，但現有 Kimi 相關證據多數係同 GPT-5.4、Claude Opus 4.6 比，而唔係直接對 GPT-5.5、Claude Opus 4.7。[18][19]

整體排名：GPT-5.5 佔上風

現有來源入面，最乾淨嘅整體信號來自 Artificial Analysis。佢列 GPT-5.5 xhigh 的 Intelligence Index 為 60、GPT-5.5 high 為 59；Claude Opus 4.7 Adaptive Reasoning Max Effort 則為 57。[2]

Kimi K2.6 喺可見嘅綜合指標片段入面低過呢個 GPT-5.5／Claude 梯隊。OpenRouter 列 Kimi K2.6 為 53.9 Intelligence、47.1 Coding、66.0 Agentic；LLMBase 的 DeepSeek V4 Flash High vs Kimi K2.6 比較亦列 Kimi 為 53.9 Intelligence、47.1 Coding。[3][1] 同一個 LLMBase 比較列 DeepSeek V4 Flash High 為 44.9 Intelligence、39.8 Coding，但要留意，呢個係 Flash 版本，唔係 DeepSeek V4 Pro 或 Pro-Max。[1]

重點係：整體排名對 GPT-5.5 vs Claude Opus 4.7 有清晰信號，但目前並無一條完整、同場四方 leaderboard，同時列出 GPT-5.5、Claude Opus 4.7、DeepSeek V4 Pro-Max 同 Kimi K2.6。[2]

同場 benchmark：Claude 同 GPT-5.5 各有勝負

VentureBeat 的共享 benchmark 表，係現有資料中較適合用來比較 DeepSeek-V4-Pro-Max、GPT-5.5、部分列出的 GPT-5.5 Pro，以及 Claude Opus 4.7 的來源。[16]

BenchmarkDeepSeek-V4-Pro-MaxGPT-5.5GPT-5.5 Pro（有列出時）Claude Opus 4.7呢份來源最高結果
GPQA Diamond90.1%93.6%—94.2%Claude Opus 4.7[16]
Humanity’s Last Exam，no tools37.7%41.4%43.1%46.9%Claude Opus 4.7[16]
Humanity’s Last Exam，with tools48.2%52.2%57.2%54.7%GPT-5.5 Pro[16]
Terminal-Bench 2.067.9%82.7%—69.4%GPT-5.5[16]
SWE-Bench Pro／SWE Pro55.4%58.6%—64.3%Claude Opus 4.7[16]
BrowseComp83.4%84.4%90.1%79.3%GPT-5.5 Pro[16]
MCP Atlas／MCPAtlas Public73.6%75.3%—79.1%Claude Opus 4.7[16]

所以，唔係一面倒。Claude Opus 4.7 喺 GPQA Diamond、HLE no-tools、SWE-Bench Pro 同 MCP Atlas 較有說服力；GPT-5.5 基本版則喺 Terminal-Bench 2.0 同 BrowseComp 表現較強，而 GPT-5.5 Pro 喺 VentureBeat 有列出嘅 HLE with tools 同 BrowseComp 最高。[16]

DeepSeek-V4-Pro-Max 喺幾項都算貼近，但喺呢張共享表入面，未有一項贏過 GPT-5.5 或 Claude Opus 4.7 的最佳結果。最接近係 BrowseComp：DeepSeek-V4-Pro-Max 83.4%，GPT-5.5 84.4%，Claude Opus 4.7 79.3%。[16]

Coding：要睇你寫緊咩 code

如果係 repository 層面嘅軟件工程任務，Claude Opus 4.7 喺 VentureBeat 共享表的 SWE-Bench Pro 結果最強：64.3%，高過 GPT-5.5 的 58.6% 同 DeepSeek-V4-Pro-Max 的 55.4%。[16]

不過 DeepSeek V4 Pro 有現有來源入面最完整嘅 coding 指標披露。Together AI 列 DeepSeek V4 Pro 為 93.5% LiveCodeBench、Codeforces 3206、80.6% SWE-Bench Verified、76.2% SWE-Bench Multilingual。[25] NVIDIA 的 model card 亦按 DeepSeek V4 Flash、V4 Pro 等版本拆出 GPQA Diamond、HLE、LiveCodeBench、Codeforces 等 benchmark，其中 V4-Pro Max 顯示 LiveCodeBench 93.5、Codeforces 3206。[31]

