GLM 4.1V 9B Thinking API Provider: Everything You Need

The release of GLM 4.1V 9B marks a pivotal moment in the evolution of artificial intelligence. As the world’s first vision-language (VL) model with genuine reasoning capabilities, GLM 4.1V 9B goes beyond simple image recognition or captioning. It can analyze visual scenes, interpret complex relationships, and logically infer context—bridging the gap between perception and understanding.

With its accessible API, you can quickly experience the model’s powerful reasoning abilities for yourself. But with several API providers and integration options available, how do you choose the best way to get started? What should you look for when evaluating providers? This article will answer these questions and help you make an informed decision, so you can unlock the full potential of GLM 4.1V 9B with confidence.

Why Is GLM 4.1V 9B Famous?

GLM 4.1V 9B‘s Reasoning Ability

Prompt: Please describe this video in detail.
Model Response:
<think> Got it, let’s analyze the video step by step. First, observe the scene: two people interacting in a room with white curtains. The setting is indoors, likely a living space or bedroom.
First character (sitting): wearing a dark blue shirt with green and yellow stripes. They are holding what looks like a piece of clothing (maybe a baby outfit) and a needle, so they might be sewing or mending. Their posture suggests focus on the task.
Second character (standing behind): wearing a teal sweater. They have their hands on the seated person’s shoulders and back, showing a supportive gesture—maybe guiding or comforting. Their body language indicates closeness and care. Now, track the interaction over time: – Initial frames: Both are focused; standing person has hands on shoulder, seated person is sewing.As frames progress, standing person adjusts their hand position (from shoulder to head), showing affection (like touching hair). Seated person occasionally glances at them, indicating communication. – Expressions: Seated person seems engaged in conversation, maybe explaining something about the sewing. Standing person listens attentively, then smiles, suggesting a positive exchange. Objects: On the table, there’s a plaid bag and some colorful items, adding to the domestic feel. The lighting is soft from the curtains, creating a warm atmosphere. Overall mood: Intimate, caring, collaborative. The interaction suggests a close relationship, possibly family (like parent – child or romantic partners) sharing a quiet moment together while doing a craft activity. Need to make sure all details are covered: characters’ actions, expressions, environment, objects, and the overall tone.</think><answer> The video depicts an intimate indoor scene featuring two individuals engaged in a collaborative moment while doing a craft activity. In a room with white curtains creating a warm, domestic atmosphere, one person sits, focused on sewing a piece of clothing (possibly a baby outfit) in a dark – striped shirt. The other, standing behind in a teal sweater, offers support—starting with hands on shoulders, then gently adjusting to the seated person’s head, showing care. They communicate through glances and listening, with the standing individual smiling positively. A plaid bag and colorful table items enhance the cozy, familial or romantic vibe, resulting in an intimate, caring interaction.

Evaluation:

• Comprehensive observation of details: It can meticulously describe the characters’ clothing (such as the person sitting wearing a dark blue shirt with stripes, and the person standing wearing a teal sweater), actions (changes from placing hands on the shoulders to the head, sewing actions, etc.), expressions (the communication demeanor of the sitting person, the smile of the standing person after listening), and the environment (white curtains, items on the table, etc.), thus constructing a rich scene.
• Coherent logic: It reasons in an organized manner following the steps of scene observation, character analysis, interaction tracking (changes in actions as the frames progress), and summary of items and atmosphere. It deduces relationships from behaviors (such as the intimate relationship possibly being family members or romantic partners), with a complete logical chain.
• Reasonable interpretation of emotions and relationships: Based on actions (placement of hands, communication and interaction) and the environment (warm lighting, household items), it accurately interprets the atmosphere of intimacy, care, and collaboration, as well as the possible character relationships, which conforms to the emotional tone presented by the scene.

How to Evaluate AI API Providers: The 5 Key Metrics

1. Context Length

The maximum number of tokens the model can handle in one request.
A higher context length enables the model to process longer texts or more complex conversations in a single interaction.

2. Token Cost

The price charged per million input and output tokens processed.
Token cost directly affects the overall usage expense, especially for large-scale or high-frequency applications.

3. Latency

The time from sending a request to receiving the first response, usually measured in milliseconds or seconds.
Lower latency results in a smoother user experience, especially for real-time or interactive applications.

4. Throughput

The number of requests the API can handle per second.
High throughput allows the API to serve more users or tasks simultaneously, which is important for applications with high concurrency.

5. Integration Capability

The API’s compatibility and ease of integration with various applications, frameworks, and development environments.
Strong integration capability is often reflected in the availability of SDKs, comprehensive documentation, code samples, and support for popular programming languages or platforms.

API Provider of GLM 4.1V 9B Thinking

According to OpenRouter, GLM‑4.1V 9B Thinking is currently offered only by Novita AI. However, our search indicates there are other API providers as well. We will compare them in detail next.

1.Novita AI

Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.

Why Should You Choose Novita AI?

