GLM-4.5V (Non-reasoning)

Intelligent & Concise, But Slow & Pricey

GLM-4.5V (Non-reasoning)

A powerful open-license multimodal model from Zhipu AI, offering strong intelligence and conciseness at the cost of high prices and slow generation speed.

Multimodal64k ContextOpen LicenseHigh IntelligenceSlow SpeedExpensive

GLM-4.5V emerges from Zhipu AI's respected General Language Model (GLM) series as a formidable contender in the open-license arena. This multimodal model, capable of processing both text and image inputs, distinguishes itself with impressive analytical capabilities. Scoring a 26 on the Artificial Analysis Intelligence Index, it stands comfortably above the average for its class, making it a strong choice for tasks that demand nuanced understanding and accurate interpretation of complex information. Its performance suggests a sophisticated architecture adept at knowledge retrieval and synthesis.

However, this intelligence comes with significant trade-offs that define its market position. The model's most glaring weaknesses are its speed and cost. With a median output speed of just 34 tokens per second, it is substantially slower than many competitors, a factor that can severely impact user experience in real-time applications like chatbots or interactive assistants. This sluggishness is compounded by a premium pricing structure. At $0.60 per million input tokens and a steep $1.80 per million output tokens, GLM-4.5V is one of the more expensive options among open-weight models, demanding careful cost management from developers.

One of GLM-4.5V's most interesting and potentially valuable characteristics is its conciseness. In our benchmark tests, it produced answers using significantly fewer tokens than the average model. This tendency toward brevity can be a double-edged sword. On one hand, it can lead to lower output token costs and provide users with more direct, less verbose answers. On the other, it may lack the detail or conversational filler that some applications require. This positions GLM-4.5V as a specialized tool: ideal for analytical tasks where precision and brevity are valued, but less suited for applications where speed, low cost, or conversational verbosity are paramount.

With a generous 64k context window and an open license, GLM-4.5V offers developers significant flexibility. The large context is well-suited for processing long documents, extensive codebases, or detailed multimodal inputs. The open license provides freedom for fine-tuning and self-hosting, which can be a crucial advantage over proprietary, black-box models. Ultimately, choosing GLM-4.5V is a strategic decision to prioritize top-tier intelligence and multimodal functionality, while accepting the compromises of higher latency and operational costs.

Scoreboard

Intelligence

26 (11 / 33)

Scores 26 on the Artificial Analysis Intelligence Index, placing it well above the class average of 22 for comparable non-reasoning models.

Output speed

34.3 tokens/s

Notably slow, generating text at nearly half the speed of the class average (60 tokens/s). This can impact real-time applications.

Input price

$0.60 / 1M tokens

Significantly more expensive than the class average of $0.20, making it costly for input-heavy tasks like RAG.

Output price

$1.80 / 1M tokens

Over three times the class average of $0.54. The high output cost is a major factor for generative use cases.

Verbosity signal

6.6M tokens

Highly concise. Generated 6.6M tokens on the Intelligence Index, far less than the 8.5M average, which can help control output costs.

Provider latency

0.70 seconds

Median time to first token (TTFT) on Novita. A reasonable latency that provides a responsive feel before the slow generation begins.

Technical specifications

Spec	Details
Model Name	GLM-4.5V (Non-reasoning)
Owner	Zhipu AI
License	Open License (Commercial use permitted)
Modalities	Text, Image
Output Format	Text
Context Window	64,000 tokens
Release Date	Early 2024
Architecture	Transformer-based, part of the General Language Model (GLM) family
Intelligence Score	26 (Artificial Analysis Intelligence Index)
Median Output Speed	34.3 tokens/s (via Novita)
Median Latency (TTFT)	0.70 seconds (via Novita)
Input Price	$0.60 / 1M tokens (via Novita)
Output Price	$1.80 / 1M tokens (via Novita)

What stands out beyond the scoreboard

Where this model wins

High Intelligence: Its score of 26 places it in the upper echelon of open-weight models, making it highly effective for complex analysis, summarization, and question-answering tasks that require deep understanding.
Multimodal Capabilities: Natively processes both text and images, enabling sophisticated use cases like visual data analysis, image captioning, and creating descriptions for e-commerce listings without needing a separate vision model.
Exceptional Conciseness: Generates notably short and to-the-point responses. This reduces output token costs and can be ideal for applications where brevity and clarity are paramount, such as data extraction or mobile interfaces.
Large Context Window: The 64k context window allows it to analyze long documents, maintain context in extended conversations, or process large codebases in a single pass, reducing the need for complex chunking strategies.
Open License Flexibility: The open license provides freedom for commercial use, fine-tuning, and self-hosting, offering a powerful alternative to more restrictive proprietary models and enabling deeper integration into custom workflows.

