GLM-5.1 API

Use Z.ai’s open-weight coding and agent model through GPTProto with a 200K context window, pay-as-you-go token billing, and one balance for 200+ AI models.

What Is the GLM-5.1 API?

GLM-5.1 is a text-to-text foundation model released by Z.ai for agentic engineering. Rather than focusing only on one-shot code generation, it is designed for workflows that repeatedly plan, call tools, inspect results, revise an approach, and verify an outcome. Z.ai highlights agentic coding, complex instruction following, front-end development, document work, and long-horizon engineering as primary uses.

The hosted GLM-5.1 API gives applications access to the model without deploying its 754B-parameter weights. Its official specification lists a 200K-token context window and a maximum output of 128K tokens. The model also supports thinking modes, response streaming, function calling, context caching, structured output, and MCP integration. Parameter availability can differ by API route, so production integrations should use the request fields documented in the GPTProto Quick Start rather than copying an official Z.ai payload unchanged.

Where GLM-5.1 Fits Best

GLM-5.1 is most relevant when a task needs more than a single completion. A coding agent can use it to break down a repository change, inspect files through tools, implement edits, run tests, read failures, and iterate. For repo-generation work, the model can turn requirements into a multi-file project while maintaining interfaces and dependencies across the generated code. It can also support terminal-oriented workflows in which the surrounding agent—not the model alone—executes commands and returns logs for the next reasoning step.

That distinction matters: an API call does not independently browse a repository, run a shell, or change files. Your application must supply the tools, permissions, relevant context, and validation loop. Treat function calls as proposals from the model, validate arguments before execution, and return concise tool results. For high-risk changes, require tests and human review before deployment.

GLM-5.1 vs GLM-5.2: Which API Should You Use?

GLM-5.2 is the current successor and expands the context window from 200K to 1M tokens. Both versions list a maximum output of 128K, and Z.ai currently lists the same official token price for each: $1.40/M input and $4.40/M output. This makes the choice less about provider list price and more about workload requirements and model-version stability.

Choose GLM-5.1 when your prompts, tool schemas, regression tests, and expected outputs have already been evaluated against the glm-5.1 model string. Keeping the version fixed avoids introducing a behavior change during an active release. For a new project, or for project-scale repository work that can use more than 200K tokens, evaluate the GLM-5.2 API first.

Open Weights or a Hosted GLM-5.1 API?

GLM-5.1 is often described as open source, but “MIT-licensed open weights” is the more precise description. Z.ai publishes the 754B-parameter model weights under the MIT license, so teams with suitable infrastructure can deploy and operate the model themselves. Self-hosting gives more control over inference infrastructure and deployment policy, but it also shifts capacity planning, serving software, monitoring, upgrades, and reliability to the operator.

Using the GLM-5.1 API is the managed alternative. GPTProto handles the hosted access and usage billing, while your application sends requests with the glm-5.1 model string. The API does not change who developed the model or the license of the downloadable weights. It changes how you obtain inference: no local weight deployment, a pay-as-you-go balance, and the option to call other integrated models through the same GPTProto account.

Practical Guidance for Reliable Agent Runs

Start with a bounded objective and an explicit definition of done. Provide only the files, interfaces, and logs needed for the current step; a 200K context limit is capacity, not a requirement to fill every request. Separate planning, editing, and verification so the model can see whether each stage succeeded before moving on.

For code changes, include repository conventions, prohibited actions, build commands, and required tests. Ask the model to identify the affected files before editing, then return tool results in a consistent format. Validate structured output against a schema and treat every proposed shell command or external action as untrusted input until your application approves it. These controls are important even when a benchmark shows strong coding performance: benchmark scores measure a defined evaluation setup, not the safety or correctness of every production call.

Z.ai reports a 58.4 score for GLM-5.1 on SWE-Bench Pro, 42.7 on NL2Repo, and 63.5 on Terminal-Bench 2.0 with Terminus-2. Use those figures as model-selection signals, then run a private evaluation with your own languages, repositories, tool definitions, latency limits, and acceptance tests before routing production traffic.

Access GLM-5.1 Through One GPTProto Key

The model string on this page is glm-5.1. When moving an existing Z.ai integration, use the authentication and base URL shown in the GPTProto Quick Start; do not leave the official Z.ai endpoint in your client. Re-test thinking-mode fields, streaming events, tool-call arguments, and structured-output handling because gateway payload support can differ.

GPTProto lists the GLM-5.1 API at $1.26 per million input tokens and $3.96 per million output tokens, 10% below Z.ai’s current list rates. The same GPTProto account and balance can also be used for the platform’s broader AI model catalog , which reduces separate provider accounts and prepaid balances when an application needs fallbacks or multiple model families.