Zhipu AI독점

GLM-5V-Turbo

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

이 모델 비교

Zhipu AI에서 개발한 고성능 기반 모델. 중국어 처리에 뛰어나며, 다양한 작업에 대응할 수 있습니다.

공유:X Hatena LINE

파라미터

Undisclosed

컨텍스트

200K

라이선스

Proprietary

출시일

2026-04-02

벤치마크 성능

AA Intelligence Index

—

LMArena Elo

—

HLE

—

ARC-AGI-2

—

SWE-bench Verified

—

GPQA Diamond

—

MMLU-Pro

—

LiveCodeBench

—

AIME 2025

—

MATH-500

—

일본어 처리 능력

🌐Multilingual

General multilingual model. Basic Japanese processing is possible, but inferior to specialized models.

API 가격

이 모델의 API 가격 정보는 현재 공개되지 않았습니다

강점

약점

활용 사례

심층 분석

Design2Code Score

94.8

vs Claude Opus 4.6: 77.3

Input Price (OpenRouter)

$1.20/1M tokens

Cheapest tracked provider route

Output Price (OpenRouter)

$4.00/1M tokens

Cache read: $0.24/1M

Context Window

200K tokens

Max output: 131K tokens

Architecture

MoE

744B total, 40B active parameters

Release Date

April 1, 2026

Z.AI's first native multimodal coding model

강점

・Native multimodal perception integrated as core reasoning component, not auxiliary interface
・Strong performance on multimodal coding benchmarks (94.8 on Design2Code) while preserving text-only coding capability
・Broad joint RL optimization across 30+ task categories yields robust gains in perception, reasoning, and agentic execution

약점

・Slower inference speed compared to text-only models (34th percentile for output speed per benchable.ai)
・Pricing varies significantly across providers ($0.70 to $5.00/1M input tokens)
・Limited independent benchmark validation; key evaluations like ZClawBench are Z.AI's proprietary benchmarks

경쟁사 비교

Model	Arena	SWE	GPQA	Price
Claude Opus 4.6	N/A	N/A	N/A	$3.00/$15.00
Kimi K2.5	N/A	N/A	N/A	N/A
GPT-5.2	N/A	80.0%*	N/A	N/A

개요

GLM-5V-Turbo is Z.AI's first native multimodal agent foundation model, designed specifically for vision-based coding and agent-driven tasks. Unlike models that bolt vision capabilities onto language backends, GLM-5V-Turbo integrates multimodal perception as a core component of reasoning, planning, tool use, and execution. The model represents a significant architectural advancement with innovations like the CogViT vision encoder and Multimodal Multi-Token Prediction (MMTP) that enable efficient processing of images, video, and text while maintaining training and inference efficiency. Positioned at the intersection of multimodal understanding and agentic execution, GLM-5V-Turbo excels at tasks requiring long-horizon planning, complex coding, and real-world interaction. It demonstrates strong performance on multimodal coding benchmarks while preserving competitive text-only coding capability, making it versatile for both visual and text-first workflows. The model is optimized for integration with agent frameworks like Claude Code and OpenClaw, enabling complete perception–planning–execution loops for practical applications. Key innovations include hierarchical optimization strategies that build agent capabilities across multiple levels of complexity, and an expanded multimodal toolchain that supports functions from image recognition to deep research. The development process also yielded important insights for agentic model development, emphasizing the foundational role of perception and the efficiency gains from hierarchical optimization over monolithic training approaches.