Where costs sneak up

Expensive Output Tokens: At $1.80 per million tokens, the output cost is over 3x the class average. Applications that generate long-form text, detailed explanations, or conversational responses will see costs escalate rapidly.
Slow Generation Speed: The low output speed of ~34 tokens/s creates a poor user experience in real-time interactive applications. Users will notice the lag, which may necessitate UI workarounds like streaming or loading animations.
Above-Average Input Price: While the output price is the main concern, the $0.60 input price is also 3x the class average. This makes it expensive for Retrieval-Augmented Generation (RAG) and other input-heavy scenarios.
Multimodal Tokenization Costs: Analyzing images can consume a large number of input tokens. Combined with the high input price, performing visual analysis at scale can become prohibitively expensive if not managed carefully.
The Cost of Unused Conciseness: If your application requires verbose, conversational, or highly detailed outputs, you are paying a premium for a model that is naturally concise. You may spend more on prompt engineering to elicit longer responses, negating the model's natural strength.

Provider pick

Our benchmark analysis for GLM-4.5V was conducted using the Novita API, a popular platform for accessing a wide range of open-source and proprietary models. The following recommendations are based on the performance and pricing characteristics observed on this platform. As only one provider was benchmarked, our guidance focuses on matching the model's inherent traits to your project's priorities.

Priority	Pick	Why	Tradeoff to accept
Top Priority	Pick	Why	Tradeoff
Intelligence & Analysis	Novita	Provides direct, pay-as-you-go access to GLM-4.5V's primary strength: its high intelligence score. Ideal for offline analysis or non-interactive tasks.	The model's inherent slowness and high cost are unavoidable. Not suitable for real-time, user-facing applications without careful UX design.
Balanced Use	Novita	As the benchmarked provider, Novita offers a known quantity for performance and a standard API that's easy to integrate for testing or moderate-volume use cases.	You are subject to a shared, multi-tenant environment. There are no options for provisioned throughput or fine-tuning, which may be required for high-demand applications.
Lowest Latency	Novita	The observed 0.70-second time-to-first-token is respectable and provides a responsive start for interactions.	This initial responsiveness is quickly overshadowed by the very slow token generation speed that follows, which can make the overall experience feel sluggish.
Cost Control	Novita (with caution)	Offers a clear pricing structure to test the model. Its conciseness can help mitigate the high output price on certain tasks.	The model is fundamentally expensive. True cost control requires careful prompt engineering and use-case selection, not just provider choice.

Provider performance and pricing are subject to change. This analysis is based on data collected in Q2 2024. Always verify current rates and performance metrics directly with the provider before making production commitments.

Real workloads cost table

To translate per-token prices into tangible figures, we've estimated the cost of running several common workloads on GLM-4.5V. These scenarios use the benchmarked Novita pricing of $0.60 per million input tokens and $1.80 per million output tokens. Note how the cost shifts based on the ratio of input to output.

Scenario	Input	Output	What it represents	Estimated cost
Scenario	Input	Output	What it represents	Estimated Cost
Summarize a long article	10,000 tokens	500 tokens	Document analysis, RAG, content summarization	$0.0069
Moderate chatbot session	3,000 tokens	1,500 tokens	Interactive conversation, customer support	$0.0045
Analyze image and write ad copy	1,500 tokens (image + prompt)	150 tokens	Multimodal analysis, e-commerce content generation	$0.00117
Generate code from a spec	4,000 tokens	2,000 tokens	Developer assistance, code generation	$0.0060
Batch-process 100 short reports	100,000 tokens (100 x 1k)	10,000 tokens (100 x 100)	Data extraction, classification at scale	$0.078
Write a 2,000-word blog post	500 tokens (prompt)	2,700 tokens	Long-form content creation	$0.00516

These examples highlight that GLM-4.5V's costs are heavily influenced by the amount of text it generates. The blog post generation, with a high output-to-input ratio, is disproportionately affected by the $1.80 output price. Conversely, the article summarization, which is input-heavy, is more influenced by the $0.60 input price. For any application, modeling your expected token ratios is key to forecasting costs accurately.

How to control cost (a practical playbook)

Given GLM-4.5V's premium pricing and slow generation, a proactive approach to optimization is essential. Implementing cost-saving and performance-masking strategies can make the difference between a successful project and an expensive, slow one. Below are several techniques to get the most out of the model while protecting your budget and user experience.

