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Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6：证据优先对比

现有公开资料不足以证明某个模型是绝对总冠军；Claude Opus 4.7的一手资料最完整，DeepSeek V4的价格和输出规格最清楚。 GPT 5.5已由OpenAI API文档和发布页确认，但可见官方资料缺少完整价格、上下文长度、输出上限和基准细节。

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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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把 Claude Opus 4.7、GPT-5.5、DeepSeek V4 和 Kimi K2.6 放在一起，很容易变成一场谁第一的排行榜。但对真正要接 API、做预算或上线 AI Agent（智能体）的团队来说，更实用的问题是：哪些结论有官方文档支撑，哪些只是第三方线索。

公开证据并不均衡。Anthropic 对 Claude Opus 4.7给出的官方说明最完整，包括 1M 上下文窗口，以及 1M 上下文按标准 API 价格计费、没有长上下文溢价的说明 [1][3]。DeepSeek 的价格页给出了最具体的规格和价格行，包括 1M 上下文、384K 最大输出、工具调用、JSON 输出和分档 token 价格 [30]。OpenAI 已在 API 文档和发布页中确认 GPT-5.5，但本文可见的官方资料还不足以完整比较价格、上下文、输出上限和基准表现 [13][22]。Moonshot AI 对 Kimi K2.6的官方定位集中在多模态、编码和 Agent 表现，但很多具体技术与商业细节仍来自第三方或用户生成页面 [37][38][41][42][43][45]。

先给结论

  • 不要急着封总冠军。 现有材料不是同一套可横向对比的成绩单：Vellum 可见内容列出 Claude Opus 4.7的基准类别但没有显示具体分数，OpenAI 的 GPT-5.5发布页提到评测但可见材料没有给出数字，Hugging Face 称 DeepSeek V4 基准有竞争力但并非 SOTA，Kimi 官方博客则建议用官方 API 复现 Kimi-K2.6基准结果 [4][22][32][37]。
  • Claude Opus 4.7的一手证据最强。 Anthropic 将其描述为面向编码和 AI Agent 的混合推理模型，具备 1M 上下文窗口；其文档还说明 1M 上下文按标准 API 价格提供，不收长上下文溢价 [1][3]。
  • DeepSeek V4的成本证据最清楚。 DeepSeek 价格页直接列出 cache-hit、cache-miss 和输出 token 价格，同时给出 1M 上下文、384K 最大输出、JSON 输出和工具调用等规格 [30]。
  • GPT-5.5已经确认，但可见官方信息偏少。 OpenAI API 文档列出 gpt-5.5 和 gpt-5.5-2026-04-23，发布页也说明 GPT-5.5和 GPT-5.5 Pro 在 2026年4月24日更新后可通过 API 使用；但这些可见材料还不足以支持全维度排名 [13][22]。
  • Kimi K2.6方向明确，但细节需要复核。 Moonshot AI 页面强调 K2.6的原生多模态、编码能力和 Agent 表现；Kimi 博客则建议通过官方 API 复现官方基准结果 [37][43]。

一眼看懂：证据强弱对比

模型证据最扎实的事实主要不确定性
Claude Opus 4.7Anthropic 称其为面向编码和 AI Agent 的混合推理模型，具备 1M 上下文窗口；官方文档称 1M 上下文按标准 API 价格提供、无长上下文溢价 [1][3]。Vellum 可见内容列出编码、Agent、金融、推理、多模态、搜索和安全等基准类别，但缺少可直接横向排名的具体分数；128K 输出和每百万 token $5/$25 的说法主要来自第三方资料 [4][5]。
GPT-5.5OpenAI API 文档列出 gpt-5.5 和 gpt-5.5-2026-04-23，标记为 long context，并显示分层速率限制；OpenAI 发布页称 GPT-5.5和 GPT-5.5 Pro 已在 API 中可用 [13][22]。可见官方材料没有给出精确上下文长度、输出上限、价格、模态细节或基准数字。第三方页面补充了部分数字，但可信度低于 OpenAI 官方文档 [14][20][21]。
DeepSeek V4DeepSeek 价格页显示 1M 上下文、384K 最大输出、JSON 输出、工具调用、Chat Prefix Completion（Beta）、FIM Completion（Beta）和明确 token 价格行 [30]。Hugging Face 称 DeepSeek 发布了 V4 Pro 和 V4 Flash 两个 checkpoint，二者均为 1M token 上下文 [32]。V4 Flash/Pro 命名、架构参数等细节在第三方汇总中更清楚；Hugging Face 对基准的概括是有竞争力但不是 SOTA [27][32]。
Kimi K2.6Moonshot AI 页面称 K2.6是原生多模态模型，强调编码能力和 Agent 表现；Kimi 博客称复现官方 Kimi-K2.6基准应使用官方 API [37][43]。精确上下文长度、输出长度、价格和开放权重状态，在本文资料中更多来自第三方或用户生成页面，而非完整官方规格表 [38][41][42][45]。