Kimi K2.6 亦有值得留意嘅 coding 證據，但最強嘅 Kimi 相關表格多數係同上一代或較早競爭對手比較。Lorka 列 Kimi K2.6 為 58.6% SWE-Bench Pro、54.0% HLE-Full with tools、90.5% GPQA-Diamond、79.4% MMMU-Pro，表格比較對象包括 GPT-5.4、Claude Opus 4.6、Gemini 3.1 Pro。[18] Verdent 則列 Kimi K2.6 為 80.2% SWE-Bench Verified、66.7% Terminal-Bench 2.0、54.0% HLE with tools、89.6% LiveCodeBench v6，並提到 Opus 4.7 喺 SWE-Bench Verified 以 87.6% 領先。[19]

實際結論係：Kimi K2.6 值得放入 coding 同 agent workflow 測試名單，但現有證據未足以話佢喺整體上擊敗 GPT-5.5 或 Claude Opus 4.7。[18][19]

收費：DeepSeek V4 成本優勢最清楚

如果 API 成本係核心考慮，DeepSeek V4 係最有價格說服力嗰個。Mashable 列 DeepSeek V4 為每 100 萬 input tokens $1.74、每 100 萬 output tokens $3.48；GPT-5.5 為 $5／$30，Claude Opus 4.7 為 $5／$25。[15]

模型／版本列出 input 價格列出 output 價格備註
GPT-5.5每 100 萬 tokens $5每 100 萬 tokens $30Mashable 喺呢個比較列出 1M context window。[15]
Claude Opus 4.7每 100 萬 tokens $5每 100 萬 tokens $25Mashable 喺呢個比較列出 1M context window。[15]
DeepSeek V4每 100 萬 tokens $1.74每 100 萬 tokens $3.48Mashable 喺呢個比較列出 1M context window。[15]
DeepSeek V4 Flash每 100 萬 tokens $0.14每 100 萬 tokens $0.28LLMBase 喺 DeepSeek V4 Flash High vs Kimi K2.6 比較列 blended price 為 $0.18。[1]
Kimi K2.6每 100 萬 tokens $0.95每 100 萬 tokens $4.00LLMBase 喺同一比較列 blended price 為 $1.71。[1]

但唔好假設所有 endpoint 都有同一個 context limit。Mashable 喺收費比較中列 DeepSeek V4、GPT-5.5、Claude Opus 4.7 都係 1M context window；但 OpenRouter 的 DeepSeek V4 Pro listing 顯示 256K max tokens、66K max output tokens。[15][3] 真正落 production 前，要核實你實際會 call 嘅 provider、模型版本同 reasoning mode。

逐個模型點用

GPT-5.5：想要最穩陣整體排名，佢係預設選擇

如果你嘅決策主要睇現有整體排名，GPT-5.5 係較安全嘅 default。Artificial Analysis 列 GPT-5.5 xhigh 為 60、GPT-5.5 high 為 59，係提供片段入面 Intelligence Index 的頭兩位。[2]

佢喺 VentureBeat 共享表亦有兩個突出項目：Terminal-Bench 2.0 為 82.7%，基本 GPT-5.5 的 BrowseComp 為 84.4%；而 GPT-5.5 Pro 喺有列出時 BrowseComp 為 90.1%。[16]

Claude Opus 4.7：硬推理同軟件工程多項任務更合拍

Claude Opus 4.7 喺整體排名緊貼 GPT-5.5：Artificial Analysis 將 Claude Opus 4.7 Adaptive Reasoning Max Effort 的 Intelligence Index 列為 57。[2] 喺 VentureBeat 共享表，佢喺 GPQA Diamond、HLE no-tools、SWE-Bench Pro、MCP Atlas 都領先 GPT-5.5 同 DeepSeek-V4-Pro-Max。[16]

