Artificial Analysis 联合 Google DeepMind 推出 MKA 多模态智能体基准

测试在开源智能体框架 Stirrup 中进行，配备代码执行、网页搜索、网页抓取与图像查看工具，最多 250 轮，并使用 GPT-6 Luna（medium）作为智能体评审。结果上 Claude Fable 5.2（max）以 57% 的 Climb WSR 居首，Gemini 4 Carbon（high）为 54%，Claude Opus 5.5（max）为 53%。

成本差异显著：Claude Fable 5.2 平均每任务 $31.97，约为 Gemini 4 Carbon（$6.29）的 5 倍、GPT-6.1 Sol（$1.11）的 29 倍，而 OpenAI 四款受测模型均处于性价比 Pareto 前沿。没有任何单一模型在全部 10 个行业领先，各垂直领域由 Claude、Gemini 系列分头占据第一。

We’ve partnered with Google DeepMind to launch their Multimodal Knowledge Agent (MKA) evaluation, a new benchmark for agentic knowledge work over complex visual files

@GoogleDeepMind’s MKA is a new benchmark requiring agents to review visually complex project files such as schematics and scans, and produce deliverables like slide decks, spreadsheets, reports, and marked-up images

Multimodal Knowledge Agent (MKA) overview:

➤ 200 private real-world visual tasks across 10 industries: Agents work from dense visual project files including architectural drawings, engineering schematics, and clinical scans to produce deliverables including slide decks, spreadsheets, PDFs, Word documents, and marked-up images. Unlike many agentic evaluations, these inputs emphasize realistic visual complexity and domain-specificity, and are genuinely required to complete the task rather than just providing additional context

➤ Climb vs. Regression rubric scoring: Each task is graded against expert rubrics, averaging ~70 weighted criteria per task. Criteria are split into ‘Regression’ (baseline checks models should pass) and ‘Climb’ criteria (harder checks designed to separate strong models). The headline score is Climb WSR (Weighted Satisfaction Rate) - the weighted proportion Climb rubric criteria successfully completed

➤ Evaluated in Stirrup with an agentic judge: We run every task in Stirrup, our open source agent harness, with code execution, web search, web fetch, and image-viewing tools (up to 250 turns), and grade submitted deliverables using GPT-6 Luna (medium) as an agentic judge

Key MKA results:

➤ Claude Fable 5.2 leads on multimodal knowledge work: Anthropic’s recently released Claude Fable 5.2 (max) leads the MKA leaderboard with a Climb WSR of 57%, followed by Gemini 4 Carbon (high) at 54% and Claude Opus 5.5 (max) at 53%

➤ Cost per Task spans nearly 30× within 10 points of the top score: Claude Fable 5.2 averages $31.97 per task, over 5× Gemini 4 Carbon ($6.29) for ~2 extra points, and ~29× GPT-6.1 Sol ($1.11). Below Carbon, all four OpenAI models tested sit on the cost-performance Pareto frontier (GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra, and GPT-6 Luna), with Claude Haiku 5.5 and MiMo-V2.6-Pro making up the rest of the frontier

➤ No single model leads across all 10 industry verticals: Claude Fable 5.2 leads five verticals (Software & Data, Engineering, Logistics & Mapping, Business, and Finance alongside Claude Sonnet 5.5), Claude Opus 5.5 (max) leads Education and Construction, Gemini 4 Argon (high) leads Science, Gemini 4 Carbon (high) leads Healthcare, and Claude Sonnet 5.5 (max) narrowly leads Law & Government

Thank you to the @GoogleDeepMind team for their work building this benchmark and collaborating with us to bring it exclusively to Artificial Analysis!

This report is categorized under AITOP's "Models" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to US-China foundation model benchmarks and commercialization comparison and AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Google DeepMind, Claude Fable 5.2, Gemini 4 Carbon, 多模态.

Source attribution: curated from Artificial Analysis (@ArtificialAnlys).