Claude Opus 4.7：官方资料最完整

在这组材料里，Claude Opus 4.7的主线最清楚。Anthropic 将其描述为推动编码和 AI Agent 前沿的混合推理模型，并在产品页写明其具备 1M 上下文窗口 [3]。同一页面还称，Opus 4.7在编码、视觉和复杂多步骤任务上表现更强，在专业知识工作上也有更好结果 [3]。

最醒目的差异点是长上下文。Anthropic 文档明确称，Claude Opus 4.7提供 1M 上下文窗口，并按标准 API 价格计费，没有长上下文溢价 [1]。这对需要处理大代码库、长文档、合同包、研究材料或多文件工作流的团队尤其关键，因为长上下文不只是能力问题，也直接影响预算。

Anthropic 文档还提到，Opus 4.7在知识工作任务上有明显提升，尤其是需要模型视觉核验自身输出的场景，例如 .docx 修订、.pptx 编辑、图表分析和图形分析 [1]。

第三方资料可以作为线索，但不宜当作最终采购依据。Caylent 称 Opus 4.7最高支持 128K 输出 token，并沿用 Opus 标准价格，即每百万输入 token $5、每百万输出 token $25 [5]。这对预算测算有参考价值，但本文材料中最强的一手价格结论，仍是 Anthropic 关于 1M 上下文无额外溢价的说明 [1]。

基准方面要谨慎。Vellum 的 Claude Opus 4.7文章列出了编码、Agent、金融、推理、多模态与视觉、搜索、安全等基准类别，但可见内容没有提供足以和 GPT-5.5、DeepSeek V4、Kimi K2.6直接排名的具体分数 [4]。

GPT-5.5：模型已确认，但可见官方规格不够完整

GPT-5.5并不是传闻。OpenAI API 文档列出了 gpt-5.5 和带日期的 gpt-5.5-2026-04-23，并标记为 long context，同时展示了按使用层级划分的速率限制信息 [13]。OpenAI 的发布页日期为 2026年4月23日，并在 2026年4月24日更新中说明 GPT-5.5和 GPT-5.5 Pro 已可在 API 中使用 [22]。

问题在于，确认存在不等于足够比较。本文可见的 OpenAI 官方材料没有给出精确上下文大小、最大输出、API 价格、模态细节、编码表现、延迟或可横向比较的基准分数 [13][22]。

第三方页面补上了一些数字，但只能算待核验线索。DesignForOnline 称 GPT-5.5价格为每百万输入 token $5、每百万输出 token $30 [14]。LLM Stats 称其 API 上下文为 1M 输入和 128K 输出，并支持文本与图像输入、文本输出 [20][21]。这些信息适合放进供应商核验清单，但不能替代 OpenAI 官方规格。

实际选型上，如果你的产品已经深度依赖 OpenAI API、权限体系、监控和工具链，GPT-5.5值得优先做兼容性测试。但仅凭本文这些官方材料，还不能负责任地说它在基准、价格或 Agent 能力上压过另外三款模型 [13][22]。

