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강력한 추론 능력 200K 긴 컨텍스트 이해 최신 추론 아키텍처

이 모델의 약점은 무엇인가요?

비오픈 소스 라이선스 모델 크기 상세 정보 비공개 제한된 배포 환경 가능성

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복잡한 논리 추론 실행 대량 문서 분석 고급 추론 작업 자동화

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Zhipu AI독점

GLM-5V-Turbo

Name: GLM-5V-Turbo
Author: Zhipu AI

GLM-5V-Turbo는 Zhipu AI가 개발한 추론 대형 모델입니다. 200K 컨텍스트 길이를 갖추고 있으며 기반 모델로서 고급 추론 능력을 제공합니다.

파라미터

Undisclosed

컨텍스트

200K

라이선스

Proprietary

출시일

2026-04-02

API 가격

이 모델의 API 가격 정보는 현재 공개되지 않았습니다

강점

・강력한 추론 능력
・200K 긴 컨텍스트 이해
・최신 추론 아키텍처

약점

・비오픈 소스 라이선스
・모델 크기 상세 정보 비공개
・제한된 배포 환경 가능성

활용 사례

・복잡한 논리 추론 실행
・대량 문서 분석
・고급 추론 작업 자동화

심층 분석

Arena Elo

1485

#3 overall (Design for Online)

SWE-Bench Verified

80.8%

vs GPT-5.2: 80.0% (arXiv)

Input Price

$1.20/1M tokens

Premium tier

Context Length

200K tokens

202,752 on OpenRouter

Agentic Index

65.6

Strong agentic performance

Output Speed

34th percentile

Slower than median models (benchable.ai)

강점

・Native multimodal agentic foundation: core architecture integrates perception, reasoning, and action
・Strong multimodal coding and agent benchmark performance (Design2Code 94.8, AndroidWorld 75.7)
・Seamless integration with major agent frameworks (Claude Code, AutoClaw, OpenClaw)

약점

・Relatively slow inference speed (34th percentile output speed)
・Premium pricing at $1.20/$4.00 per 1M tokens, significantly more expensive than some competitors
・Limited independent validation of key proprietary benchmarks (ZClawBench, ClawEval)

경쟁사 비교

Model	Arena	SWE	GPQA	Price
Claude Opus 4.6	1490	80.0%	92.4%	$5.00/$25.00
GLM-5-Turbo	1475	80.4%	91.3%	$0.96/$3.20
DeepSeek V3.2	1480	81.2%	91.8%	$0.28/$0.42

개요

GLM-5V-Turbo, developed by Zhipu AI (Z.ai), represents a significant architectural step toward native multimodal agent foundation models. Released on April 1, 2026, it is specifically designed to treat multimodal perception—processing images, videos, GUIs, and documents—as an integrated core component of reasoning and planning, rather than a peripheral feature. The model introduces key innovations including a new CogViT vision encoder for fine-grained understanding, Multimodal Multi-Token Prediction (MMTP) for efficient training, and extensive joint reinforcement learning across over 30 task categories to build robust agentic capabilities. Positioned as a premium-tier model for complex agent workflows, GLM-5V-Turbo excels in tasks requiring long-horizon planning and visual grounding, such as UI-to-code generation, GUI automation, and multimodal deep research. Its development emphasizes practical lessons for agentic AI, highlighting the foundational importance of perception and the efficiency of hierarchical optimization over monolithic training. While benchmark claims are strong, particularly on Z.ai's own agentic evaluations, the model operates within a competitive landscape where independent validation and cost-effectiveness are critical factors for adoption. The model's integration strategy focuses on becoming the cognitive core within external agent frameworks like Claude Code and AutoClaw, offloading execution to specialized tools while focusing on high-dimensional reasoning. This approach, combined with a substantial 200K context window and a rich ecosystem of official skills, aims to position GLM-5V-Turbo as a versatile engine for building the next generation of autonomous, vision-enabled agents.

