GLM 5.3
glm-5.3Compare glm-5.3 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 1M
- Maximum input window
- Max output
- 943.7K
- 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 5.3
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 5.3
处理一段代码之外的工程问题
GLM-5.3 是面向复杂软件工程和长周期智能体任务的推理模型。它适合阅读项目结构后制定改造方案，分析模块间依赖，处理代码迁移，或围绕既定目标持续完成实现与验证。对于需要把很多细节同时放在心里的工作，它可以帮助组织分析顺序和实施步骤。
长材料要有清楚的任务主线
模型支持文本输入和输出，可结合代码、接口说明、变更记录与错误日志进行分析。代码迁移时，先说明要保留的行为和兼容范围，再提供目标环境与验收条件；问题排查时，则保留调用路径和失败案例，让结论能对应具体证据。
跨模块工作适合按依赖顺序推进。先识别影响面，再完成最小可验证改动，检查通过后继续下一部分。应用接入执行工具时，应把编译结果、测试失败和运行状态交回，帮助模型修正后续计划。没有执行条件时，也应区分建议方案与已验证结果。
始终开启的推理
GLM-5.3的推理始终开启，可用low、high和max调整投入。简单任务可以减少分析深度，复杂工程任务则需要留出足够验证空间。长流程中保存关键决策、已完成修改与剩余风险，可以减少重复工作，也便于团队在中途审阅方向。
Use cases and prompting
代码迁移示例
“将现有文件存储模块迁移到新的客户端库。先找出所有调用方和行为差异，保留上传、下载、重试及错误返回约定。按依赖顺序分步改动，每步验证成功路径、超时和权限失败，最后列出仍依赖旧库的位置。”
提供库版本、相关代码与兼容要求，避免只给一个笼统迁移目标。
可以完全关闭推理吗？
GLM-5.3始终启用推理，可选择low、high或max调整强度。
多模块改造怎样避免遗漏？
先建立调用清单和验收项，分阶段记录已修改位置与验证结果。最终对照清单检查，不能只以代码生成完成作为结束标志。
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 |
No restriction
Frequently asked questions about glm-5.3
What is glm-5.3?
Compare glm-5.3 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call glm-5.3?
Create an API key with access to glm-5.3, 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-5.3 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-5.3?
The model catalog lists a context window of 1048576 tokens. Check the selected endpoint for request limits.
What is the maximum output of glm-5.3?
The model catalog lists a maximum output of 943718 tokens. Your request settings may set a lower limit.
How should I evaluate glm-5.3 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.