GLM 5.2 260617
glm-5-2-260617Compare glm-5-2-260617 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 1M
- Maximum input window
- Max output
- 131.1K
- 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 5.2 260617
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 5.2 260617
带着整个项目背景推进开发
GLM-5.2 是面向长程任务的文本模型，适合需要理解多份代码、接口契约和工程约束的开发工作。它可以先梳理系统结构，再协助处理模块解耦、接口迁移、SDK 适配和跨语言重构，让后续修改有明确的项目背景。
百万级 Token 上下文有助于容纳较完整的工程材料。与其只贴出报错的一行，可以提供相关调用链、数据结构、测试和编码规范，让模型判断修改会影响哪些位置。长上下文仍需要组织：标清文件路径、当前版本和关键限制，能让分析更容易追踪。
怎样让跨文件修改可检查？
先要求它列出模块职责、依赖和计划，再按阶段提交修改。每一阶段写清保留的接口行为、涉及文件和验证方式；对于迁移任务，尤其要检查旧调用方、配置与测试是否一起调整。让真实构建和测试结果驱动下一轮修正，比只阅读一份完整方案更有助于发现问题。
它可以自己运行项目吗？
工具调用需要编程应用实际提供命令执行、文件访问等能力。模型给出命令或补丁时，表示已经形成操作内容；只有工具执行并返回结果，才能说明项目确实运行过。移动端和小程序任务也需要相应设备、平台工具和运行日志配合验证。
适合从研究材料做原型吗？
可以辅助把算法说明、数据流程和实验要求转化为工程实现。应把已知设计与待补充假设分开，并通过可运行代码和指标检查逐步验证，避免把“代码看起来完整”当作复现已经成功。
Use cases and prompting
提交项目结构、相关源文件与工程要求，例如：“把当前通知模块从业务处理函数中解耦，保持消息内容、调用接口和失败重试行为一致。先画出调用关系，提出最小迁移步骤，再逐步修改；完成后检查原有测试，并验证重复发送和失败恢复。”
接入编程工具时明确文件范围、依赖限制和允许执行的命令。使用接口支持的思考设置，并给长任务留出足够输出预算。每阶段保留已完成、未验证和剩余事项，最终查看实际差异与运行结果，再决定是否合入。
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 |
|---|---|---|---|
| official | Unlimited | Unlimited | Unlimited |
| Volcengine | Unlimited | Unlimited | Unlimited |
| 火山引擎特价 | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about glm-5-2-260617
What is glm-5-2-260617?
Compare glm-5-2-260617 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call glm-5-2-260617?
Create an API key with access to glm-5-2-260617, 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-5-2-260617 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-5-2-260617?
The model catalog lists a context window of 1048576 tokens. Check the selected endpoint for request limits.
What is the maximum output of glm-5-2-260617?
The model catalog lists a maximum output of 131072 tokens. Your request settings may set a lower limit.
How should I evaluate glm-5-2-260617 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.