DeepSeek V4：价格和输出规格最可落地

DeepSeek 在成本侧给出的信息最具体。其 API 价格页显示 1M 上下文长度、384K 最大输出、JSON 输出、工具调用、Chat Prefix Completion（Beta）和 FIM Completion（Beta）[30]。

价格页还列出了多组 token 价格：cache-hit 输入包括每百万 token $0.028 和 $0.03625，cache-miss 输入包括 $0.14 和 $0.435，输出包括 $0.28 和 $0.87；可见内容中还带有限时 75% 折扣和划线的非折扣价提示 [30]。这意味着，做预算时不能只看一个平均单价，尤其要区分缓存命中和未命中的成本。

V4 具体版本的信息则部分依赖第三方汇总。EvoLink 称截至 2026年4月24日，DeepSeek 官方 API 文档已列出 deepseek-v4-flash 和 deepseek-v4-pro，发布官方价格，并记录 1M 上下文与 384K 最大输出 [27]。Hugging Face 称 DeepSeek 发布了两个 MoE checkpoint：DeepSeek-V4-Pro 为 1.6T 总参数、49B 激活参数，DeepSeek-V4-Flash 为 284B 总参数、13B 激活参数；二者都有 1M token 上下文窗口 [32]。Hugging Face 同时评价其基准数字有竞争力，但不是 SOTA [32]。

OpenRouter 的 V4 Pro 页面则单独列出 1,048,576 token 上下文窗口，以及每百万输入 token $0.435、每百万输出 token $0.87 的价格 [31]。这有助于交叉验证商业图景，但考虑到 DeepSeek 自身价格页包含限时折扣提示，生产前仍应直接复核当前价格 [30][31]。

实用判断是：如果你的第一筛选条件是成本、长上下文、大输出、JSON 输出或工具调用，DeepSeek V4很值得尽早测试。但它并不会自动赢在质量、可靠性、安全、延迟或工具调用成功率上，这些仍要用自己的工作负载实测。

Kimi K2.6：定位亮眼，但规格证据还不够硬

Kimi K2.6的方向很清晰：Moonshot AI 页面称 K2.6是原生多模态模型，强调强编码能力和 Agent 表现 [43]。Kimi 技术博客的可见内容还写明，若要复现官方 Kimi-K2.6基准结果，建议使用官方 API；第三方提供商则应参考 Kimi Vendor Verifier（KVV）[37]。

但本文材料中，许多具体数字主要来自第三方。LLM Stats 称 Kimi K2.6输入上下文窗口为 262,144 token，并最多可生成 262,144 token 输出 [42]。DesignForOnline 称 Kimi K2.6具备 262K 上下文、视觉、工具使用、函数调用，价格从每百万 token $0.7500 起 [41]。Atlas Cloud 列出的 Kimi K2.6 API 价格则从每百万 token $0.95 起 [38]。另有 LinkedIn 文章称 Kimi K2.6为开放权重模型，但这属于用户生成证据，在 Moonshot 直接确认许可条款前应按低置信度处理 [45]。

因此，Kimi K2.6适合进入多模态编码、Agent 工作流和第三方推理服务的候选名单；但在生产决策前，应向 Moonshot 或官方 API 来源核验许可证、上下文长度、输出上限、价格、基准方法和服务商兼容性 [37][43]。

为什么现在不该颁发基准王冠

单一排行榜在这里很容易误导。Vellum 可见内容列出了 Claude Opus 4.7的基准领域，但没有显示直接排名所需的具体分数 [4]。OpenAI 的 GPT-5.5发布页结构中包含评测部分，但可见材料没有展示数字 [22]。Hugging Face 对 DeepSeek V4的说法是基准有竞争力但非 SOTA [32]。Kimi 官方博客则指向通过官方 API 复现 Kimi-K2.6基准结果，而不是在可见材料中直接给出所有结果 [37]。

更重要的是，模型排名会随任务变化而翻盘。写代码、长上下文检索、多模态文档分析、工具调用稳定性、Agent 规划、延迟，以及缓存命中与未命中情况下的总成本，都是不同测试。没有同一套数据和同一套评分标准，就说某个模型通吃，只是营销话术，不是证据。

