GLM 5.2
glm-5.2Compare glm-5.2 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
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens |
|---|---|---|
$1.4 | $4.4 | $0.26 |
- Input/ 1M tokens
- $1.4
- Output/ 1M tokens
- $4.4
- Cache Read/ 1M tokens
- $0.26
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens |
|---|---|---|
$1.288 | $4.048 | $0.2392 |
- Input/ 1M tokens
- $1.288
$1.4 - Output/ 1M tokens
- $4.048
$4.4 - Cache Read/ 1M tokens
- $0.2392
$0.26
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens |
|---|---|---|
$1.4 | $4.4 | $0.26 |
- Input/ 1M tokens
- $1.4
- Output/ 1M tokens
- $4.4
- Cache Read/ 1M tokens
- $0.26
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens |
|---|---|---|
$0.63 | $1.98 | $0.117 |
- Input/ 1M tokens
- $0.63
$1.4 - Output/ 1M tokens
- $1.98
$4.4 - Cache Read/ 1M tokens
- $0.117
$0.26
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
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
适合把一项工程任务持续做下去
GLM-5.2 是智谱面向长流程工作的推理模型，接收和输出文本。它适合需要阅读大量项目背景、连续调用工具并反复验证的任务，例如跨模块功能开发、代码迁移、复杂排障和自动化工作流。
项目级开发往往不止是写出一段函数，还要理解已有接口、处理依赖、遵循工程规范并完成验证。可以让 GLM-5.2 先阅读需求与关键代码，整理影响范围，再按步骤实施和检查。长上下文有助于同时保留设计约定、历史讨论和工具结果，减少任务推进时反复补充背景的负担。
它强调编程与工具使用能力，也提供不同的推理投入选择。遇到跨服务故障、相互冲突的需求或复杂算法问题时，可以增加思考深度；明确而局部的改动则应给出清晰范围，避免把简单任务扩展成不必要的重构。在具备文件读取、命令执行等工具的开发助手中，它可以根据实际反馈调整后续步骤。
长流程仍需要明确的完成标准。开始前列出必须满足的行为、兼容条件和检查方法；执行中保留已完成、待验证和受阻事项；交付时要求说明实际修改与验证结果。模型能提出部署或操作方案，是否已经执行成功，应以工具返回和环境中的真实状态判断。
Use cases and prompting
代码迁移提示词
“请将这个项目的数据访问模块迁移到新接口。先阅读旧接口与调用位置，列出行为差异和受影响文件，再按模块完成修改。保留现有错误处理与兼容约定。每一步执行后检查结果，最后汇总已通过的验证和仍需人工决定的问题。”
常见问题
- 项目材料很多，应该先给什么？先给架构说明、目标模块和接口约定，再按依赖补充代码，减少无关内容干扰。
- 长任务怎样方便接续？阶段结束时保存目标、约束、修改位置和未完成检查，让下一轮从明确状态继续。
- 能直接分析界面截图吗？该模型以文本输入为主，界面问题可提供页面结构、报错文本及相关代码。
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 |
|---|---|---|---|
| Volcengine | Unlimited | Unlimited | Unlimited |
| 火山引擎特价 | Unlimited | Unlimited | Unlimited |
| Alibaba | Unlimited | Unlimited | Unlimited |
| official | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about glm-5.2
What is glm-5.2?
Compare glm-5.2 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call glm-5.2?
Create an API key with access to glm-5.2, 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 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?
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?
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 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.