所有评测

Multimodal Knowledge Agent (MKA) Benchmark

Artificial Analysis' implementation of Google DeepMind's Multimodal Knowledge Agent (MKA) benchmark. It tests whether agents can read visually dense project files, such as architectural drawings, engineering schematics and clinical scans, and produce the deliverables a professional would.
查看示例任务

MKA Climb WSR

Claude Fable 5.2 (Max, Default Fallback)在 MKA Climb WSR 中得分最高,为 57%;其次是Gemini 4 Carbon (High),得分 54%和Claude Opus 5.5 (Max, Default Fallback),得分 53%

每项任务成本

GPT-6 Luna (Max)在 MKA 上的每项任务成本最低,为 $0.18;其次是Claude Haiku 5.5 (Max)($0.32)和MiMo-V2.6-Pro($0.32)

使用的输出 Token

GPT-6 Astra (Max)运行 MKA 使用的输出 Token 最少,为 8.39M;其次是GPT-6.1 Sol (Max)(9.23M)和GPT-6.1 Terra (Max)(9.43M)

得分

MKA: Climb WSR

每项任务中已满足的 Climb 量表标准的加权占比均值
No publicly available API

MKA:Climb WSR vs. 每项任务成本

MKA Climb WSR vs. average cost per task (USD)
Most attractive quadrant
Pareto line

示例任务

行业
Construction
角色
Architect

Create ‘Architectural_Section_Elevation_Opening_Coordination_Audit.pptx’, an exception-only coordination audit of the five floor plans against Sections A-A and B-B and the four elevations. Base the analysis only on the supplied drawings, and prepare the report in accordance with the attached drawing coordination review standard.

On every floor, audit the content cut by each section against the plan, the content visible beyond each cut plane, and every exterior door and window opening, including its representation on the elevations, its floor band and its written width. Begin with a building summary showing each floor’s overall status and separate Mismatch and Cannot Determine counts, then present only Mismatch and Cannot Determine findings, organized by floor and supported by crops from the relevant drawings. Limit the report to 10 slides.

输入文件(12)

打开文件

模型输出

各模型在一次运行中提交的文件。Office 文件以 PDF 形式展示。

行业结果

MKA:各行业 Climb WSR

每项任务中已满足的 Climb 量表标准的加权占比均值,按行业划分 · 颜色在每个行业内根据所有已测试模型的分数进行归一化

输出文件类型结果

MKA:各输出文件类型 Climb WSR

每项任务中已满足的 Climb 量表标准的加权占比均值,按各标准所评分的输出划分 · 颜色在每种输出文件类型内根据所有已测试模型的分数进行归一化

成本

成本仅包含对被测模型的调用,不包括评审调用。

MKA:每项任务成本

每项任务的平均成本(美元),按输入、缓存命中、缓存写入、推理和回答 Token 划分
No publicly available API

Token 使用量

MKA:每项任务的输出 Token

运行一项任务使用的输出 Token,按推理和回答 Token 划分
No publicly available API

速度

MKA:每项任务耗时

每项任务的估算总耗时(分钟):答案和推理生成时间加上工具执行时间 · Lower is better
No publicly available API

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