Lean into Conciseness with Prompting

Capitalize on the model's natural brevity to control costs. The high output price makes every saved token valuable.

Use explicit instructions: Add phrases like "Be concise," "Answer in one sentence," or "Use bullet points" to your prompts.
Set output constraints: Instruct the model to limit its response to a specific number of words or paragraphs.
Request structured data: Ask for output in JSON or another machine-readable format. This naturally discourages conversational filler and makes the output more predictable and useful.

Optimize Input to Reduce Costs

While cheaper than output, the model's input price is still 3x the class average. Reducing input tokens is a key cost-saving lever, especially in RAG or chat applications.

Compress context: Use summarization techniques on chat history or documents before including them in the prompt.
Use shorter system prompts: Refine your system messages to be as efficient as possible.
Implement token-aware truncation: When context exceeds the window, intelligently truncate the least relevant parts of the history rather than just the oldest.

Mask Latency with UI/UX Patterns

The model's slow generation speed (~34 tokens/s) is a major UX challenge. You can't make the model faster, but you can make it feel faster.

Stream responses: Always stream tokens back to the user as they are generated. A sentence appearing word-by-word feels more interactive than waiting five seconds for a full paragraph.
Use optimistic UI: In some cases, you can show a loading state or even a preliminary, less-intelligent response from a faster model while GLM-4.5V processes in the background.
Combine with faster models: Use a cheaper, faster model for simple conversational turns and only route complex, analytical queries to GLM-4.5V.

Pre-process Images for Multimodal Use

Image data can translate into a surprisingly high number of input tokens. Reducing image size and complexity before sending it to the API is crucial for managing costs.

Resize images: Downscale high-resolution images to the minimum size necessary for the task. The model doesn't need a 4K image to identify a cat.
Increase compression: Use a higher JPEG compression level to reduce file size.
Convert to grayscale: If color is not relevant to the analysis, converting the image to grayscale can sometimes reduce its token representation.

FAQ

What is GLM-4.5V?

GLM-4.5V is a large multimodal language model developed by Zhipu AI, a prominent Chinese AI research company. It is part of their General Language Model (GLM) series and is capable of understanding and processing both text and image inputs to produce text-based outputs. It is distinguished by its high intelligence score and an open license.

What does the "(Non-reasoning)" tag signify?

The "(Non-reasoning)" tag likely indicates that this version of the model is a general-purpose foundation model, not one that has been specifically fine-tuned for complex, multi-step reasoning tasks (like solving advanced math problems or logical puzzles). It distinguishes it from a potential future variant, such as a "GLM-4.5V (Reasoning)" model, that might be optimized for those specific capabilities. This version excels at knowledge retrieval, language understanding, and vision analysis.

How does GLM-4.5V compare to models like GPT-4o or Claude 3 Sonnet?

GLM-4.5V competes as a powerful open-license alternative to leading proprietary models. In terms of raw intelligence, it is highly capable and approaches their performance on many benchmarks. However, it generally lags significantly behind models like GPT-4o and Claude 3 Sonnet in terms of speed and cost-efficiency. Its primary advantage is its open license, which offers greater flexibility for customization and deployment than the closed, API-only proprietary models.

Is GLM-4.5V a good choice for a real-time chatbot?

It's a trade-off. Its intelligence allows it to provide high-quality, nuanced answers. However, its very slow generation speed of around 34 tokens per second can make conversations feel sluggish and unresponsive. While usable with UI techniques like streaming, it is not an ideal choice for applications where a fast, snappy user experience is the top priority. Faster models would be a better fit for that specific use case.

What are the best use cases for GLM-4.5V?

GLM-4.5V shines in applications where its high intelligence and multimodal skills are critical, and where speed is a secondary concern. Ideal use cases include:

Offline Data Analysis: Analyzing and summarizing large volumes of text or images where response time is not critical.
Complex Question Answering: Tackling difficult questions that require deep knowledge and synthesis.
Content Moderation: Using its visual and text understanding to flag inappropriate content.
E-commerce: Analyzing product images to automatically generate descriptive text, where its conciseness is a benefit.

What does the "open license" allow?

An open license, such as the one provided with GLM-4.5V, typically grants users significant freedom, including the right to use the model for commercial purposes, modify it, and distribute their modified versions. It allows for self-hosting, which can provide data privacy and control advantages. However, it is crucial to read the specific terms of the license agreement to understand any restrictions or obligations, such as attribution requirements.

GLM-4.5V (Non-reasoning)