Anthropic 自家發布資料亦提到內部 research-agent benchmark：Claude Opus 4.7 喺六個 module 的整體分數並列第一，為 0.715；General Finance 分數為 0.813，高過 Opus 4.6 的 0.767。[17] 不過呢類屬於廠方內部 benchmark，應視為補充背景，而唔係中立 leaderboard 證據。[17]

DeepSeek V4：如果版本配合工作，性價比最突出

DeepSeek V4 最明顯嘅優勢係價錢。Mashable 比較中，佢每 100 萬 input／output tokens 價格係 $1.74／$3.48，明顯低過 GPT-5.5 的 $5／$30 同 Claude Opus 4.7 的 $5／$25。[15]

DeepSeek V4 Pro 亦有強 coding 指標，包括 Together AI 列出嘅 93.5% LiveCodeBench、Codeforces 3206、80.6% SWE-Bench Verified、76.2% SWE-Bench Multilingual。[25] 取捨係：喺 VentureBeat 共享表，DeepSeek-V4-Pro-Max 即使部分項目接近，例如 BrowseComp，都仍然落後於 GPT-5.5 或 Claude Opus 4.7 的最佳結果。[16]

Kimi K2.6：coding／agent 值得試，但四方比較未夠實

Kimi K2.6 最難下定論，因為現有 Kimi-focused benchmark 多數係同 GPT-5.4、Claude Opus 4.6 比，而唔係直接同 GPT-5.5、Claude Opus 4.7 比。[18][19] 但佢嘅信號唔弱：OpenRouter 列 Kimi K2.6 為 53.9 Intelligence、47.1 Coding、66.0 Agentic；Verdent 則列 80.2% SWE-Bench Verified 同 89.6% LiveCodeBench v6。[3][19]

所以唔應該理解成 Kimi K2.6「唔掂」。更準確係：直接證據較薄。如果 Kimi 嘅價格、部署路線或者 agentic 行為啱你現有 stack，佢值得做實測；但就現有資料而言，未足以稱佢為對 GPT-5.5 或 Claude Opus 4.7 的整體勝者。[18][19]

揀之前要留意

  • 版本名好重要。 DeepSeek V4 喺來源中有 V4、V4 Flash、V4 Pro、DeepSeek-V4-Pro-Max 等叫法；價格、限制同 benchmark 結果會因版本同 reasoning setting 而變。[1][15][25][31]
  • Kimi 比較無咁直接。 現有較強嘅 Kimi K2.6 benchmark 表，經常係同 GPT-5.4、Claude Opus 4.6 比，而唔係 GPT-5.5、Claude Opus 4.7。[18][19]
  • Humanity’s Last Exam no-tools 數字有不一致。 LLM Stats 同 VentureBeat 報 GPT-5.5 41.4%、Claude Opus 4.7 46.9%；但 Mashable 的 GPT vs Claude 片段報 GPT-5.5 40.6%、Opus 4.7 31.2%。[7][16][9]
  • 內部 benchmark 唔等於獨立 leaderboard。 Anthropic 的 Opus 4.7 發布文章有內部 research-agent 成績，但閱讀時應同跨供應商公開比較分開看。[17]
  • 價格同 context limit 會因 provider 而變。 同一模型家族喺唔同 endpoint 可能有唔同 context window、token limit 同 output cap。[3][15]

底線

如果你最重視現有整體智能排名，揀 GPT-5.5 較有根據。[2] 如果你嘅工作似 GPQA Diamond、HLE no-tools、SWE-Bench Pro、MCP Atlas 呢類硬推理或軟件工程項目，Claude Opus 4.7 更有說服力。[16] 如果你重視 price-performance，而且可以驗證自己要用嘅 V4 版本，DeepSeek V4 嘅 API 價格明顯低過 GPT-5.5 同 Claude Opus 4.7，DeepSeek V4 Pro 亦有強 coding 指標。[15][25] 至於 Kimi K2.6，應視為有潛力嘅 coding／agentic 候選，但以現有直接證據，未足以稱為整體擊敗 GPT-5.5 或 Claude Opus 4.7。[18][19]