벤치마크 및 성능

상세 비교

Head-to-head comparisons highlight GLM-5V-Turbo's positioning against key competitors: **vs. Claude Opus 4.6 (Anthropic):** - **Pricing:** GLM-5V-Turbo is significantly cheaper ($1.20/$4.00 vs. $5.00/$25.00 per 1M tokens). - **Context Window:** Claude Opus 4.6 offers a much larger 1M token context vs. GLM-5V-Turbo's 200K. - **Strengths:** Claude Opus 4.6 excels in nuanced reasoning, coding workflow support, and has broader provider availability. GLM-5V-Turbo's core strength is its native multimodal agentic design and superior performance on vision-centric benchmarks like Design2Code (94.8 vs. 77.3). - **Use Case:** Choose Claude for pure coding tasks with large context needs and when cost is secondary. Choose GLM-5V-Turbo for multimodal agent workflows where visual perception is integral and budget is a consideration. **vs. GLM-5-Turbo (Z.ai's own text-only variant):** - **Pricing:** Identical API pricing ($1.20/$4.00 per 1M tokens). - **Capabilities:** GLM-5-Turbo is text-only with higher reported speed (~200+ TPS). GLM-5V-Turbo adds native image/video processing but is slower (34th percentile speed). - **Tool Reliability:** GLM-5-Turbo reports a 0.67% tool call error rate on OpenRouter, optimized for text-based agent chains. - **Use Case:** Use GLM-5-Turbo for high-throughput, text-only agent pipelines. Use GLM-5V-Turbo only when the task requires visual understanding (e.g., GUI automation, design-to-code). Some teams use both in a pipeline for efficiency. **vs. DeepSeek V3.2:** - **Pricing:** DeepSeek V3.2 is an order of magnitude cheaper ($0.28/$0.42 per 1M tokens). - **Context & Openness:** DeepSeek offers 128K context and is open-source. GLM-5V-Turbo is proprietary with a larger 200K context. - **Strengths:** DeepSeek leads on standard academic benchmarks (SWE-Bench, MMLU). GLM-5V-Turbo's claim is specialized agentic performance in multimodal settings. DeepSeek lacks native multimodal input in its current V3 iteration. - **Use Case:** DeepSeek is the cost-effective choice for general coding and reasoning. GLM-5V-Turbo targets premium, vision-heavy agent applications where its specialized architecture may justify the cost.

커뮤니티 평가

Initial community and developer reactions, as gathered from review sites and technical commentary, are mixed but focused on its specialized niche: - **Technical Praise:** Engineers highlight the sophisticated architecture (CogViT, MMTP) and the model's strong results on agentic benchmarks like AndroidWorld and Design2Code. The integration with Claude Code and AutoClaw is seen as a smart strategy to leverage existing ecosystems. - **Skepticism on Benchmarks:** Many developers note the reliance on Z.ai's proprietary benchmarks (ZClawBench, ClawEval) and call for more independent, third-party validation on standardized benchmarks like SWE-Bench Verified before making production decisions. - **Cost Concerns:** The pricing ($1.20/$4.00) is a frequent point of discussion. While cheaper than Claude Opus 4.6, it is substantially more expensive than alternatives like DeepSeek V3 or Qwen, leading to questions about ROI for all but the most complex multimodal agent tasks. - **Performance vs. Hype:** Some reviewers (e.g., Verdent Guides, ComputerTech) caution that the model is a specialized "sharp tool for a narrow job," not a general-purpose frontier model. Its slower speed is noted as a real operational constraint for latency-sensitive applications. - **Adoption Pattern:** Early adopters appear to be developer teams building sophisticated GUI automation, visual coding assistants, and deep research agents, rather than general chatbot applications.

활용 사례

1. **Multimodal Deep Research & Report Generation:** Agents tasked with gathering information from the web, analyzing charts, images, and documents, and synthesizing findings into structured reports with interleaved text and figures. *Choose GLM-5V-Turbo over text-only models when the research sources are visually rich (e.g., academic papers with figures, dashboards, infographics).* 2. **Visual Coding & UI Automation:** Use cases involving interpreting design mockups (Figma, screenshots) to generate functional frontend code (HTML/CSS/JS), or replicating existing website UIs with high visual fidelity. *It outperforms Claude Opus 4.6 on Design2Code, making it a strong candidate for design-to-code pipelines.* 3. **GUI Agent Orchestration:** Automating tasks within mobile apps or desktop software by interpreting on-screen content. Examples include QA testing of applications, automated form filling, or extracting data from software interfaces. *Its high scores on AndroidWorld and OSWorld benchmarks validate this use case.* 4. **Document Intelligence and Transformation:** Processing complex PDFs, slide decks, or scanned documents to extract tables, formulas, and layout information for repurposing into new formats (e.g., converting a PDF report into a web page or PowerPoint). *The model's native document understanding and official skills (PDF-to-Web, PDF-to-PPT) support this workflow directly.*