GLM 5.2 API Guide: GLM-5.2 Pricing, Model ID, 1M Context, and Cost Examples

GLM-5.2 is available through SiliconFlow's Serverless API with a usable 1M-token context window. Developers can access it using the model ID zai-org/GLM-5.2 and pay separately for standard input, cached input, and generated output.

This pricing structure makes cost estimation more detailed than multiplying all tokens by one rate. Developers must consider how much new context they send, how much reusable context qualifies for cache reads, and how long the model's response becomes.

This guide explains the GLM-5.2 API model ID, current SiliconFlow pricing, 1M-context cost implications, three workload examples, and its position against DeepSeek-V4-Pro and DeepSeek-V4-Flash.

Pricing in this guide was checked on June 25, 2026. Model availability and API pricing may change, so verify current SiliconFlow pricing before production deployment.

What Is GLM-5.2?

GLM-5.2 is a flagship language model from Z.ai designed for long-horizon tasks. Its main focus is not simply producing longer answers. It is intended to retain project-level information across extended coding, analysis, and agent workflows.

Z.ai describes GLM-5.2 as a model designed for long-horizon agentic engineering, where the model needs to keep track of large technical contexts across multiple steps. On SiliconFlow, developers can access GLM-5.2 through the Serverless API without managing model deployment, making it easier to test workflows such as repository-level code analysis, cross-file refactoring, tool calling, API migration, and multi-step debugging.

Relevant use cases include:

• Understanding large repositories and their internal relationships

• Refactoring code across multiple files or modules

• Following architectural rules throughout a long task

• Reviewing documentation, configurations, tests, and source code together

• Completing multi-stage workflows involving planning, implementation, verification, and revision

• Calling external tools or functions during agent workflows

The model uses a sparse Mixture-of-Experts architecture and is available through SiliconFlow's pay-as-you-go Serverless service. Developers therefore do not need to deploy or maintain the underlying model infrastructure before testing it.

The 1M-token context is one of the most important GLM 5.2 features. However, context capacity should not be confused with guaranteed task quality. A larger window allows more material to be submitted, but developers still need to control relevance, prompt structure, output limits, and evaluation methods.

GLM-5.2 Model ID and API Access on SiliconFlow

The correct SiliconFlow model ID is:

This exact string must be placed in the model field of an API request. Using only GLM-5.2, GLM 5.2, or another shortened name may cause the request to fail because those are display names rather than the SiliconFlow API identifier.

SiliconFlow provides access through its OpenAI-compatible interface. The base URL is:

The Chat Completions endpoint is:

Developers can use the API through direct HTTP requests or a compatible OpenAI SDK. A typical request contains:

• A SiliconFlow API key

• The zai-org/GLM-5.2 model ID

• One or more messages

• An output limit

• Optional sampling, streaming, or tool parameters

Before integrating the GLM-5.2 API into an application, developers can test it in the SiliconFlow Playground with real prompts and parameters. This helps evaluate response quality, output length, and workflow fit before repeated API calls. SiliconFlow also supports model comparison, so teams can compare GLM-5.2 with other models using similar prompts and configurations before choosing a production setup.

The API model ID should be stored in application configuration rather than repeated across multiple files. This makes it easier to switch models, run comparative tests, or update the identifier if the platform changes its catalogue.

GLM-5.2 Pricing: Input, Output, and Cache Read

SiliconFlow currently lists three GLM-5.2 pricing items:

Standard input refers to billable prompt tokens that are processed as new input. This may include system instructions, user prompts, source code, retrieved documents, conversation history, and tool results.

Cached input uses lower pricing when reusable input qualifies for and hits the platform's context cache. It should not be treated as the default rate for every repeated request. Actual cache eligibility and cache hits depend on how the request is constructed and how the platform handles the repeated prefix.

Output tokens are generated by the model. Because output costs more per token than standard input, an unnecessarily high response limit can materially increase the total bill.

The basic formula is:

Suppose a request contains 80,000 new input tokens, 20,000 cached tokens, and 5,000 output tokens. Its estimated charge is:

This estimate covers model-token charges only. Retries, failed application logic, repeated agent steps, extra tool calls, and other services can increase the final application cost.

What a 1M Context Window Changes in API Cost

A 1M-token context window changes how much information an application can send in one request. It does not change the listed price per token, and it does not mean every request is billed for one million tokens.

A request using 50,000 input tokens is billed for approximately 50,000 input tokens, not the entire context capacity. The larger window only raises the upper limit available to the application.

This distinction matters because long-context applications often behave differently from ordinary chat applications.