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重點

  • 整體 Intelligence Index 方面，Artificial Analysis 列 GPT 5.5 xhigh 60、GPT 5.5 high 59，領先 Claude Opus 4.7 Adaptive Reasoning Max Effort 的 57。[2]
  • 共享 benchmark 係分庭抗禮：Claude Opus 4.7 贏 GPQA Diamond、HLE no tools、SWE Bench Pro、MCP Atlas；GPT 5.5／GPT 5.5 Pro 贏 Terminal Bench 2.0、BrowseComp 同 HLE with tools（Pro）。[16]
  • 成本敏感就要睇 DeepSeek V4：Mashable 列出 DeepSeek V4 每 100 萬 input／output tokens 為 $1.74／$3.48，低過 GPT 5.5 的 $5／$30 同 Claude Opus 4.7 的 $5／$25。[15]

人們還問

「GPT-5.5 vs Claude Opus 4.7 vs DeepSeek V4 vs Kimi K2.6：邊個最值得用？」的簡短答案是什麼？

整體 Intelligence Index 方面，Artificial Analysis 列 GPT 5.5 xhigh 60、GPT 5.5 high 59，領先 Claude Opus 4.7 Adaptive Reasoning Max Effort 的 57。[2]

首先要驗證的關鍵點是什麼？

整體 Intelligence Index 方面，Artificial Analysis 列 GPT 5.5 xhigh 60、GPT 5.5 high 59，領先 Claude Opus 4.7 Adaptive Reasoning Max Effort 的 57。[2] 共享 benchmark 係分庭抗禮：Claude Opus 4.7 贏 GPQA Diamond、HLE no tools、SWE Bench Pro、MCP Atlas；GPT 5.5／GPT 5.5 Pro 贏 Terminal Bench 2.0、BrowseComp 同 HLE with tools（Pro）。[16]

接下來在實務上我該做什麼？

成本敏感就要睇 DeepSeek V4：Mashable 列出 DeepSeek V4 每 100 萬 input／output tokens 為 $1.74／$3.48，低過 GPT 5.5 的 $5／$30 同 Claude Opus 4.7 的 $5／$25。[15]

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。

開啟相關頁面

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 benchmarks of Claude Opus 4.7, GPT-5.5, DeepSeek V4, Kimi K2.6 and compare as comprehensively as possible

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引用的答案

Using only the supplied evidence, the strongest benchmark signal is that GPT-5.5 leads the available aggregate Intelligence Index, with GPT-5.5 xhigh at 60 and GPT-5.5 high at 59, while Claude Opus 4.7 Adaptive Reasoning Max Effort scores 57.[5] DeepSeek V4 Pro has the richest disclosed task-level benchmark data in the evidence, including 93.5% LiveCodeBench, Codeforces 3206, 80.6% SWE-Bench Verified, and 76.2% SWE-Bench Multilingual.[6] For Kimi K2.6, the supplied evidence contains positioning and pricing but no comparable numeric benchmark scores, so a full apples-to-apples benchmark ranking is not supported.[1][3]

Key findings

  • GPT-5.5 is the best-supported leader on the only cross-model aggregate benchmark available here: GPT-5.5 xhigh ranks first with an Intelligence Index of 60, and GPT-5.5 high ranks second with 59.[5]

  • Claude Opus 4.7 is close behind on that same aggregate measure: Claude Opus 4.7 Adaptive Reasoning Max Effort ranks third with an Intelligence Index of 57.[5]

  • DeepSeek V4 Pro has the clearest disclosed coding/SWE benchmark profile in the supplied evidence: 93.5% LiveCodeBench, Codeforces 3206, 80.6% SWE-Bench Verified, and 76.2% SWE-Bench Multilingual.[6]

  • Kimi K2.6 is described as an “open-source flagship” agent-coding model in the launch/news evidence, but the supplied evidence does not provide numeric benchmark scores for Kimi K2.6.[1]

  • DeepSeek V4 is materially cheaper than GPT-5.5 in the pricing evidence: DeepSeek V4 is listed at $1.74 per 1M input tokens and $3.48 per 1M output tokens, while GPT-5.5 is listed at $5 per 1M input tokens and $30 per 1M output tokens, with both shown as 1M-context models.[4]

