Short answer
GLM-4.6 is a text-in, text-out model from Z.ai with a documented 200K-token context limit and 128K maximum output. Z.ai documents coding, reasoning, and tool-use capabilities, and provides a production API model identifier: glm-4.6. Its published standard API price is $0.60 per million input tokens and $2.20 per million output tokens (USD). Those facts make it a candidate for text-based coding, long-context, and tool-using workflows—but they do not establish that it will perform well enough for your particular task. Run a comparison on representative work before committing. (docs.z.ai)
Bottom line: consider GLM-4.6 when its documented limits, API integration, and task-test results fit your needs. Don’t select it on broad claims of coding or agent performance alone. Z.ai’s price list also includes other GLM models, so compare alternatives if you are choosing a model from scratch rather than maintaining a GLM-4.6 integration. (docs.z.ai)
What GLM-4.6 is—and what its specs mean
Z.ai’s model documentation identifies GLM-4.6 as a text-only input and output model. It lists a 200K-token context length and 128K maximum output. The context figure is the model’s stated upper limit, not a promise that every request can use the full amount: the prompt and generated output must fit within applicable limits, and your account or request settings may impose additional constraints. (docs.z.ai)
The name matters: GLM-4.6 is not GLM-4.6V. Z.ai documents GLM-4.6V separately as a vision-language model with image, video, text, and file input. If your task requires images or video, don’t assume the text-only GLM-4.6 supports those inputs; assess the separate V model and its own API documentation. (docs.z.ai)
Z.ai describes GLM-4.6 as supporting coding, reasoning, tool use, search-based agents, and writing. Its migration guide gives the API model name as glm-4.6 and says deep thinking is enabled by default. These are documented capabilities; they do not guarantee accuracy, successful tool execution, or a particular response time in your application. (docs.z.ai)
When to consider GLM-4.6
| Your workload | Why it may be worth testing | What to measure before choosing |
|---|---|---|
| Coding and code review | Z.ai documents coding capabilities and publishes coding evaluations. |
Whether it fixes your actual issues, passes your tests, follows repository conventions, and uses an acceptable number of tokens. |
| Long text or code context | The documented context limit is 200K tokens. | Whether your real prompts fit, whether relevant details are retained across long inputs, and whether quality holds up on your tasks. |
| Tool-using workflows | Z.ai documents tool use and streamed tool-call support. |
Whether calls use the right tool and arguments, recover from tool errors, and finish reliably in your own orchestration. |
| Image or video understanding | The standard GLM-4.6 page says input and output are text. | Evaluate a separate multimodal model, such as GLM-4.6V, rather than treating GLM-4.6 as a vision model. |
Z.ai reports a 48.6% win rate against Claude Sonnet 4 in its comparison. This is a vendor-reported result, not an independent guarantee or a direct prediction for your workload. Treat it as a reason to test—not as proof that GLM-4.6 is the best choice for your team. (z.ai)
A practical, reproducible fit test
Use a small set of real tasks that reflects your deployment: for example, bug fixes with known tests, questions over long documents with answer keys, or tool calls where the expected function and arguments are clear. Run the same inputs through GLM-4.6 and the alternative you are considering. Keep prompts, tools, and evaluation conditions consistent; repeat tasks where output variation matters.
Score observable outcomes: task success, factual or test errors, invalid tool calls, latency, input/output token use, and estimated cost. Include difficult cases and failures, not just examples where the model succeeds. If the result changes when you alter the prompt or settings, record that too. This test plan is a recommendation, not a claim that a particular test was run here.
Production API: identifier, limits, and price
Z.ai’s migration documentation identifies the API model name as glm-4.6 and documents the model’s token limits. For the standard model API, use Z.ai’s current quick-start documentation to confirm the base URL, authentication, and request format for your account. Standard model API credentials and coding-plan credentials are separate and are not interchangeable. (docs.z.ai · docs.z.ai)
Z.ai’s published price list gives these GLM-4.6 rates:
- Input: $0.60 per 1 million tokens
- Output: $2.20 per 1 million tokens
The price list is denominated in USD and labels model prices per million tokens. Confirm the applicable account terms and any additional charges before estimating production spend; the figures above are token charges, not a subscription price. The price page also lists cached-input and cached-storage columns, but this article uses only the standard input and output rates. (docs.z.ai)
Illustrative calculation: a request using 100,000 input tokens and generating 20,000 output tokens would cost about $0.104 at those listed rates: (100,000 ÷ 1,000,000 × $0.60) + (20,000 ÷ 1,000,000 × $2.20). This is a calculation from the published rates, not a measured API bill; it excludes any different treatment of cached tokens or other applicable charges.
The cited model and pricing information does not establish a universal throughput quota or rate limit for every account. Check the limit applicable to your API account and deployment before planning peak traffic. A model’s context and output limits are not the same thing as requests-per-minute or tokens-per-minute limits. (docs.z.ai)
Z.ai’s documentation discusses streamed tool-call output, but this guide does not prescribe exact streaming flags or a runnable client configuration. Confirm the parameters and event handling in the stream tool documentation and the current standard API quick start before implementing them. (docs.z.ai)
How to decide
Choose GLM-4.6 for further evaluation if you need a text model, its stated context/output limits fit your request design, the standard API integration works for your account, and it meets your quality, reliability, latency, and cost thresholds on representative tasks.
Look elsewhere or compare alternatives if you need native image/video input from this exact model, if your workload exceeds the documented limits, or if another model performs better in your own evaluation. Z.ai’s listed catalog includes other GLM models; their presence is a reason to compare, not evidence that a different model will be better for your task. (docs.z.ai)
Before production, confirm the exact model is selectable with your credentials, validate the account’s request limits, test failure and retry behavior, and estimate costs using your expected input/output token mix. That distinguishes “the API exists” from “this API setup meets my production requirements.”
Sources
GLM-4.6 — Z.AI Developer Documentation
Migrate to GLM-4.6 — Z.AI Developer Documentation
Pricing — Z.AI Developer Documentation
Quick Start — Z.AI Developer Documentation
Quick Start — Z.AI Developer Documentation
Stream Tool Call — Z.AI Developer Documentation
GLM-4.6: Advanced Agentic, Reasoning and Coding Capabilities