A repository assistant may submit source files, architecture documentation, tests, API specifications, and previous decisions together. A document-analysis workflow may send several reports within one prompt. An agent may also accumulate messages and tool results across many steps.

In these cases, total cost depends on actual consumption rather than the advertised window.

Developers should also remember that input, system instructions, conversation history, tool messages, and requested output all require context space. A request should not be designed to fill the entire nominal window without leaving room for model output and processing overhead.

The 1M context may reduce the need to divide a project into many isolated requests, but it does not remove the need for context management. Sending irrelevant content can:

• Raise input cost

• Increase latency

• Make important instructions harder to locate

• Repeat outdated project information

• Produce conflicts between old and current requirements

A better workflow filters the available material before submission and separates stable context from task-specific input. For GLM-5.2 caching, stable content such as system instructions, coding conventions, long reference documents, repository rules, or API specifications can be placed in a consistent prompt prefix when context caching or prompt caching is available. Dynamic content, including the current task, recent code changes, test failures, and user instructions, can then be appended after that stable prefix. This setup may help repeated prompt segments qualify for cache reads, but developers should not assume every repeated token will receive cached-input pricing. How to trigger prompt cache depends on the platform's cache behavior, the request structure, and whether the reusable prefix remains unchanged across calls. For production use, log actual input tokens, cached-input tokens where available, output tokens, retries, and cost per completed task.

For production use, measure both cost per request and cost per completed task. One large request may appear expensive but still cost less than several failed or fragmented requests. Conversely, a full repository upload may be wasteful when retrieval can supply only the files needed for the current step.

Cost Examples for Three Token Workloads

The following examples show how GLM-5.2 API cost changes across three workload sizes. Each example includes an uncached calculation and an idealised calculation in which all input tokens are billed at cached-input pricing.

The cached versions are not promises of actual cache performance. They show the mathematical difference between the two published pricing items.

Small Request: 10K Input and 2K Output

This workload could represent a focused code review, a short document analysis, or a response based on a limited set of retrieved passages.

Without cached input:

With all input billed as cached input:

At this size, output already represents a meaningful share of the bill. Limiting repetitive explanations may be more useful than aggressively reducing a small prompt.

Medium Workload: 100K Input and 10K Output

This example may resemble a multi-file analysis, a technical audit, or a detailed review of specifications and implementation documents.

Without cached input:

With all input billed as cached input:

Repeated medium-sized contexts can create substantial savings when a stable prefix qualifies for cache reads. However, developers should measure actual cache usage rather than budgeting around a theoretical full hit.

Long-Context Workload: 900K Input and 20K Output

This example approaches the model's long-context use case. It may involve a large codebase snapshot, extensive documentation, or a long agent history.

Without cached input:

With all input billed as cached input:

A single long request remains relatively predictable, but costs can multiply quickly inside an agent loop. Ten uncached requests of this size would produce an estimated token charge of $13.48 under the same assumptions.

Applications should therefore track the number of calls, not only the size of one call.

GLM-5.2 vs DeepSeek V4 Pro and Flash: Price and Context

SiliconFlow also provides DeepSeek-V4-Pro and DeepSeek-V4-Flash. All three models are currently listed with a 1049K context length, so context capacity alone does not decide the comparison.

GLM-5.2 has a lower standard input price than DeepSeek-V4-Pro. However, DeepSeek-V4-Pro has lower cached-input and output pricing. The cheaper model therefore depends on the workload's token distribution.

For example, GLM-5.2 may have a pricing advantage when a task contains a high proportion of new input and relatively little output. DeepSeek-V4-Pro may become less expensive when prompts receive substantial cache benefits or produce longer responses.

DeepSeek-V4-Flash is considerably cheaper in all three token categories, making it an attractive candidate for high-volume tasks such as classification, extraction, routing, summarisation, or routine responses.

However, price per token is only one part of model selection. A lower-priced model is not necessarily cheaper per completed task if it requires more retries, additional validation, or repeated corrective prompts.

The most reliable comparison process is to test each model with:

• The same source material

• The same system instructions

• The same output limit

• The same tool configuration

• The same quality rubric

• Several representative tasks rather than one prompt

Teams should measure success rate, latency, output length, retry frequency, and total task cost. This produces a more useful decision than comparing benchmark claims or token prices in isolation.

When GLM-5.2 Is the Better Fit

GLM-5.2 is most relevant when a task depends on retaining engineering context over a long sequence rather than producing one isolated answer.

It may be a stronger fit for project-level code analysis, cross-module refactoring, API migration, test-driven implementation, and workflows that must preserve technical constraints throughout multiple stages.

