GLM-4.6 at a glance
GLM-4.6 is Z.ai’s text-in/text-out language model for coding, reasoning, long-context tasks, and tool-using agent workflows. Z.ai’s documentation lists a 200K-token context window and up to 128K output tokens. Z.ai provides a documented standard API route; that does not establish that TokenForge Cloud offers the model. Z.ai lists standard API pricing at $0.60 per million input tokens and $2.20 per million output tokens, with cached input listed at $0.11 per million tokens. (docs.z.ai)
This guide is scoped to the Z.ai standard API and the information available on November 9, 2026. Treat capability statements and evaluation results as Z.ai’s claims, not a guarantee of results on your application.
What GLM-4.6 is designed to do
Z.ai describes GLM-4.6 as an improvement over GLM-4.5 in coding, reasoning, long-context processing, and agent tasks, including tool use. Its documentation lists text as both the input and output modality. So GLM-4.6 may be worth evaluating when your application needs text-based coding help, reasoning over long prompts, or a model that can participate in tool-calling workflows. It is not the GLM-4.6V vision model; the GLM-4.6 page lists text input and output. (docs.z.ai)
Specifications Z.ai documents:
- Context window: up to 200K tokens.
- Maximum output: up to 128K tokens.
- Input/output: text.
- Request model identifier:
glm-4.6.
The context limit is a ceiling, not a promise that every application prompt should use the full window. Very long inputs can increase cost and may not improve a task’s results. Set a practical output cap for your use case, and test long-context quality on your own material. (docs.z.ai)
What the evaluation evidence tells you—and doesn’t
Benchmark results and provider-reported win rates are useful only when the evaluation setup, compared model versions, and task mix are clear. The evidence available for this guide does not establish those details sufficiently to use a headline score for model selection. Treat the capability descriptions above as intended-use claims, not proof of performance on your workload.
For a selection decision, run a small, repeatable evaluation using representative tasks. For a coding agent, for example, compare whether each model completes the same bug fixes or feature requests, follows repository conventions, uses tools correctly, and passes your tests. Track task success, review effort, latency, and token use—not just a benchmark headline.
Production API access through Z.ai
Z.ai’s GLM-4.6 release information says the model is available through its API platform, and its migration documentation identifies the request model as glm-4.6. The standard API quick start documents the chat-completions route, bearer-token authentication, and a request body with a model and messages. (z.ai)
A minimal cURL smoke test, combining those documented details, is:
curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" \
-H "Authorization: Bearer $ZAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4.6",
"messages": [{"role": "user", "content": "Reply with: GLM-4.6 API test passed"}]
}'
Set ZAI_API_KEY to a standard Z.ai API key before running the command; a successful response should contain a chat completion. Keep the key in a secret manager or environment variable, not source code. Before production rollout, also validate your actual request format, error handling, streaming or tool-call behavior if used, rate limits, and account access in the current Z.ai documentation.
Do not confuse the standard model API with the GLM Coding Plan. Z.ai says keys for its standard API and Coding Plan are separate and not interchangeable; its Coding Plan documentation limits calls to supported tools and plan quotas. Choose the product and credential type that match your integration. (docs.z.ai)
TokenForge Cloud availability is unconfirmed here. The available research did not establish that TokenForge Cloud currently exposes GLM-4.6, nor did it establish a TokenForge model ID, endpoint, or rate. Do not use the Z.ai endpoint or pricing as evidence of TokenForge support. Confirm those details in TokenForge’s own current product documentation before building against it.
Z.ai API pricing and a sample cost
Z.ai’s pricing page lists GLM-4.6 at $0.60 per 1 million input tokens, $0.11 per 1 million cached input tokens, and $2.20 per 1 million output tokens. The page states that prices are in USD. These are Z.ai rates; they should not be transferred to another provider or treated as TokenForge Cloud pricing. (docs.z.ai)
Illustrative calculation, assuming no cached input: a workload with 1 million input tokens and 250,000 output tokens would cost $1.15 at those listed rates: (1 × $0.60) + (0.25 × $2.20). This is a token-charge example, not a forecast; actual spend depends on token volume and whether input qualifies for the listed cached-input rate. It excludes any costs not specified in the cited token-pricing table.
Is GLM-4.6 a fit?
Consider a Z.ai API pilot if you need a text-only model for coding, reasoning, or tool-using tasks and its documented context and output ceilings suit your application. Compare it against alternatives on your own test set if quality, latency, or cost is consequential. If you need image or video input, evaluate a multimodal model such as GLM-4.6V rather than assuming GLM-4.6 accepts those inputs. (docs.z.ai)
Before committing, verify the provider route you will actually deploy, its current access and billing terms, and the model behavior on representative tasks. For TokenForge Cloud specifically, GLM-4.6 support and applicable pricing remain unconfirmed by the information cited here.
Sources
GLM-4.6 Overview — Z.AI Developer Documentation
GLM-4.6: Advanced Agentic, Reasoning and Coding Capabilities — Z.ai
Migrate to GLM-4.6 — Z.AI Developer Documentation
Pricing — Z.AI Developer Documentation
Quick Start — Z.AI Developer Documentation