GLM 5
glm-5Compare glm-5 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 204.8K
- Maximum input window
- Max output
- 128K
- Maximum tokens per response
- Modalities
- →
Pricing by Supplier
Capabilities / Supported modalities
Provider & data privacy
GLM tokenizerPerformance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·GLM 5
This model is not included in the current benchmark snapshot.
Missing models or measurements are not zero scores.
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About GLM 5
先理解系统，再解决局部问题
GLM-5关注复杂系统工程和长周期智能体任务，适合需要同时考虑架构、接口、状态与异常处理的工作。它可以帮助研发团队梳理模块关系、分析故障路径、规划功能实现，也可以围绕一组资料完成持续的推理与信息整合。
当问题横跨多个组件时，单看报错位置往往不够。可以把请求入口、业务逻辑、数据访问和任务处理的相关实现放在一起，让模型沿实际流程判断原因。描述目标时，应说明正常行为、失败现象与必须保留的约束，这能让方案更贴近已有系统。
适合有反馈的工作方式
GLM-5可以参与代码生成和工具协作，但有效的工程流程仍需要真实反馈。应用执行查询、编译或测试后，应把输出交回模型，保留失败信息，让下一轮改动围绕实际问题展开。先做一个最小验证，再逐步扩大改动范围，有助于减少无关重构。
模型以文本输入和输出为主，适合代码、日志、文档和结构化信息。面对长任务，可以记录已确认事实、实施步骤和待验证假设；完成时要求说明实际产物与验证结果，方便团队继续审阅和维护。
Use cases and prompting
异步任务设计示例
“为图片处理服务设计异步任务流程。要求提交后返回任务标识，处理中可查询状态，失败可安全重试。结合现有代码说明状态如何流转，怎样避免重复处理，哪些错误可以重试，并给出最小实施顺序和验证用例。”
把现有接口、队列行为与数据约束一起提供，避免得到脱离项目的通用方案。
生成的方案太大，怎样收敛？
明确本次只解决的问题、允许修改的模块和交付期限，要求先完成最小可用流程。
怎样检查它有没有真正理解系统？
先让它复述一次完整请求路径，并指出关键状态和失败点，再开始改动；发现理解偏差时及时补充材料。
API access
API documentation
Code samples
/v1/chat/completions| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| Alibaba | Unlimited | Unlimited | Unlimited |
| official | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about glm-5
What is glm-5?
Compare glm-5 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call glm-5?
Create an API key with access to glm-5, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is glm-5 priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of glm-5?
The model catalog lists a context window of 204800 tokens. Check the selected endpoint for request limits.
What is the maximum output of glm-5?
The model catalog lists a maximum output of 128000 tokens. Your request settings may set a lower limit.
How should I evaluate glm-5 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.