  • DeepSeek V4 Flash is far cheaper than Kimi K2.6 in the direct pricing comparison: DeepSeek V4 Flash is listed at $0.14 per 1M input tokens, $0.28 per 1M output tokens, and $0.18 blended, while Kimi K2.6 is listed at $0.95 per 1M input tokens, $4.00 per 1M output tokens, and $1.71 blended.[3]

  • The supplied evidence treats DeepSeek V4 as a family with multiple variants, including V4 Flash and V4 Pro, so comparisons depend on which variant is meant.[2][3][6]

Benchmark and capability comparison

AreaGPT-5.5Claude Opus 4.7DeepSeek V4Kimi K2.6
Aggregate Intelligence Index60 for GPT-5.5 xhigh; 59 for GPT-5.5 high.[5]57 for Claude Opus 4.7 Adaptive Reasoning Max Effort.[5]No numeric Intelligence Index for DeepSeek V4 is provided in the supplied evidence.No numeric Intelligence Index for Kimi K2.6 is provided in the supplied evidence.
Coding / SWE benchmarksNo exact coding benchmark scores are provided in the supplied evidence.The launch evidence says Claude Opus 4.7 has improved programming and a threefold vision upgrade, but no exact benchmark numbers are provided.[1]DeepSeek V4 Pro is listed with 93.5% LiveCodeBench, Codeforces 3206, 80.6% SWE-Bench Verified, and 76.2% SWE-Bench Multilingual.[6]Kimi K2.6 is positioned as an open-source flagship agent-coding model, but no exact benchmark numbers are provided.[1]
Context window evidenceGPT-5.5 is listed with a 1M context window in the pricing comparison.[4]The supplied pricing snippet does not clearly provide Claude Opus 4.7’s context window.DeepSeek V4 is listed with a 1M context window in one comparison, and DeepSeek V4 Flash / Pro are also described as 1M-context options in enterprise guidance.[2][4]No context-window figure for Kimi K2.6 is provided in the supplied evidence.
Pricing evidence$5 per 1M input tokens and $30 per 1M output tokens.[4]$5 per 1M input tokens is visible in the supplied snippet, but the output price is truncated.[4]DeepSeek V4 is listed at $1.74 per 1M input tokens and $3.48 per 1M output tokens; DeepSeek V4 Flash is listed at $0.14 input, $0.28 output, and $0.18 blended.[3][4]Kimi K2.6 is listed at $0.95 per 1M input tokens, $4.00 per 1M output tokens, and $1.71 blended.[3]
Best-supported use case from evidenceHighest aggregate intelligence among the four where evidence is available.[5]High-end reasoning close to GPT-5.5 on the available Intelligence Index.[5]Strongest supplied coding/SWE evidence and strong cost positioning, especially for Flash and Pro variants.[2][3][6]Potentially strong open-source agent-coding positioning, but benchmark evidence is insufficient.[1]

Pricing and value takeaways

  • On the available aggregate benchmark, GPT-5.5 leads Claude Opus 4.7 by 3 points at the xhigh setting and by 2 points at the high setting.[5]

  • On the available API pricing comparison, GPT-5.5 costs about 2.9x DeepSeek V4 for input tokens and about 8.6x DeepSeek V4 for output tokens.[4]

  • In the DeepSeek V4 Flash vs Kimi K2.6 pricing comparison, Kimi K2.6’s blended price of $1.71 per 1M tokens is about 9.5x DeepSeek V4 Flash’s $0.18 blended price.[3]

  • One enterprise-oriented comparison recommends DeepSeek V4 Flash for high-volume text summarization, customer-service classification, and internal knowledge-base Q&A because of low cost and 1M context.[2]

  • The same enterprise-oriented comparison recommends DeepSeek V4 Pro for complex document analysis, cross-department workflows, and automated agents because it is described as lower-cost than closed flagship models while offering high-end open-weight capability and 1M context.[2]

