Z.AI: GLM 5.3在线

GLM-5.3 was developed by Z.ai, also known as Zhipu AI. GLM-5.3 is Z.ai’s latest flagship language model for complex software engineering, coding agents, tool use, and long-horizon tasks. It was announced on August 14, 2026, as the successor to GLM-5.2.

According to Z.ai, GLM-5.3 uses the same base model as GLM-5.2, with its performance improvements coming from expanded post-training on real-world engineering workflows. The model is designed to handle longer processes involving planning, implementation, testing, debugging, and delivery. Checking the live 开桥 route page for GLM-5.3 availability, pricing, limits, licensing, and API behavior.

GLM-5.3 supports a context length of up to 1 million tokens and a maximum output length of 128,000 tokens, according to Z.ai’s official model documentation.

The extended context window can support large codebases, long technical documents, repository-level analysis, extended conversations, retrieval-augmented generation, and multi-step agent workflows. Actual usable input length may also depend on the provider route, output allocation, latency requirements, and API configuration. GLM-5.3 model documentation

When accessing GLM-5.3 through 开桥, developers should use the model ID and context configuration shown on the live 开桥 GLM-5.3 model page. Model identifiers used by other platforms may not work with the 开桥 API.

GLM-5.3 is primarily designed for coding, agentic engineering, and complex technical tasks. Suitable use cases include:

• Navigating and modifying large codebases
• Generating, reviewing, and refactoring code
• Debugging issues across multiple files or services
• Running long-horizon software-engineering workflows
• Using terminals, APIs, MCP servers, and other external tools
• Producing structured technical documentation
• Conducting authorized vulnerability research and defensive code review

No model is automatically the best choice for every workload. Before production deployment, teams should evaluate GLM-5.3 on representative tasks for output accuracy, code correctness, long-context reliability, latency, throughput, and total token cost.

Z.ai reports that GLM-5.3 scored 28.3 on Terminal-Bench 3.0, compared with 4.6 for GLM-5.2. Its reported score increased from 46.2 to 66.9 on DeepSWE v1.1 and from 23.8 to 28.5 on Agents’ Last Exam (CLI).

Z.ai also reports a 50% improvement over GLM-5.2 on its internal Z.ai Code Bench. GLM-5.3 also scored 84.5% on CyberGym for vulnerability discovery and 54.4% on ExploitBench, according to Z.ai. Z.ai notes that its current strengths are concentrated in vulnerability discovery and the earlier stages of the exploitation chain, so it should only be used for authorized security testing and defensive research. These results indicate stronger performance in terminal interaction, long-horizon software engineering, and agent execution.

When comparing GLM-5.3 with Claude, GPT, Qwen, Kimi, or DeepSeek models, teams should use the same tasks, execution environment, reasoning settings, tool permissions, and token budgets. Vendor-reported benchmarks do not establish that GLM-5.3—or any other model—is universally better across quality, cost, latency, and reliability.

The figures are vendor-reported results rather than independent 开桥 evaluations. Benchmark comparisons should account for the evaluation harness, tool configuration, reasoning effort, token budget, and scoring methodology. Official GLM-5.3 benchmark results

Z.ai has announced that GLM-5.3 will be released as open weights. At the time of Z.ai’s August 14 announcement, the weights and license were not yet publicly available; Z.ai said they were expected approximately two weeks after launch, following additional safety evaluation and hardening. Until the official repository and license are available, GLM-5.3 should be described as announced for an open-weight release rather than available for self-hosting. Official GLM-5.3 announcement

开桥 lists GLM-5.3 at the following rates:

• Input: $1.40 per 1M tokens
• Cached input: $0.26 per 1M tokens
• Cache write: Not listed per 1M tokens
• Output: $4.40 per 1M tokens

There is no separately documented foundation model called “GLM-5.3 Pro.” The term “Pro” may refer to a subscription or service tier offered by another platform, rather than a different version of the GLM-5.3 model. On 开桥, developers access the listed GLM-5.3 model through the available provider routes.

Developers can access GLM-5.3 through the 开桥 unified API using Claude Code, Cline, Codex, OpenCode, and other compatible tools.

Configure your client with a 开桥 API key, the model ID z-ai/glm-5.3, and the appropriate base URL:

• Anthropic-compatible tools: https://openqiao.com/api/anthropic
• OpenAI-compatible tools: https://openqiao.com/api/v1

开桥 provides GLM-5.3 through TencentCloud and BigModel routes. The model supports messages, chat.completions, and responses, with a 1M-token context window and up to 128K output tokens. Check the live 开桥 model page and integration guide for current route details and tool-specific configuration.

Yes. Z.ai officially documents function calling and support for agent-oriented tool use for GLM-5.3. Through a compatible agent framework, these capabilities allow the model to inspect repositories, call external APIs, execute permitted terminal commands, process tool results, and iteratively update code. GLM-5.3 capabilities

A production agent still requires safeguards outside the model, including tool permissions, argument validation, logging, retry handling, execution limits, and human approval for sensitive operations.

No. Z.ai currently documents GLM-5.3 as a text-input, text-output model. Native image and video input are not listed among its supported modalities.

On 开桥, developers should use GLM-5.3 for text-based workloads such as coding, software-engineering agents, long-context analysis, tool use, and cybersecurity tasks. For image or video processing, select a multimodal model whose 开桥 model page explicitly lists the required input type. Model capabilities can vary by route and may change over time, so developers should check the live 开桥 model page before sending non-text content.

Z.ai officially documents streaming output, structured JSON output, function calling, and context caching as GLM-5.3 capabilities. GLM-5.3 documentation

However, the capabilities available through 开桥 may depend on the selected provider route and supported API protocol. 开桥 lists streaming and tool/function calling for GLM-5.3. Its chat.completions protocol lists response_format, and both listed routes show a cache-read price; caching behavior depends on the selected route and protocol.

Developers should verify the live GLM-5.3 route details before relying on prompt-cache behavior or cached-input pricing in a production cost model.