벤치마크 및 성능

GLM-5V-Turbo demonstrates strong performance across multimodal coding and agentic benchmarks while maintaining solid text-only capabilities. Key benchmark results from the official technical report include: | Benchmark Category | Benchmark | GLM-5V-Turbo Score | Key Comparisons | |-------------------|-----------|-------------------|------------------| | **Multimodal Coding** | Design2Code | 94.8 | Outperforms Claude Opus 4.6 (77.3) | | | Flame-VLM-Code | Strong | Leading results on UI-to-code generation | | | Vision2Web | Strong | End-to-end visual website development | | **Multimodal Tool Use** | ImageMining | 30.7 | Tests deep visual search capabilities | | | BrowseComp-VL | 51.9 | Visual web browsing and reasoning | | | MMSearch | 72.9 | Multimodal search integration | | | SimpleVQA | 78.2 | Visual question answering | | **GUI Agent** | AndroidWorld | 75.7 | Mobile GUI agent performance | | | OSWorld | 62.3 | Open-ended computer tasks | | | WebVoyager | Strong | Web agent performance | | **Text-only Coding** | CC-Backend | 22.8 | Surpasses base GLM-5-Turbo model | | | CC-Frontend | 68.4 | Preserves text coding capabilities | | | CC-RepoExploration | 72.2 | Maintains coding performance | Additional performance notes from the arXiv paper: - Joint RL optimization across 30+ task categories shows stable gains across domains - Perception improvements: +4.8% on RefCOCO-avg (2D grounding), +5.6% on MVBench (video), +7.7% on SUNRGBD (3D grounding) - Agentic improvements: +4.9% on OSWorld (GUI agents), +3.5% on MMSearch (tool use) - Strong performance in claw agent frameworks: 87.0/80.7 on PinchBench, 57.7/75.0 on ClawEval The model maintains a 200K context window with 131K max output tokens, supporting complex, long-horizon agentic workflows.

상세 비교

**GLM-5V-Turbo vs Claude Opus 4.6:** - **Pricing**: GLM-5V-Turbo is significantly cheaper ($1.20/$4.00 vs $3.00/$15.00 per 1M tokens) - **Multimodal Coding**: GLM-5V-Turbo leads on Design2Code (94.8 vs 77.3) - **Context**: Claude has larger context window (1M vs 200K) - **Capabilities**: Both support vision, multimodal input, and tool use; Claude adds code execution and computer use - **Best for**: GLM-5V-Turbo for cost-effective multimodal coding; Claude for longer-context reasoning and additional agentic features **GLM-5V-Turbo vs Kimi K2.5:** - **Positioning**: Both target visual agentic intelligence - **Performance**: GLM-5V-Turbo shows competitive/exceeding results on multimodal tool use benchmarks - **Ecosystem**: GLM-5V-Turbo integrates deeply with Claude Code and OpenClaw; Kimi has its own agent framework - **Pricing**: Direct comparison requires checking current rates, but GLM-5V-Turbo offers transparent pricing via OpenRouter **GLM-5V-Turbo vs GPT-5.2:** - **Specialization**: GLM-5V-Turbo is natively multimodal from ground up; GPT-5.2 likely adds vision as capability - **Agentic Focus**: GLM-5V-Turbo explicitly designed for agent workflows with joint RL training - **Pricing**: GPT-5.2 pricing not directly comparable from available data - **Benchmark Claims**: GLM-5V-Turbo paper suggests performance competitive with GPT-5.2 on multimodal tasks

커뮤니티 평가

Based on OpenRouter traffic data and application adoption, GLM-5V-Turbo has seen significant adoption in the developer community: **Top applications by token volume (OpenRouter data):** 1. **Hermes Agent** (2B tokens) - Nous Research's self-improving AI agent uses GLM-5V-Turbo for persistent memory and skill building 2. **SillyTavern** (431M tokens) - Power-user LLM frontend for character creation and roleplay 3. **OpenClaw** (307M tokens) - Open-source AI agent for messaging apps 4. **Claude Code** (297M tokens) - Anthropic's agentic coding tool integration **Developer reactions and adoption patterns:** - The model's integration with Claude Code and OpenClaw has been particularly noted for enabling complete visual coding workflows - Community interest in its Design2Code benchmark performance (94.8) versus Claude Opus 4.6 (77.3) has generated discussion about cost-effective alternatives for frontend development - Some developers report using GLM-5V-Turbo for screenshot-to-code workflows and GUI automation tasks - The model's 200K context window makes it suitable for complex, long-horizon agent tasks despite being smaller than some competitors' windows - Pricing consistency across providers ($1.20 input from multiple routes) is viewed favorably for production planning **Adoption notes:** The model appears popular for applications requiring multimodal understanding with agent capabilities, particularly in coding and automation scenarios. Its integration with established frameworks like Claude Code suggests practical utility in existing developer workflows.

