GLM-4.1V-Thinking

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What GLM-4.1V-Thinking is

GLM-4.1V-9B-Thinking 📖 View the GLM-4.1V-9B-Thinking <a href="https://arxiv.org/abs/2507.01006" target="blank"paper</a. 📍 Using GLM-4.1V-9B-Thinking API at <a href="https://www.bigmodel.cn/dev/api/visual-reasoning-model/GLM-4.1V-Thinking"Zhipu Foundation Model Open Platform</a Model Introduction Vision-Language Models (VLMs) have become foundational components of intelligent systems. As real-world AI tasks grow increasingly complex, VLMs must evolve beyond basic multimodal perception to enhance their reasoning capabilities in complex tasks. This involves improving accuracy, comprehensiveness, and intelligence, enabling applications such as complex problem solving, long-context understanding, and multimodal agents. Based on the GLM-4-9B-0414 foundation model, we present the new open-source VLM model GLM-4.1V-9B-Thinking, designed to explore the upper limits of reasoning in vision-language models. By introducing a "thinking paradigm" and leveraging reinforcement learning, the model significantly enhances its capabilities. It achieves state-of-the-art performance among 10B-parameter VLMs, matching or even surpassing the 72B-parameter Qwen-2.5-VL-72B on 18 benchmark tasks. We are also open-sourcing the base model GLM-4.1V-9B-Base to support further research into the boundaries of VLM capabilities. Compared to the previous generation models CogVLM2 and the GLM-4V series, GLM-4.1V-Thinking offers the following improvements: 1. The first reasoning-focused model in the series, achieving world-leading performance not only in mathematics but also across various sub-domains. 2. Supports 64k context length. 3. Handles arbitrary aspect ratios and up to 4K image resolution. 4. Provides an open-source version supporting both Chinese and English bilingual usage. Benchmark Performance By incorporating the Chain-of-Thought reasoning paradigm, GLM-4.1V-9B-Thinking significantly improves answer accuracy, richness, and interpretability. It comprehensively surpasses traditional non-reasoning visual models. Out of 28 benchmark tasks, it achieved the best performance among 10B-level models on 23 tasks, and even outperformed the 72B-parameter Qwen-2.5-VL-72B on 18 tasks. Quick Inference

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys glm-4-1v-thinking for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (glm-4-1v-thinking below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"glm-4-1v-thinking","messages":[{"role":"user","content":"Hello"}]}'

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