1. Development Efficiency

• Built-in Multimodal Models: Advanced models like DeepSeek V3, DeepSeek R1, and LLaMA 3.3 70B are already integrated and available for immediate use—no extra setup required.
• Streamlined Deployment: Developers can launch AI models quickly and easily, without the need for a specialized AI team or complex procedures.

2. Cost Advantage

• Proprietary Optimization: Unique optimization technologies lower inference costs by 30%-50% compared to major providers, making AI more affordable.

How to Access GLM 4.1V 9B Thinking via Novita API?

Step 1: Log In and Access the Model Library

Log in to your account and click on the Model Library button.

Step 2: Choose Your Model

Browse through the available options and select the model that suits your needs.

Step 3: Start Your Free Trial

Begin your free trial to explore the capabilities of the selected model.

Step 4: Get Your API Key

To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.

Step 5: Install the API

Install API using the package manager specific to your programming language.

After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.

from openai import OpenAI
  
client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    api_key="session_kgNdXtDPt2zYc95i-nDWPaW4Zl_e7nf4VDpukuIVBKpko1-LE8xCasG4YK7c-3c1xnPzGYRuocFk_DhkPUUQyQ==",
)

model = "thudm/glm-4.1v-9b-thinking"
stream = True # or False
max_tokens = 4000
system_content = ""Be a helpful assistant""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }

chat_completion_res = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": system_content,
        },
        {
            "role": "user",
            "content": "Hi there!",
        }
    ],
    stream=stream,
    max_tokens=max_tokens,
    temperature=temperature,
    top_p=top_p,
    presence_penalty=presence_penalty,
    frequency_penalty=frequency_penalty,
    response_format=response_format,
    extra_body={
      "top_k": top_k,
      "repetition_penalty": repetition_penalty,
      "min_p": min_p
    }
  )

if stream:
    for chunk in chat_completion_res:
        print(chunk.choices[0].delta.content or "", end="")
else:
    print(chat_completion_res.choices[0].message.content)
  
  

2.SiliconFlow

SiliconFlow as an API provider offers several key benefits:

• Speed: Extremely fast inference for both language and multimodal models.
• Flexibility: Options for serverless, dedicated, or custom deployments—run models your way.
• Efficiency: High throughput, low latency, and cost-effective pricing.
• Privacy: No data is ever stored; your models and data stay private.
• Dev-Ready: Comes with SDKs, observability, and easy scaling out of the box.
• Simplicity: A single API for all models, fully compatible with OpenAI standards.

These benefits focus on speed, reliability, privacy, and developer-friendly features—addressing what developers care about most.

API Python Guide

import requests

url = "https://api.siliconflow.com/v1/chat/completions"

payload = {
    "model": "THUDM/GLM-4.1V-9B-Thinking",
    "stream": False,
    "max_tokens": 512,
    "enable_thinking": True,
    "thinking_budget": 4096,
    "min_p": 0.05,
    "temperature": 0.7,
    "top_p": 0.7,
    "top_k": 50,
    "frequency_penalty": 0.5,
    "n": 1,
    "stop": [],
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "image_url": {
                        "detail": "auto",
                        "url": "data:image/png;base64,XXX"
                    },
                    "type": "image_url"
                }
            ]
        }
    ]
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.request("POST", url, json=payload, headers=headers)

print(response.text)

GLM 4.1V 9B Thinking’s API Price

Novita AI

• Input Price: $0.035 / million tokens
• Output Price: $0.138 / million tokens

Silicon Flow

• Input Price: $0.035 / million tokens
• Output Price: $0.14 / million tokens

These prices show that both Novita AI and Silicon Flow charge exactly the same rate for input tokens, while the output token price is nearly identical, with only a slight difference—$0.138 per million tokens for Novita AI and $0.14 per million tokens for Silicon Flow. This makes it easy to compare costs across providers and choose the one that best fits your requirements.

GLM 4.1V 9B Thinking API Access on Hugging Face

GLM 4.1V 9B Thinking API Access on Free Tier

GLM 4.1V 9B API with SDK

GLM 4.1V 9B sets a new benchmark for AI by introducing true reasoning to vision-language models. Its ability to observe, analyze, and deduce relationships from both images and text enables more natural, insightful, and human-like interactions. With accessible APIs, developer-friendly SDKs, and support for popular agent frameworks, GLM 4.1V 9B empowers users and organizations to build advanced AI solutions that understand the world in a richer and more nuanced way. Start exploring its capabilities today on Novita AI’s free API playground.

Frequently Asked Questions

Novita AI is the All-in-one cloud platform that empowers your AI ambitions. Integrated APIs, serverless, GPU Instance — the cost-effective tools you need. Eliminate infrastructure, start free, and make your AI vision a reality.

Recommend Reading

• GLM 4.1V 9B Thinking vs Qwen2.5 VL 72B: Which Fits What？
• Deepseek v3 vs Llama 3.3 70b: Language Tasks vs Code & Math
• Llama 3.2 3B vs DeepSeek V3: Comparing Efficiency and Performance.