Evidence notes

  • The strongest single benchmark source in the supplied evidence is the Intelligence Index listing, which ranks GPT-5.5 xhigh first at 60, GPT-5.5 high second at 59, and Claude Opus 4.7 Adaptive Reasoning Max Effort third at 57.[5]

  • The strongest task-level benchmark evidence is for DeepSeek V4 Pro, because it includes exact coding and software-engineering metrics rather than only positioning language.[6]

  • The Kimi K2.6 evidence is mostly launch positioning and pricing, not benchmark results.[1][3]

  • A user post describes four one-shot head-to-head experiments comparing Codex on GPT-5.5 with Claude Code on Opus 4.7, but the supplied snippet does not provide the outcomes or benchmark scores.[31]

  • There is conflicting or variant-specific DeepSeek context/pricing evidence: one comparison lists DeepSeek V4 with a 1M context window, while another provider listing for DeepSeek V4 Pro shows 256K max tokens and 66K max output tokens.[4][7]

Limitations / uncertainty

  • Insufficient evidence for a full, comprehensive benchmark comparison across Claude Opus 4.7, GPT-5.5, DeepSeek V4, and Kimi K2.6.

  • Insufficient evidence to rank DeepSeek V4 or Kimi K2.6 against GPT-5.5 and Claude Opus 4.7 on the same aggregate Intelligence Index, because the supplied excerpt only provides numeric Intelligence Index scores for GPT-5.5 and Claude Opus 4.7 among the requested models.[5]

  • Insufficient evidence to compare all four models on the same coding benchmarks, because only DeepSeek V4 Pro has detailed numeric coding/SWE benchmark scores in the supplied evidence.[6]

  • Insufficient evidence to compare multimodal performance across all four models, because the supplied evidence only includes a launch-positioning claim that Claude Opus 4.7 has a threefold vision upgrade and does not provide comparable multimodal benchmark numbers for the others.[1]

Summary

GPT-5.5 has the strongest supplied aggregate benchmark result, with Intelligence Index scores of 60 and 59 depending on reasoning setting.[5] Claude Opus 4.7 is close behind at 57 and is the only other requested model with a numeric aggregate score in the supplied evidence.[5] DeepSeek V4 Pro has the best-supported coding/SWE benchmark profile, while DeepSeek V4 Flash and V4 Pro appear especially strong on cost and long-context enterprise use cases.[2][3][6] Kimi K2.6 may be competitively positioned as an open-source agent-coding model, but the supplied evidence does not include enough numeric benchmark data to verify how it compares against GPT-5.5, Claude Opus 4.7, or DeepSeek V4.[1][3]

來源

  • [1] Compare DeepSeek V4 Flash (Reasoning, High Effort) vs Kimi K2.6 | AI Model Comparisonllmbase.ai

    Metric DeepSeek logo De DeepSeek V4 Flash (Reasoning, High Effort) DeepSeek Kimi logo Ki Kimi K2.6 Kimi --- Pricing per 1M tokens Input Cost $0.14/1M $0.95/1M Output Cost $0.28/1M $4.00/1M Blended (3:1) $0.18/1M $1.71/1M Specifications Organization DeepSeek...

  • [2] DeepSeek V4 Pro (Reasoning, High Effort) vs Kimi K2.6: Model Comparisonartificialanalysis.ai

    What are the top AI models? The top AI models by Intelligence Index are: 1. GPT-5.5 (xhigh) (60), 2. GPT-5.5 (high) (59), 3. Claude Opus 4.7 (Adaptive Reasoning, Max Effort) (57), 4. Gemini 3.1 Pro Preview (57), 5. GPT-5.4 (xhigh) (57). Which is the fastest...

  • [3] DeepSeek V4 Pro vs Kimi K2.6 - AI Model Comparison | OpenRouteropenrouter.ai

    Ready Output will appear here... Pricing Input$0.7448 / M tokens Output$4.655 / M tokens Images– – Features Input Modalities text, image Output Modalities text Quantization int4 Max Tokens (input + output)256K Max Output Tokens 66K Stream cancellation Suppo...

