Z.ai debuts open source GLM-4.6V, a native tool-calling vision model for multimodal reasoning

Chinese language AI startup Zhipu AI aka Z.ai has released its GLM-4.6V series, a brand new technology of open-source vision-language fashions (VLMs) optimized for multimodal reasoning, frontend automation, and high-efficiency deployment.

The discharge consists of two fashions in “giant” and “small” sizes:

1. GLM-4.6V (106B), a bigger 106-billion parameter mannequin geared toward cloud-scale inference

2. GLM-4.6V-Flash (9B), a smaller mannequin of solely 9 billion parameters designed for low-latency, native functions

Recall that usually talking, fashions with extra parameters — or inner settings governing their conduct, i.e. weights and biases — are extra highly effective, performant, and able to acting at the next basic stage throughout extra diverse duties.

Nevertheless, smaller fashions can provide higher effectivity for edge or real-time functions the place latency and useful resource constraints are essential.

The defining innovation on this sequence is the introduction of native operate calling in a vision-language mannequin—enabling direct use of instruments similar to search, cropping, or chart recognition with visible inputs.

With a 128,000 token context size (equal to a 300-page novel’s value of textual content exchanged in a single enter/output interplay with the consumer) and state-of-the-art (SoTA) outcomes throughout greater than 20 benchmarks, the GLM-4.6V sequence positions itself as a extremely aggressive various to each closed and open-source VLMs. It is out there within the following codecs:

• API access through OpenAI-compatible interface

• Try the demo on Zhipu’s internet interface

• Download weights from Hugging Face

• Desktop assistant app out there on Hugging Face Spaces

Licensing and Enterprise Use

GLM‑4.6V and GLM‑4.6V‑Flash are distributed underneath the MIT license, a permissive open-source license that enables free industrial and non-commercial use, modification, redistribution, and native deployment with out obligation to open-source by-product works.

This licensing mannequin makes the sequence appropriate for enterprise adoption, together with situations that require full management over infrastructure, compliance with inner governance, or air-gapped environments.

Mannequin weights and documentation are publicly hosted on Hugging Face, with supporting code and tooling out there on GitHub.

The MIT license ensures most flexibility for integration into proprietary programs, together with inner instruments, manufacturing pipelines, and edge deployments.

Structure and Technical Capabilities

The GLM-4.6V fashions comply with a standard encoder-decoder structure with important variations for multimodal enter.

Each fashions incorporate a Imaginative and prescient Transformer (ViT) encoder—based mostly on AIMv2-Enormous—and an MLP projector to align visible options with a big language mannequin (LLM) decoder.

Video inputs profit from 3D convolutions and temporal compression, whereas spatial encoding is dealt with utilizing 2D-RoPE and bicubic interpolation of absolute positional embeddings.

A key technical characteristic is the system’s assist for arbitrary picture resolutions and facet ratios, together with broad panoramic inputs as much as 200:1.

Along with static picture and doc parsing, GLM-4.6V can ingest temporal sequences of video frames with express timestamp tokens, enabling strong temporal reasoning.

On the decoding aspect, the mannequin helps token technology aligned with function-calling protocols, permitting for structured reasoning throughout textual content, picture, and gear outputs. That is supported by prolonged tokenizer vocabulary and output formatting templates to make sure constant API or agent compatibility.

Native Multimodal Instrument Use

GLM-4.6V introduces native multimodal operate calling, permitting visible property—similar to screenshots, photos, and paperwork—to be handed immediately as parameters to instruments. This eliminates the necessity for intermediate text-only conversions, which have traditionally launched data loss and complexity.

The software invocation mechanism works bi-directionally:

• Enter instruments may be handed photos or movies immediately (e.g., doc pages to crop or analyze).

• Output instruments similar to chart renderers or internet snapshot utilities return visible information, which GLM-4.6V integrates immediately into the reasoning chain.

In follow, this implies GLM-4.6V can full duties similar to:

• Producing structured studies from mixed-format paperwork

• Performing visible audit of candidate photos

• Routinely cropping figures from papers throughout technology

• Conducting visible internet search and answering multimodal queries

Excessive Efficiency Benchmarks In comparison with Different Related-Sized Fashions

GLM-4.6V was evaluated throughout greater than 20 public benchmarks protecting basic VQA, chart understanding, OCR, STEM reasoning, frontend replication, and multimodal brokers.

In accordance with the benchmark chart launched by Zhipu AI:

• GLM-4.6V (106B) achieves SoTA or near-SoTA scores amongst open-source fashions of comparable measurement (106B) on MMBench, MathVista, MMLongBench, ChartQAPro, RefCOCO, TreeBench, and extra.

• GLM-4.6V-Flash (9B) outperforms different light-weight fashions (e.g., Qwen3-VL-8B, GLM-4.1V-9B) throughout nearly all classes examined.

