Z.ai Vision Models

Compare all 3 Z.ai vision models we track, 2 of them open-weight. Try them on your own images, free in the Roboflow Playground.

3 models · 2 open-weight · 3 free to try · Updated Aug 2026

About Z.ai Vision Models

Z.ai ships vision models on two tiers, and our catalog tracks 3 of them. 2 are open-weight: the weights are downloadable, so you can self-host under their own licenses. The other 1 is proprietary, reached through an API and billed by the provider. The open-weight side runs on MIT terms. The newest additions are GLM 5.3 Flash (Aug 2026) and GLM 5V Turbo (Apr 2026).

Between them, the Z.ai models we list cover 10 distinct vision tasks. The widest coverage is chart question answering (3 models), document question answering (3), and OCR (3). For chart question answering, start with GLM 5.3 Flash, GLM 5V Turbo, or GLM-OCR.

On the open-weight tier, GLM 5.3 Flash is the largest at 320B total, 18B active parameters and GLM-OCR the smallest at 0.9B, small enough to run on a single GPU or on-device. Both carry a permissive license, so commercial use is straightforward.

On the API tier, the one option is GLM 5V Turbo (Apr 2026), billed per token by the provider rather than run on your own hardware. Which tier to start on is a constraints question, not a quality one: reach for the open-weight side when you need offline inference, predictable per-image cost, or a checkpoint you can fine-tune on your own data, and for the API side when you want broad general reasoning without managing GPUs. All 3 Z.ai vision models run live in the Roboflow Playground, so you can run the same image through several of them and compare the answers before committing to one.

Which Z.ai Model Should You Use?

What each of the 3 Z.ai vision models in our catalog is built for, and how you run it.

GLM 5.3 Flash: Best for: Object Detection and OCR. Open weights: MIT license, 320B total, 18B active parameters. Self-host it or run it here. Runnable in the Playground.
GLM 5V Turbo: Best for: Object Detection and OCR. Proprietary: no downloadable weights, billed per token through Z.ai's API. Runnable in the Playground.
GLM-OCR: Best for: OCR and Document Question Answering. Open weights: MIT license, 0.9B parameters. Self-host it or run it here. Small enough for on-device and edge deployment. Runnable in the Playground.

Open-Source Z.ai Models

2 models with downloadable weights you can self-host under their licenses (MIT). All run live in the Playground through hosted APIs, so self-hosting is optional.

GLM 5.3 Flash

GLM-5.3-Flash is the first natively multimodal model in Z.ai's GLM-5 series, a mixture-of-experts transformer with roughly 320 billion total parameters and 18 billion activated per token. It routes each token through 8 of 288 experts across 45 language layers that interleave KDA linear attention with sparse multi-head latent attention, and pairs them with a 24-layer vision encoder that handles image and video input. The checkpoint declares a maximum context length of 1,048,576 tokens, ships in native FP8, and includes a multi-token prediction draft layer for speculative decoding. Z.ai reports that the hybrid attention design reduces attention computation by 3.01x and KV cache size by 4.44x relative to GLM-5.3.The model starts from a newly trained base built on a 30 trillion token multimodal pre-training corpus and adopts Manifold-Constrained Hyper-Connections to improve scaling efficiency. Vision is integrated into the coding and agent loop, so the model can inspect interfaces, rendered output, and images while operating across code, browsers, and graphical user interfaces. Z.ai reports scores of 84.3 on Terminal-Bench 2.1, 63.4 on DeepSWE 1.1, 55.3 on Humanity's Last Exam with tools, and 48.8 on AutomationBench, and the model exposes low, high, and max thinking modes.