  • [7] GPT-5.5 vs Claude Opus 4.7: Pricing, Speed, Benchmarks - LLM Statsllm-stats.com

    Reasoning & knowledge Benchmark GPT-5.5 Opus 4.7 Lead --- --- GPQA Diamond 93.6% 94.2% Opus +0.6 HLE (no tools) 41.4% 46.9% Opus +5.5 HLE (with tools) 52.2% 54.7% Opus +2.5 The HLE no-tools margin (+5.5pp) is the most informative entry in the table because...

  • [9] OpenAI's GPT-5.5 vs Claude Opus 4.7: Which is better? | Mashablemashable.com

    Thanks for signing up! SWE-Bench Pro: GPT-5.5 scored 58.6; Opus 4.7 scored 64.3 percent Terminal-Bench 2.0: GPT-5.5 scored 82.7 percent; Opus 4.7 scored 69.4 percent Humanity's Last Exam: GPT-5.5 scored 40.6 percent; Opus 4.7 scored 31.2 percent\ Humanity's...

  • [15] DeepSeek V4 is here: How it compares to ChatGPT, Claude, Geminimashable.com

    Here's how the API pricing compares: DeepSeek V4 costs $1.74 per 1 million input tokens and $3.48 per 1 million output tokens (1 million context window) GPT-5.5 costs at $5 per 1 million input tokens and $30 per 1 million output tokens (1 million context wi...

  • [16] DeepSeek-V4 arrives with near state-of-the-art intelligence at 1/6th ...venturebeat.com

    BenchmarkDeepSeek-V4-Pro-MaxGPT-5.5GPT-5.5 Pro, where shownClaude Opus 4.7Best result among these GPQA Diamond90.1%93.6%—94.2%Claude Opus 4.7 Humanity’s Last Exam, no tools37.7%41.4%43.1%46.9%Claude Opus 4.7 Humanity’s Last Exam, with tools48.2%52.2%57.2%54...

  • [17] Introducing Claude Opus 4.7 - Anthropicanthropic.com

    Image 7: logo Based on our internal research-agent benchmark, Claude Opus 4.7 has the strongest efficiency baseline we’ve seen for multi-step work. It tied for the top overall score across our six modules at 0.715 and delivered the most consistent long-cont...

  • [18] Kimi K2.6 Tested: Does It Beat Claude and GPT-5? | Lorka AIlorka.ai

    Benchmark What it tests Kimi K2.6 GPT-5.4 Opus 4.6 Gemini 3.1 Pro --- --- --- HLE-Full (with tools) Agentic reasoning with tool use 54.0% 52.1% 53.0% 51.4% DeepSearchQA (F1) Research retrieval and synthesis 92.5% 78.6% 91.3% 81.9% SWE-Bench Pro Multi-file c...

  • [19] Kimi K2.6 vs Claude Opus 4.6 vs GPT-5.4 - Verdent AIverdent.ai

    Benchmark K2.6 Claude Opus 4.6 GPT-5.4 Notes --- --- SWE-Bench Pro 58.60% 53.40% 57.70% Moonshot in-house harness; SEAL mini-swe-agent puts GPT-5.4 at 59.1%, Opus 4.6 at 51.9% SWE-Bench Verified 80.20% 80.80% 80% Tight cluster; Opus 4.7 now leads at 87.6% T...

  • [25] DeepSeek V4 Pro API - Together AItogether.ai

    Coding & Software Engineering: • 93.5% LiveCodeBench and Codeforces 3206 for competitive and production code generation • 80.6% SWE-Bench Verified for autonomous software engineering across repositories • 76.2% SWE-Bench Multilingual for cross-language soft...

  • [31] deepseek-v4-pro Model by Deepseek-ai | NVIDIA NIM - NVIDIA Buildbuild.nvidia.com

    Benchmark (Metric) V4-Flash Non-Think V4-Flash High V4-Flash Max V4-Pro Non-Think V4-Pro High V4-Pro Max --- --- --- Knowledge & Reasoning MMLU-Pro (EM) 83.0 86.4 86.2 82.9 87.1 87.5 SimpleQA-Verified (Pass@1) 23.1 28.9 34.1 45.0 46.2 57.9 Chinese-SimpleQA...