An open-weight model from Z AI that delivers top-tier reasoning capabilities and remarkable speed, albeit at a slightly premium price point and with high verbosity.
GLM-4.6 (Reasoning) emerges as a formidable contender in the landscape of open-weight large language models. Developed by Z AI, this model is specifically engineered to excel at complex cognitive tasks. Its '(Reasoning)' designation is not just a label; it signifies a fine-tuning process focused on enhancing its abilities in logical deduction, multi-step problem-solving, and nuanced instruction following. This makes it a powerful tool for developers and researchers building applications that require a deep understanding of context and causality, moving beyond simple text generation to more sophisticated analytical capabilities.
Scoring an impressive 56 on the Artificial Analysis Intelligence Index, GLM-4.6 places itself firmly in the upper echelon of models, significantly outperforming the class average of 42. This high score is a testament to its advanced architecture and training, positioning it as a reliable choice for tasks that would challenge less capable models. However, this intelligence comes with a notable characteristic: extreme verbosity. During testing, it generated 86 million tokens, nearly four times the average. While this thoroughness can be beneficial for exhaustive analysis, it has direct implications for cost and requires careful management in production environments.
Performance is another area where GLM-4.6 shines. With an average output speed of 93 tokens per second, it is more than twice as fast as the average model in its class. This combination of high intelligence and rapid inference makes it uniquely suited for interactive applications like advanced chatbots, real-time data analysis tools, and developer assistants where both the quality and speed of the response are critical. The model's massive 200,000-token context window further expands its utility, enabling it to process and analyze entire books, extensive legal documents, or large codebases in a single pass. This capability unlocks new possibilities for deep contextual understanding and long-form content generation, though it also introduces a significant cost vector that users must consider when processing large inputs.
Despite its open license, which offers flexibility in deployment, GLM-4.6 is positioned at a slightly premium price point. Its input and output token costs are moderately higher than the average, and its high verbosity can amplify output costs significantly. The total evaluation cost of over $226 on the Intelligence Index underscores that harnessing its power comes at a price. Therefore, prospective users must weigh its superior reasoning and speed against a total cost of ownership that may exceed that of other open-weight alternatives. The key to successfully deploying GLM-4.6 lies in leveraging its strengths for tasks that truly demand them while implementing strategies to mitigate its cost drivers.
56 (8 / 51)
93.0 tokens/s
$0.60 / 1M tokens
$2.20 / 1M tokens
86M tokens
~0.47 seconds
| Spec | Details |
|---|---|
| Model Owner | Z AI |
| License | Open |
| Context Window | 200,000 tokens |
| Model Type | Generative Language Model (GLM) |
| Architecture | Dense Transformer |
| Tuning Focus | Reasoning, Complex Instruction Following |
| Input Modalities | Text |
| Output Modalities | Text |
| Quantization Support | Available via providers (FP8, FP4 noted) |
| Intelligence Index Score | 56 / 100 |
| Intelligence Index Rank | #8 out of 51 |
| Average Speed (Benchmark) | 93.0 tokens/s |
Choosing the right API provider for GLM-4.6 is crucial for balancing performance and cost. The ideal choice depends entirely on your primary application need: are you optimizing for the lowest possible price, the fastest response time for an interactive tool, or the highest throughput for batch processing? Our benchmarks reveal clear winners for each of these priorities.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Cost | Deepinfra (FP4) | Offers the best blended price at $0.93 per million tokens, driven by an exceptionally low output price. Ideal for budget-sensitive, output-heavy batch jobs. | Uses FP4 quantization, which may introduce a minor, often negligible, impact on quality. Its raw speed is lower than premium-priced providers. |
| Maximum Throughput | Cerebras | Delivers an unparalleled 1595 tokens/s, more than 8 times faster than the next competitor. The definitive choice for high-volume, non-interactive processing. | Top-tier performance typically comes at a premium price. Latency, while low, is not its primary advantage over raw speed. |
| Lowest Latency | Cerebras | At 0.24 seconds time-to-first-token, it provides the most immediate response, which is critical for conversational AI and real-time user interfaces. | As with throughput, this best-in-class performance is likely to be one of the most expensive options. |
| Balanced Performance | Together.ai | A strong all-rounder with very low latency (0.44s), great speed (148 t/s), and a competitive blended price of $1.00 per million tokens. | It is not the absolute cheapest nor the fastest, but represents a highly effective compromise for a wide range of applications. |
| Cost-Effective Speed | Fireworks | Provides the second-fastest speed in the benchmark (188 t/s) at a very attractive blended price of $0.96. Also features the lowest input token price. | Latency is slightly higher than Cerebras or Together.ai, making it a better fit for tasks where throughput is more important than initial response time. |
Provider benchmarks reflect a snapshot in time and are subject to change. Performance can vary based on server load, geographic region, and specific API configurations. Quantized models (e.g., FP4, FP8) offer cost savings but should be tested for any potential quality degradation on your specific tasks.