GLM-OCR

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder-decoder architecture by Zhipu AI. The model combines a 0.4B-parameter CogViT visual encoder pre-trained on large-scale image-text data, a lightweight cross-modal connector with efficient token downsampling, and a 0.5B-parameter GLM language decoder, totaling 0.9B parameters. To address the inefficiency of standard autoregressive decoding in deterministic OCR tasks, GLM-OCR introduces a Multi-Token Prediction (MTP) mechanism that predicts multiple tokens per step, significantly improving decoding throughput while keeping memory overhead low through shared parameters. Training proceeds through four stages: visual encoder pretraining with MIM, CLIP, and distillation objectives; vision-language pretraining on document parsing, grounding, and VQA data; supervised fine-tuning on curated OCR datasets covering text, formula, table, and key information extraction; and full-task reinforcement learning to improve accuracy and structural consistency.At the system level, GLM-OCR adopts a two-stage pipeline in which PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition. This design enables robust handling of diverse document layouts including tables, formulas, and multi-column text. The model supports document parsing and targeted recognition tasks, producing structured outputs in Markdown, JSON, and LaTeX formats across more than 100 languages. On the OmniDocBench V1.5 benchmark, GLM-OCR scores 94.62, and achieves 94.0 on OCRBench and 96.5 on UniMERNet for formula recognition.

Z.ai Models via API

1 proprietary model where the weights aren't downloadable: access is through each provider's API and billed by them. Try all of them free in the Playground.

GLM 5V Turbo

GLM-5V-Turbo is a native multimodal model from Z.ai that extends the GLM family with joint image, video, and text input aimed at vision-centered coding and agent workflows. The model reads screenshots, design drafts, document layouts, and interface captures and generates runnable code from them, covering tasks such as turning a visual design into a working front end, diagnosing rendering and layout defects from screen captures, and operating graphical user interfaces during long-horizon agent runs. It accepts roughly 200,000 input tokens and can emit up to 131,072 output tokens in a single response, which supports sessions that hold specifications, source files, logs, and visual references at the same time.Training includes a joint reinforcement learning stage spanning more than 30 tasks simultaneously, an approach Z.ai describes as a way to counter the trade-off in which improving visual recognition degrades programming ability and the reverse. Reported evaluations cover pure-text coding on the backend, frontend, and repository exploration tracks of CC-Bench-V2, together with agent execution suites such as PinchBench, ClawEval, and ZClawBench, indicating that text coding behavior is retained after visual input is added.

Frequently Asked Questions About Z.ai Vision Models

Which Z.ai models can do chart question answering?

3 of the 3 Z.ai vision models we track handle chart question answering: GLM 5.3 Flash, GLM 5V Turbo, and GLM-OCR. Each model page lists its full task coverage, license, and specs.

Which Z.ai models can do document question answering?

3 of the 3 Z.ai vision models we track handle document question answering: GLM 5.3 Flash, GLM 5V Turbo, and GLM-OCR. Each model page lists its full task coverage, license, and specs.

Are Z.ai vision models open source?

Partly. 2 of the 3 Z.ai vision models we track are open-weight (for example GLM 5.3 Flash and GLM-OCR), downloadable and self-hostable under their licenses (MIT). The other 1 are proprietary and reached through Z.ai's API.

What is the best Z.ai model for chart question answering?

We do not publish a Z.ai-only ranking, so pick on constraints rather than a label. 3 Z.ai models handle chart question answering; the most recent is GLM 5.3 Flash (Aug 2026). For fixed categories in production, a model fine-tuned on your own data typically beats any general-purpose model.

How many Z.ai vision models are on Roboflow Playground?

We track 3 live Z.ai vision models, 2 open-weight and 1 available through an API. The most recent addition is GLM 5.3 Flash, released Aug 2026.

Can I try Z.ai vision models for free?

Yes. All 3 Z.ai models run live in the Roboflow Playground. Upload your own image, run several models on it at once, and compare the outputs side by side. No setup and no account required.

This page lists all 3 Z.ai vision models in the Roboflow Playground catalog: 2 open-weight models you can self-host and 1 proprietary model accessed through an API. They cover chart question answering, document question answering, and OCR, among other tasks. All of them run live in the Roboflow Playground on your own images. Compare licenses, parameters, prices, and release dates side by side, or open any model page for full details.