An open, multimodal model from Z AI, distinguished by its exceptional output speed and large context window, but with a higher-than-average cost structure and significant latency.
GLM-4.6V emerges from Z AI as a powerful contender in the open model landscape, bringing a compelling combination of features to the table. As a multimodal model, it can process both text and image inputs to generate text outputs, opening up a wide array of vision-language applications. Its most prominent feature is a massive 128,000-token context window, which allows it to handle incredibly long documents, extensive chat histories, or complex multi-part prompts in a single pass. This capability makes it a strong candidate for sophisticated retrieval-augmented generation (RAG), in-depth document analysis, and comprehensive summarization tasks that would choke models with smaller context limits.
Performance-wise, GLM-4.6V is a tale of two extremes. On one hand, its generation speed is phenomenal. Clocking in at a median of 99 tokens per second on the benchmarked provider, SiliconFlow, it ranks among the fastest models available. Once it starts generating text, it does so with blistering speed, making it ideal for tasks that require rapid creation of large volumes of text. However, this speed is counterbalanced by a remarkably high time-to-first-token (TTFT) of over 22 seconds. This significant latency means there's a long pause before the model begins its response, rendering it unsuitable for real-time, interactive applications like chatbots or live assistants. It is best suited for asynchronous, background processing where the initial delay is less critical than the overall throughput.
The economic profile of GLM-4.6V is another critical consideration. Its pricing is positioned at the premium end of the market. The input token price of $0.30 per million tokens is already above the market average of $0.20. The output token price is even more striking at $0.90 per million tokens, significantly higher than the average of $0.54. This 3-to-1 ratio between output and input costs heavily penalizes verbose, generative tasks. Consequently, the model is most cost-effective for workloads that are input-heavy and output-light, such as classification, extraction, or short-form summarization. For applications requiring long, detailed generated responses, the costs can accumulate rapidly, making careful prompt engineering and output length control essential for budget management.
N/A (N/A / 33)
99.2 tokens/s
$0.30 / 1M tokens
$0.90 / 1M tokens
N/A tokens
22.08 seconds
| Spec | Details |
|---|---|
| Model Owner | Z AI |
| License | Open |
| Context Window | 128,000 tokens |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Model Family | GLM (General Language Model) |
| Variant | 4.6V (Vision) |
| Specialization | General Purpose, Multimodal Analysis |
| Architecture | Transformer-based |
| Primary Provider | SiliconFlow |
Our analysis for GLM-4.6V is based on performance data from a single provider, SiliconFlow. As the only benchmarked option, it becomes the de facto choice, but it's crucial to understand the inherent strengths and weaknesses of this specific implementation.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Balanced | SiliconFlow | The only provider benchmarked, offering a complete, albeit specific, performance profile. | You must accept the package deal: elite speed comes with high latency and premium pricing. |
| Speed | SiliconFlow | Delivers an exceptional 99.2 tokens/s generation speed, making it a top choice for throughput. | The 22-second TTFT means total job time can be long for short tasks, despite the fast generation. |
| Cost-Effective | SiliconFlow | As the sole option, it's the only way to access the model, but it is not a budget choice. | Both input and output prices are above average, with output being particularly expensive. |
| Low Latency | None Recommended | The benchmarked provider has extremely high latency (22.08s TTFT). | This model is not suitable for interactive or real-time applications via this provider. |
Provider recommendations are based on a snapshot of market data. Performance and pricing are subject to change. This analysis is based on the 'Non-reasoning' variant of the model.
To understand the practical cost implications of GLM-4.6V's pricing, let's model a few common scenarios. These examples highlight how the 3:1 output-to-input cost ratio influences the total price depending on the nature of the task. All costs are estimated based on SiliconFlow's pricing of $0.30/1M input and $0.90/1M output tokens.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Document Summarization | 20,000 token article | 500 token summary | Input-heavy task, ideal for the model's cost structure. | ~$0.0065 |
| Content Generation | 100 token prompt | 3,000 token blog post | Output-heavy task, where costs can escalate quickly. | ~$0.0030 |
| Image Analysis | 1,200 token equivalent image | 250 token description | A multimodal task that remains cost-effective due to a short output. | ~$0.0006 |
| RAG from a Report | 50,000 token report + 200 token query | 150 token answer | Leverages the large context window for an extraction-style task. | ~$0.0152 |
| Multi-Turn Chat Session | 4,000 input tokens | 4,000 output tokens | A balanced exchange that highlights the higher cost of output. | ~$0.0048 |
The takeaway is clear: GLM-4.6V is most economical for tasks where the input is significantly larger than the output. The cost of generating a 3,000-token blog post is nearly half that of summarizing a 20,000-token document, demonstrating the heavy financial penalty on verbose generation.
Managing the cost of GLM-4.6V requires a strategy that plays to its strengths (speed, large context) while mitigating its weaknesses (high output cost, high latency). The following tactics can help you optimize your usage and budget.
The model's 22-second latency makes it a non-starter for real-time applications. Instead, build your architecture around asynchronous jobs.
With output tokens costing 3x more than input tokens, controlling verbosity is the single most effective cost-saving measure.
max_tokens parameter to a reasonable limit to prevent unexpectedly long and expensive responses.The 128k context window is a key feature; use it for tasks where the model's cost structure is favorable.
GLM-4.6V is a large language model from Z AI. It is multimodal, meaning it can process both text and images. Key features include a very large 128,000-token context window, an open license, and extremely fast text generation speed, though it suffers from high initial latency.
While not explicitly defined in the source data, a "Non-reasoning" tag typically suggests that this version of the model is not optimized for tasks requiring complex, multi-step logical deduction, mathematical problem-solving, or intricate planning. It is likely tuned more for knowledge retrieval, summarization, and creative generation based on the provided context.
It's a mixed bag. The model's generation speed (output tokens per second) is exceptionally fast, ranking in the top 5 of its class. However, its latency (time to first token) is extremely slow, at over 22 seconds. This means it's fast for generating long texts but slow to start, making it ideal for background jobs but poor for interactive chat.
Yes, it is positioned as a premium model. Both its input and output token prices are higher than the market average. The cost is particularly high for output tokens, which are three times more expensive than input tokens. This makes it most cost-effective for tasks with large inputs and short outputs.
Given its profile, GLM-4.6V excels at asynchronous, input-heavy tasks. Ideal use cases include:
Do not use this model for user-facing, real-time applications. Instead, design your system for asynchronous processing. Use a job queue to submit tasks to the model and process them in the background. The high latency is a 'cold start' problem; once the model is running, its throughput is excellent for clearing a backlog of tasks.