To understand the practical cost of using GLM-4.6, it's helpful to model a few common scenarios. The following estimates are based on the model's benchmarked average pricing of $0.60 per 1M input tokens and $2.20 per 1M output tokens. Note that the model's high verbosity could increase output token counts and costs beyond these estimates if not carefully managed through prompting.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Long Document Summarization | 50,000 tokens | 2,000 tokens | Analyzing a lengthy research paper or legal document to extract key findings. | ~$0.074 |
| Complex RAG Chat Session | 15,000 tokens | 500 tokens | A user asks a question requiring significant contextual data from a vector database. | ~$0.010 |
| Code Generation & Refactoring | 2,000 tokens | 4,000 tokens | A developer provides a function and asks the model to refactor it and add documentation. | ~$0.010 |
| Drafting a Blog Post | 500 tokens | 8,000 tokens | Generating a long-form article based on a brief outline and key points. | ~$0.018 |
| Batch Email Classification (100 emails) | 50,000 tokens (100x500) | 10,000 tokens (100x100) | Processing a batch of customer support emails to categorize and tag them by topic. | ~$0.052 |
While the cost per individual task is low, the model's slightly premium pricing and high verbosity become significant at scale. Output-heavy workloads, like content generation, are particularly impacted by the $2.20/M output price. For high-volume applications, the cumulative costs will be noticeably higher than with less verbose, more economically priced models.
Successfully deploying GLM-4.6 requires a proactive approach to cost management. Its high performance is paired with cost drivers—verbosity and premium pricing—that can be mitigated. By implementing specific strategies, you can harness the model's power without incurring unexpected expenses.
The single most effective cost-control measure is to counteract the model's natural verbosity. Since output tokens are significantly more expensive than input tokens, controlling the length of the response is key.
Your choice of API provider has a direct impact on both performance and cost. Do not default to a single provider for all use cases.
The 200k context window is a powerful feature, but also a significant cost driver if misused. Avoid filling the context window unless absolutely necessary.
Providers like Parasail (FP8) and Deepinfra (FP4) offer lower prices by using quantized models. Quantization reduces the precision of the model's weights, making it smaller and faster to run, with the savings passed on to you.
GLM-4.6 (Reasoning) is an open-weight large language model from Z AI. It is specifically fine-tuned to excel at tasks requiring logical deduction, complex instruction following, and multi-step problem-solving. It combines high intelligence with very fast inference speeds and a large 200,000-token context window.
GLM-4.6 generally ranks higher in intelligence and speed than many other open-weight models in its class. Its key differentiators are its specialized reasoning capabilities and its massive context window. However, this performance comes at the cost of being more verbose and having slightly higher per-token prices than the average open model.
The "Reasoning" tag indicates that the base model has undergone additional training (fine-tuning) on datasets designed to improve its performance on cognitive tasks. This includes mathematical word problems, logic puzzles, code generation, and following complex, multi-part instructions. It is optimized for 'thinking' rather than just 'writing'.
The 200k context window is a double-edged sword. It is incredibly powerful for specific use cases, such as analyzing an entire book, a long legal contract, or a complex codebase in one go. However, it is also very expensive to fully utilize. For most common tasks, like simple Q&A or short-form content creation, this large context is unnecessary and can lead to needlessly high costs if not managed properly.
High verbosity is a characteristic of the model's training and architecture. It may have been trained to provide thorough, step-by-step explanations, which is beneficial for its reasoning capabilities but results in long outputs. This is a known trait that can be managed by providing explicit length constraints and formatting instructions in your prompts.
Quantization is a process that reduces the precision of the numbers (weights) inside the model. This makes the model smaller, faster, and cheaper to run. FP8 (8-bit floating point) and FP4 (4-bit floating point) are different levels of this process. Providers like Deepinfra and Parasail offer these versions at a lower price. You should consider using them if cost is a major concern, but it's recommended to test them against the standard version to ensure there's no noticeable drop in quality for your specific application.