Z AI
Released December 8, 2025

GLM 4.6V

GLM 4.6V is a vision-language model from Z AI featuring native multimodal function calling, interleaved image-text generation, and document understanding.

Inputs
Image
Text
Video
Outputs
Text

Model Overview

Capabilities, design details, and architectural traits

GLM 4.6V - Native Multimodal Function Calling Vision-Language Model

GLM 4.6V is Z.AI's (Zhipu AI) open-source vision-language model that introduces native multimodal function calling - bridging the gap between visual perception and executable action to provide a unified foundation for multimodal agents.

Native Multimodal Function Calling

GLM 4.6V's defining capability is its closed-loop perception-to-execution pipeline:

  • Multimodal Tool Input: Images, screenshots, and document pages are passed directly as tool call parameters - no prior text conversion required, preventing information loss.
  • Multimodal Tool Output: Visual results returned by tools (charts, rendered web screenshots, search-retrieved product images) are directly interpreted and incorporated into subsequent reasoning chains without text intermediaries.

Documented Capabilities

  • Interleaved Image-Text Generation: Takes multimodal context (documents, user inputs, tool-retrieved images) and synthesizes coherent, visually grounded mixed-media outputs. During generation, the model can autonomously call search and retrieval tools to curate additional visuals.
  • Multimodal Document Understanding: Processes up to 128K tokens of multi-document input as direct image pages - interpreting text, layout, charts, tables, and figures jointly without OCR preprocessing. Equivalent to ~150 complex document pages, 200 slide pages, or a 1-hour video in a single inference pass.
  • Frontend Replication & Visual Editing: Reconstructs pixel-accurate HTML/CSS/JS from UI screenshots by detecting layout, components, and color schemes. Supports natural-language-driven iterative visual edits - users can circle a region and issue a text instruction; the model locates and corrects the corresponding code.

Benchmark Performance

Independent evaluations · Artificial Analysis

11.2%
Intelligence

Accuracy & Capability Details

GPQA - Graduate Science71.9%
Humanity's Last Exam9.6%
SciCode - Scientific Coding30.4%
Instruction Following30.1%
Long Context Reasoning48.7%
τ²-Bench - Agentic Tasks31.6%
TerminalBench - System Control14.4%

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Model Details
General Info
ProviderZ AIAnthropicAnthropic
Release DateDecember 8, 2025September 22, 2026September 28, 2026
Knowledge Cutoff--Jun 2026
Context & Limits
Context Window131K
1M
Best Context Window
1M
Best Context Window
Pricing (per 1M tokens)
Input Pricing
$0.30
Best Input Pricing
$4$2
Output Pricing
$0.90
Best Output Pricing
$20$10
Modalities
Inputs
imagetextvideo
textimagefile
textimagefile
Outputs
text
text
text
Benchmarks (0-100)
Intelligence Index8.4
57.6
Best Intelligence Index
56.0
Coding Index---
Agentic Index---
GLM 4.6V
Claude Opus 5.5
Claude Sonnet 5.5

Humanity's Last Exam

Extremely difficult logical reasoning and knowledge.

4%
GLM 4.6V
Humanity's Last Exam
Score: 4%
GLM 4.6V
61%
Claude Opus 5.5
Humanity's Last Exam
Score: 61%
Claude Opus 5.5
55%
Claude Sonnet 5.5
Humanity's Last Exam
Score: 55%
Claude Sonnet 5.5

Long Context Reasoning

Logical reasoning over long context windows.

17%
GLM 4.6V
Long Context Reasoning
Score: 17%
GLM 4.6V
85%
Claude Opus 5.5
Long Context Reasoning
Score: 85%
Claude Opus 5.5
83%
Claude Sonnet 5.5
Long Context Reasoning
Score: 83%
Claude Sonnet 5.5

SciCode Benchmark

Scientific coding and mathematical modeling.

27%
GLM 4.6V
SciCode Benchmark
Score: 27%
GLM 4.6V
67%
Claude Opus 5.5
SciCode Benchmark
Score: 67%
Claude Opus 5.5
61%
Claude Sonnet 5.5
SciCode Benchmark
Score: 61%
Claude Sonnet 5.5

Independent evaluation data provided by Artificial Analysis. To view the latest benchmarks and full details, visit their official site.

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