Consider testing GLM-5.2 when you are building or evaluating long-context AI coding agents and developer tools. Popular examples in this category include Claude Code, Cline, Cursor, and Codex-style coding agents, where the model may need to read repository context, edit files, follow project rules, call tools, and continue a task across multiple steps.

GLM-5.2 may be worth testing when the application needs to:

• Understand relationships across many repository files

• Preserve API contracts and architectural rules

• Track earlier decisions through a long workflow

• Combine source code, tests, configuration, and documentation

• Use tools within multi-step engineering tasks

• Continue refining an implementation after validation failures

It may be unnecessary for simple, high-volume tasks where speed and low token cost are the primary objectives. DeepSeek-V4-Flash is likely to deserve early testing in those cases because its listed prices are substantially lower.

The decision between GLM-5.2 and DeepSeek-V4-Pro requires more direct evaluation. Both offer long context, but their pricing profiles differ. Task success, response length, and cache behaviour can change which model produces the lower total cost.

A practical architecture does not need to send every request to one model. A routing layer can assign routine work to a lower-cost model and reserve GLM-5.2 for tasks that require deeper project context or more sustained engineering execution.

How to Start Using GLM-5.2 on SiliconFlow

Developers can begin by creating a SiliconFlow account, generating an API key, and testing the model in the Playground. Once the prompt and expected output are clear, the same workflow can be moved into an application.

Install or update the OpenAI Python package:

Store the API key in an environment variable rather than placing it directly in source code:

The following Python example sends a basic request through SiliconFlow's OpenAI-compatible interface:

import os

from openai import OpenAI

api_key = os.getenv("SILICONFLOW_API_KEY")

if not api_key:

    raise ValueError(

        "SILICONFLOW_API_KEY is not set. "

        "Add it to your environment before running this script."

    )

client = OpenAI(

    api_key=api_key,

    base_url="https://api.siliconflow.com/v1",

)

try:

    response = client.chat.completions.create(

        model="zai-org/GLM-5.2",

        messages=[

            {

                "role": "system",

                "content": (

                    "You are a senior software engineer. "

                    "Identify technical risks and explain practical fixes."

                ),

            },

            {

                "role": "user",

                "content": (

                    "Review the supplied module architecture and identify "

                    "possible API compatibility risks."

                ),

            },

        ],

        max_tokens=4096,

        stream=False,

    )

    print(response.choices[0].message.content)

    if response.usage:

        print(f"Prompt tokens: {response.usage.prompt_tokens}")

        print(f"Completion tokens: {response.usage.completion_tokens}")

        print(f"Total tokens: {response.usage.total_tokens}")

except Exception as exc:

    print(f"GLM-5.2 API request failed: {exc}")

import os

from openai import OpenAI

api_key = os.getenv("SILICONFLOW_API_KEY")

if not api_key:

    raise ValueError(

        "SILICONFLOW_API_KEY is not set. "

        "Add it to your environment before running this script."

    )

client = OpenAI(

    api_key=api_key,

    base_url="https://api.siliconflow.com/v1",

)

try:

    response = client.chat.completions.create(

        model="zai-org/GLM-5.2",

        messages=[

            {

                "role": "system",

                "content": (

                    "You are a senior software engineer. "

                    "Identify technical risks and explain practical fixes."

                ),

            },

            {

                "role": "user",

                "content": (

                    "Review the supplied module architecture and identify "

                    "possible API compatibility risks."

                ),

            },

        ],

        max_tokens=4096,

        stream=False,

    )

    print(response.choices[0].message.content)

    if response.usage:

        print(f"Prompt tokens: {response.usage.prompt_tokens}")

        print(f"Completion tokens: {response.usage.completion_tokens}")

        print(f"Total tokens: {response.usage.total_tokens}")

except Exception as exc:

    print(f"GLM-5.2 API request failed: {exc}")

import os

from openai import OpenAI

api_key = os.getenv("SILICONFLOW_API_KEY")

if not api_key:

    raise ValueError(

        "SILICONFLOW_API_KEY is not set. "

        "Add it to your environment before running this script."

    )

client = OpenAI(

    api_key=api_key,

    base_url="https://api.siliconflow.com/v1",

)

try:

    response = client.chat.completions.create(

        model="zai-org/GLM-5.2",

        messages=[

            {

                "role": "system",

                "content": (

                    "You are a senior software engineer. "

                    "Identify technical risks and explain practical fixes."

                ),

            },

            {

                "role": "user",

                "content": (

                    "Review the supplied module architecture and identify "

                    "possible API compatibility risks."