该先测谁？

  • 先测 Claude Opus 4.7：如果你看重官方明确记录的 1M 上下文、编码、AI Agent、视觉、复杂多步骤任务和知识工作提升 [1][3]。
  • 先测 GPT-5.5：如果你的应用已经搭在 OpenAI 基础设施上，当前重点是验证 gpt-5.5 的 API 路径和迁移成本 [13][22]。
  • 先测 DeepSeek V4：如果你的第一筛选项是价格、长上下文、最大输出、JSON 输出或工具调用；DeepSeek 价格页是本文中最具体的成本来源 [30]。
  • 先测 Kimi K2.6：如果你优先关注 Moonshot AI 的多模态、编码和 Agent 方向，同时愿意单独核验上下文、价格、输出、许可和服务商细节 [37][38][41][42][43][45]。

更稳妥的评测办法

真正的生产选型，建议做任务级 bake-off，而不是只看大而全的排行榜。把四个候选模型放在同一套提示词、工具、上下文长度、文件输入和评分规则下，至少记录五项指标：任务成功率、工具调用可靠性、长上下文准确性、延迟，以及包含缓存策略后的 token 总成本。

对 DeepSeek，要把 cache-hit 和 cache-miss 成本分开算，因为价格页明确拆分了这些行 [30]。对 GPT-5.5，要把 OpenAI 已确认的信息和第三方上下文、价格说法分开记录，等官方文档补齐后再合并判断 [13][14][20][21][22]。对 Kimi K2.6，要把服务商页面和用户生成的开放权重说法视为待核验线索，而不是最终采购证据 [37][38][41][42][45]。

最终判断

只看证据、不看热闹，Claude Opus 4.7是这次对比中官方资料最清楚的旗舰模型，尤其是在 1M 上下文、编码、AI Agent 和知识工作相关主张上 [1][3]。DeepSeek V4拥有最强的价格证据，并且长上下文和大输出规格也有较明确支撑，但 V4 Flash/Pro 的部分架构和命名细节仍更多依赖第三方汇总 [27][30][32]。GPT-5.5已由 OpenAI 自家 API 和发布材料确认，只是可见官方信息还太少，不足以完整比较表现 [13][22]。Kimi K2.6在多模态、编码和 Agent 方向有可信的官方定位，但许多精确技术与商业主张仍需要更强的一手确认 [37][38][41][42][43][45]。

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要点

  • 现有公开资料不足以证明某个模型是绝对总冠军；Claude Opus 4.7的一手资料最完整，DeepSeek V4的价格和输出规格最清楚。
  • GPT 5.5已由OpenAI API文档和发布页确认，但可见官方资料缺少完整价格、上下文长度、输出上限和基准细节。
  • Kimi K2.6在多模态、编码和Agent方向值得关注，但上下文、价格和开放权重等具体说法仍需以Moonshot官方API或文档核验。

人们还问

“Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 vs Kimi K2.6：证据优先对比”的简短答案是什么？

现有公开资料不足以证明某个模型是绝对总冠军；Claude Opus 4.7的一手资料最完整，DeepSeek V4的价格和输出规格最清楚。

首先要验证的关键点是什么？

现有公开资料不足以证明某个模型是绝对总冠军；Claude Opus 4.7的一手资料最完整，DeepSeek V4的价格和输出规格最清楚。 GPT 5.5已由OpenAI API文档和发布页确认，但可见官方资料缺少完整价格、上下文长度、输出上限和基准细节。

接下来在实践中我应该做什么？

Kimi K2.6在多模态、编码和Agent方向值得关注，但上下文、价格和开放权重等具体说法仍需以Moonshot官方API或文档核验。

接下来我应该探索哪个相关主题？

继续“Claude Opus 4.7、GPT-5.5、DeepSeek V4 与 Kimi K2.6：2026 基准对比与选型结论”以获得另一个角度和额外的引用。

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我应该将其与什么进行比较？

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研究对话

你

研究问题

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

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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.

来源

  • [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...

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    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...

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    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

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    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