• The 106B mannequin’s 128K-token window permits it to outperform bigger fashions like Step-3 (321B) and Qwen3-VL-235B on long-context doc duties, video summarization, and structured multimodal reasoning.

Instance scores from the leaderboard embody:

• MathVista: 88.2 (GLM-4.6V) vs. 84.6 (GLM-4.5V) vs. 81.4 (Qwen3-VL-8B)

• WebVoyager: 81.0 vs. 68.4 (Qwen3-VL-8B)

• Ref-L4-test: 88.9 vs. 89.5 (GLM-4.5V), however with higher grounding constancy at 87.7 (Flash) vs. 86.8

Each fashions had been evaluated utilizing the vLLM inference backend and assist SGLang for video-based duties.

Frontend Automation and Lengthy-Context Workflows

Zhipu AI emphasised GLM-4.6V’s skill to assist frontend improvement workflows. The mannequin can:

• Replicate pixel-accurate HTML/CSS/JS from UI screenshots

• Settle for pure language modifying instructions to change layouts

• Determine and manipulate particular UI elements visually

This functionality is built-in into an end-to-end visible programming interface, the place the mannequin iterates on structure, design intent, and output code utilizing its native understanding of display captures.

In long-document situations, GLM-4.6V can course of as much as 128,000 tokens—enabling a single inference go throughout:

• 150 pages of textual content (enter)

• 200 slide decks

• 1-hour movies

Zhipu AI reported profitable use of the mannequin in monetary evaluation throughout multi-document corpora and in summarizing full-length sports activities broadcasts with timestamped occasion detection.

Coaching and Reinforcement Studying

The mannequin was educated utilizing multi-stage pre-training adopted by supervised fine-tuning (SFT) and reinforcement studying (RL). Key improvements embody:

• Curriculum Sampling (RLCS): Dynamically adjusts the problem of coaching samples based mostly on mannequin progress

• Multi-domain reward programs: Process-specific verifiers for STEM, chart reasoning, GUI brokers, video QA, and spatial grounding

• Operate-aware coaching: Makes use of structured tags (e.g., <assume>, <reply>, <|begin_of_box|>) to align reasoning and reply formatting

The reinforcement studying pipeline emphasizes verifiable rewards (RLVR) over human suggestions (RLHF) for scalability, and avoids KL/entropy losses to stabilize coaching throughout multimodal domains

Pricing (API)

Zhipu AI presents aggressive pricing for the GLM-4.6V sequence, with each the flagship mannequin and its light-weight variant positioned for prime accessibility.

• GLM-4.6V: $0.30 (enter) / $0.90 (output) per 1M tokens

• GLM-4.6V-Flash: Free

In comparison with main vision-capable and text-first LLMs, GLM-4.6V is among the many most cost-efficient for multimodal reasoning at scale. Under is a comparative snapshot of pricing throughout suppliers:

USD per 1M tokens — sorted lowest → highest complete value

Earlier Releases: GLM‑4.5 Collection and Enterprise Purposes

Previous to GLM‑4.6V, Z.ai launched the GLM‑4.5 household in mid-2025, establishing the corporate as a critical contender in open-source LLM improvement.

The flagship GLM‑4.5 and its smaller sibling GLM‑4.5‑Air each assist reasoning, software use, coding, and agentic behaviors, whereas providing sturdy efficiency throughout normal benchmarks.

The fashions launched twin reasoning modes (“considering” and “non-thinking”) and will mechanically generate full PowerPoint shows from a single immediate — a characteristic positioned to be used in enterprise reporting, schooling, and inner comms workflows. Z.ai additionally prolonged the GLM‑4.5 sequence with extra variants similar to GLM‑4.5‑X, AirX, and Flash, focusing on ultra-fast inference and low-cost situations.

Collectively, these options place the GLM‑4.5 sequence as a cheap, open, and production-ready various for enterprises needing autonomy over mannequin deployment, lifecycle administration, and integration pipel

Ecosystem Implications

The GLM-4.6V launch represents a notable advance in open-source multimodal AI. Whereas giant vision-language fashions have proliferated over the previous yr, few provide:

• Built-in visible software utilization

• Structured multimodal technology

• Agent-oriented reminiscence and determination logic

Zhipu AI’s emphasis on “closing the loop” from notion to motion through native operate calling marks a step towards agentic multimodal programs.

The mannequin’s structure and coaching pipeline present a continued evolution of the GLM household, positioning it competitively alongside choices like OpenAI’s GPT-4V and Google DeepMind’s Gemini-VL.

Takeaway for Enterprise Leaders

With GLM-4.6V, Zhipu AI introduces an open-source VLM able to native visible software use, long-context reasoning, and frontend automation. It units new efficiency marks amongst fashions of comparable measurement and offers a scalable platform for constructing agentic, multimodal AI programs.

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