                ),

            },

        ],

        max_tokens=4096,

        stream=False,

    )

    print(response.choices[0].message.content)

    if response.usage:

        print(f"Prompt tokens: {response.usage.prompt_tokens}")

        print(f"Completion tokens: {response.usage.completion_tokens}")

        print(f"Total tokens: {response.usage.total_tokens}")

except Exception as exc:

    print(f"GLM-5.2 API request failed: {exc}")

For a direct REST request, use the Chat Completions endpoint:

curl --request POST \

  --url https://api.siliconflow.com/v1/chat/completions \

  --header "Authorization: Bearer $SILICONFLOW_API_KEY" \

  --header "Content-Type: application/json" \

  --data '{

    "model": "zai-org/GLM-5.2",

    "messages": [

      {

        "role": "user",

        "content": "Explain the dependencies and risks in this migration plan."

      }

    ],

    "max_tokens": 4096,

    "stream": false

  }'

curl --request POST \

  --url https://api.siliconflow.com/v1/chat/completions \

  --header "Authorization: Bearer $SILICONFLOW_API_KEY" \

  --header "Content-Type: application/json" \

  --data '{

    "model": "zai-org/GLM-5.2",

    "messages": [

      {

        "role": "user",

        "content": "Explain the dependencies and risks in this migration plan."

      }

    ],

    "max_tokens": 4096,

    "stream": false

  }'

curl --request POST \

  --url https://api.siliconflow.com/v1/chat/completions \

  --header "Authorization: Bearer $SILICONFLOW_API_KEY" \

  --header "Content-Type: application/json" \

  --data '{

    "model": "zai-org/GLM-5.2",

    "messages": [

      {

        "role": "user",

        "content": "Explain the dependencies and risks in this migration plan."

      }

    ],

    "max_tokens": 4096,

    "stream": false

  }'

Begin with a moderate output limit and increase it only when the task requires more space. Production applications should also log token usage, request duration, error codes, retries, and task-level success.

Run representative evaluation tasks before sending a full 1M-token workload. This confirms that the model, prompt, and context-selection strategy provide enough value to justify the larger request.

FAQs

Q1. What Is the SiliconFlow Model ID for GLM-5.2?

The model ID is zai-org/GLM-5.2. Use this exact value in the model field of SiliconFlow API requests.

Q2. How Much Does the GLM-5.2 API Cost?

SiliconFlow currently lists GLM-5.2 at $1.40 per million standard input tokens, $0.26 per million cached-input tokens, and $4.40 per million output tokens. Prices should be checked again before production deployment.

Q3. Does Every GLM-5.2 Request Cost as Much as One Million Tokens?

No. The approximately 1M-token context window is a capacity limit, not a fixed billing unit. A request is charged according to the tokens it actually uses.

Q4. Is Cached Input Automatically Applied to Every Repeated Prompt?

It should not be assumed. The lower rate applies when input qualifies for and hits the platform's cache. Measure actual usage rather than treating all repeated text as cached.

Q5. Is GLM-5.2 Cheaper Than DeepSeek-V4-Pro?

GLM-5.2 has lower standard input pricing, but DeepSeek-V4-Pro has lower cached-input and output pricing. The cheaper option depends on the proportion of new input, cached input, and generated output.

Q6. Is GLM-5.2 Cheaper Than DeepSeek-V4-Flash?

No, based on current SiliconFlow token pricing. DeepSeek-V4-Flash is much cheaper for standard input, cached input, and output. GLM-5.2 should therefore be selected for workload fit rather than the lowest token price.

Q7. What Is GLM-5.2 Best Used For?

It is primarily positioned for long-horizon engineering and agent workflows. Examples include repository analysis, cross-file refactoring, API migration, extended debugging, and tasks that must retain project constraints over many steps.

Q8. Can GLM-5.2 Process an Entire Codebase?

Its long context allows large amounts of code and documentation to be submitted, but whether an entire repository fits depends on token count. Relevance also matters. Filtering generated files, dependencies, duplicate content, and unrelated assets can improve both cost and task focus.

Q9. How Should GLM-5.2 API Costs Be Monitored?

Record prompt tokens, completion tokens, cache-related usage where available, retries, tool-call loops, and the number of requests required to finish a task. Cost per successful task is more meaningful than cost per individual call.

Q10. How Can I Reduce GLM-5.2 API Cost?

Remove irrelevant context, limit unnecessary output, reuse stable prompt prefixes where caching applies, avoid repeated full-context calls, and route simple workloads to lower-cost models. Test these changes against task quality rather than reducing tokens blindly.