GLM-4.6 is a text-based large language model launched by Zhipu AI, balancing long-context processing, practical programming, reasoning, and natural writing. It is suited to analyzing requirements, materials, and historical discussions together, then delivering code, summaries, or copy. Compared with GLM-4.5, it expands the context window and improves frontend generation, tool collaboration, and multi-turn role expression.
Clarify capacity, input and output, and invocation methods before selecting a model.
The context, output limit, and thinking toggle are native specifications; this platform provides text generation and conversation workflows, with invocation parameters and length budgets applied according to the selected endpoint.
Discover what glm-4.6 can bring to your work.
GLM-4.6 is suitable not only for completing short functions, but also for organizing modification plans around requirement specifications, existing code, and error messages. It supports mainstream programming languages and emphasizes the visual effects and logical layout of frontend pages, making it suitable for first generating page or feature drafts and then refining interactions and implementation through multiple rounds of feedback.
A larger context window makes it easier to process multiple materials, project constraints, and discussion history at once, reducing the need to split materials frequently. The model supports using tools during reasoning and can be used for task decomposition, search result integration, and cross-tool collaboration; performing searches or business operations requires the corresponding tools and runtime environment.
It emphasizes readability, style adaptation, and consistency of characters across multiple rounds, making it suitable for ongoing revisions of novels, scripts, and brand copy. For translation, it is optimized for French, Russian, Japanese, Korean, and informal expressions, and can incorporate glossaries and tone requirements to preserve semantic coherence and localized expression in long passages.
Start with specific tasks to find where the model can be effective.
Provide page goals, component requirements, existing code, and acceptance criteria, and have the model deliver layout code, interaction logic, and modification notes. Then add textual descriptions of screenshots or test errors to continue refining the solution. Suitable for product prototyping and development assistance; generated code still needs to be tested in the project environment.
Organize document text, meeting notes, and analytical questions into text, and ask the model to extract themes, compare viewpoints, and generate a report draft. You can specify chapter structure and original-source citation requirements to obtain summaries, difference lists, and items requiring confirmation; key conclusions should retain their corresponding materials for subsequent human review.
Provide product descriptions, target languages, glossaries, and brand tone to generate localized copy or short-drama translations; you can also provide character settings and conversation history to continuously create dialogue. Deliverables are primarily text, suitable for further polishing by editorial teams, without equating translation or character writing with speech synthesis.
Choose based on task complexity, input materials, and expected results.
If tasks often involve long materials, code, and multi-step reasoning at the same time, GLM-4.6 is worth considering: native context has been expanded from GLM-4.5's 128K to 200K, with improvements to practical programming and tool use. For short Q&A or fixed-template tasks, it is recommended to compare delivery quality using your own examples; there is no need to switch solely because of a version update.
For general conversation, content creation, and applications that balance text analysis with code assistance, choose GLM-4.6 first. If the core task is code generation, tool calling, or Agent orchestration, include GLM-4.7 in comparative testing. Focus on modification correctness, tool completion, and output style; do not apply specifications from newer series versions to GLM-4.6.
From a small-scale task to production integration.
Define objectives, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand output quality and capability boundaries.
Answers to common questions about using glm-4.6.
Its native input and output are both text, making it suitable for Q&A, code, writing, and text analysis. Use models with the corresponding capabilities for image recognition or speech generation; even if the call structure includes image or audio fields, this does not mean GLM-4.6 has these capabilities.
No. The context window describes the overall text range available for a task, while the native maximum output is separately 128K tokens. Actual requests must also account for input, conversation history, and response budget; for long-material tasks, first define a summary or chapter objective to avoid requesting too much content at once.
Native usage provides thinking.type, which can be set to enabled or disabled and is enabled by default. It is not the same control method as reasoning_effort and should not be used interchangeably. When designing applications, distinguish between native thinking settings and the reasoning parameters of the selected calling endpoint.
When using /glm/chat/completions, submit the necessary history in order in messages and read the assistant's response text. If you want managed sessions, you can use the two conversations endpoints, enable stateful, and include the returned id in subsequent requests to continue the discussion.
Tool-calling capability means the model can select tools and organize call parameters; it does not mean every query will access the internet. Direct generation endpoints require declaring tools and handling execution results; when using session workflows with tools, you should also clearly specify retrieval objectives, authorization scope, and required deliverables.
Model information · Updated: 2026-10-01. For calling parameters and billing rules, see the API and pricing sections.
Start with clear goals and determine from actual results whether it suits your work.
Input
$0.35per 1M tokens
Official $0.35
Output
$0.53per 1M tokens
Official $0.53
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
View Docs