활용 사례

**1. Visual Frontend Development and Code Generation:** Choose GLM-5V-Turbo when converting design mockups, screenshots, or Figma exports into executable frontend code. Example: Automatically recreating a mobile app interface from reference images, generating multiple mockup pages with consistent visual style. The model's Design2Code score of 94.8 indicates superior performance in this area compared to alternatives like Claude Opus 4.6. **2. GUI Automation and Agent Workflows:** Ideal for building agents that interact with graphical user interfaces. Example: Using with Claude Code to autonomously browse websites, collect visual assets, and recreate pages. The model's strong performance on AndroidWorld (75.7) and OSWorld (62.3) benchmarks demonstrates capability in real computer environment tasks. **3. Multimodal Deep Research and Content Creation:** When research requires integrating visual evidence with textual analysis. Example: Creating text-image interleaved reports from web research, generating presentations from academic papers with embedded figures. The model's tools for image cropping, web search, and document parsing support complete research workflows. **4. Technical Document Processing with Visual Elements:** For tasks requiring understanding of documents with charts, diagrams, or visual layouts. Example: Parsing technical reports, extracting data from charts, or creating documentation from complex visual materials. The model's OCR capabilities and visual grounding make it suitable for document-heavy workflows. **When to choose over alternatives:** - Over text-only models when visual understanding is core to the task - Over higher-priced models when cost efficiency is critical ($1.20 input vs $3.00+ for alternatives) - Over specialized vision models when agentic capabilities and tool integration are needed - When integration with Claude Code or OpenClaw frameworks is part of the architecture

최신 뉴스

**Release and Availability (April 2026):** - GLM-5V-Turbo launched on April 1, 2026 as Z.AI's first native multimodal coding foundation model - Immediately available through Z.AI's API and OpenRouter with consistent pricing ($1.20/$4.00 per 1M tokens) - Open-sourced under MIT license with commercial use permitted **Integration Updates:** - Deep integration with Claude Code and OpenClaw agent frameworks announced at launch - Official skills ecosystem published on ClawHub including PDF-to-Web, Web Replication, Stock Analyst, and others - Support for both Claude Code and AutoClaw frameworks for complete perception-planning-execution loops **Technical Innovations Publicized:** - Introduction of CogViT vision encoder and Multimodal Multi-Token Prediction (MMTP) architecture - Joint reinforcement learning across 30+ task categories including STEM, grounding, video, GUI agents, and coding agents - Expanded multimodal toolchain with tools for image recognition, web search, browser automation, and deep research **Benchmark Performance Highlights:** - Achieved 94.8 on Design2Code benchmark, outperforming Claude Opus 4.6's 77.3 - Strong performance on multimodal agent benchmarks including BrowseComp-VL (51.9) and MMSearch (72.9) - Demonstrated competitive results with Kimi K2.5 on multimodal tool use tasks **Ecosystem Development:** - Official skills repository published at github.com/zai-org/GLM-skills - Master skill available for unified installation and usage across agent systems - Integration with GLM-OCR and GLM-Image specialized models for extended capabilities

Positioned at the intersection of multimodal understanding and agentic execution, GLM-5V-Turbo excels at tasks requiring long-horizon planning, complex coding, and real-world interaction. It demonstrates strong performance on multimodal coding benchmarks while preserving competitive text-only coding capability, making it versatile for both visual and text-first workflows. The model is optimized for integration with agent frameworks like Claude Code and OpenClaw, enabling complete perception–planning–execution loops for practical applications.

Key innovations include hierarchical optimization strategies that build agent capabilities across multiple levels of complexity, and an expanded multimodal toolchain that supports functions from image recognition to deep research. The development process also yielded important insights for agentic model development, emphasizing the foundational role of perception and the efficiency gains from hierarchical optimization over monolithic training approaches.

출처

분석 생성일: 2026-07-17