GLM-5.2 vs GPT-5.5: Pricing, Context Window & Open-Source Access

GLM-5.2 and GPT-5.5 both offer context windows of about one million tokens, but their pricing and access models differ substantially. GLM-5.2 is available through SiliconFlow at lower published input and output rates. Its model weights are also available under the MIT License. GPT-5.5 is a proprietary model delivered through OpenAI's hosted APIs, with separate pricing for short- and long-context requests.

For teams evaluating GLM-5.2 vs GPT-5.5, the decision should not rest on context size alone. API cost, deployment control, tool support, output quality, retry rates, and operational requirements can all affect the final cost of a production workload.

This guide helps teams compare the public pricing data, context-window limits, open-weight access, and estimated API costs behind both models, so they can decide where GLM-5.2 may be worth testing in real AI applications.

This comparison uses public specifications and pricing available on June 25, 2026. It does not claim that either model is universally better.

How This Comparison Was Built

A reliable model comparison needs a consistent measurement method. The figures below compare GLM-5.2 through the SiliconFlow Serverless API with GPT-5.5 through OpenAI's direct Standard API.

The comparison follows five rules:

• Prices are stated per one million tokens.

• Cost estimates assume uncached text input unless stated otherwise.

• The same input and output token counts are applied to both models.

• Batch, Flex, Priority, regional processing, and external tool fees are excluded from the main calculations.

• Published specifications are not treated as proof of real-world task quality.

The cost formula is:

This formula estimates API charges under fixed token assumptions. It does not account for different response lengths, reasoning-token use, retries, failed requests, or task completion rates.

The comparison also separates three concepts that are often mixed together:

• Context window: The total amount of information a model can process within a request.

• Maximum output: The upper limit on generated tokens, where documented.

• Open-weight access: Whether developers can obtain and deploy the model weights themselves.

Keeping these definitions separate prevents a large context window from being mistaken for stronger reasoning or better long-document recall.

Availability, API Access, and Open-Source Status

GLM-5.2 is available as a Serverless model on SiliconFlow under the model identifier zai-org/GLM-5.2. Developers can test models in the SiliconFlow Playground, create an API key, and call supported models through REST endpoints or the OpenAI Python library.

A typical OpenAI-compatible client configuration uses SiliconFlow's API base URL:

from openai import OpenAI
client = OpenAI(
    api_key="YOUR_SILICONFLOW_API_KEY",
    base_url="https://api.siliconflow.com/v1"
)

from openai import OpenAI
client = OpenAI(
    api_key="YOUR_SILICONFLOW_API_KEY",
    base_url="https://api.siliconflow.com/v1"
)

from openai import OpenAI
client = OpenAI(
    api_key="YOUR_SILICONFLOW_API_KEY",
    base_url="https://api.siliconflow.com/v1"
)

This interface can reduce migration work for applications already structured around OpenAI-compatible chat completions. However, compatibility at the request level does not mean every model supports the same parameters, tools, output formats, or response behavior. Teams should still test their exact integration.

GLM-5.2 is listed with an MIT License, and its model weights are available for download. The wider GLM-5 series also has deployment guidance for inference frameworks such as vLLM, SGLang, xLLM, and KTransformers. This gives developers two broad access paths:

1. Use GLM-5.2 through the SiliconFlow Serverless API.

2. Download the available weights and operate suitable inference infrastructure.

Open-weight access is valuable for teams that need deeper control over deployment, networking, data handling, or model operations. It can support private infrastructure plans, internal experimentation, and customized serving environments. However, self-managed deployment also introduces a higher technical and operational burden. A model of this size may require substantial compute, memory, storage, inference optimization, monitoring, scaling, and engineering support.

For many teams, the more practical starting point is the SiliconFlow Serverless API. It allows developers to test GLM-5.2 without setting up large-scale model infrastructure, managing GPU capacity, or maintaining an inference stack. This is especially useful for teams that want to validate long-context workloads, compare API costs, or move quickly from prototype to production evaluation.

Teams with strict infrastructure, compliance, or customization requirements can still evaluate private deployment later. In that sense, GLM-5.2 gives developers a flexible path: start with a managed API to reduce operational complexity, then consider self-managed deployment if the business case requires deeper control.

GPT-5.5 is available through OpenAI's hosted APIs, including the Responses API and Chat Completions API. OpenAI does not list downloadable GPT-5.5 weights or an open model license. Therefore, developers access the model as a managed service rather than deploying its weights on their own infrastructure.

The practical distinction is clear: GLM-5.2 offers both managed API access and an open-weight deployment path, while GPT-5.5 is primarily available as a hosted proprietary API.

Context Window Comparison

The headline context capacities of GLM-5.2 and GPT-5.5 are almost identical.

The difference between 1,049K and 1,050K tokens is not meaningful for most purchasing decisions. Both models belong to the approximately one-million-token context category.

That capacity may support large code repositories, multi-document analysis, extended conversation histories, research collections, or long agent trajectories. However, the advertised limit does not establish how accurately either model uses every part of that window.

A context-window figure cannot prove:

• Accurate retrieval from distant sections of a prompt

• Reliable reasoning across hundreds of thousands of tokens

• Equal attention to all included documents

• Stable performance near the maximum limit

• Lower latency for large requests

• Successful completion of a long-running agent workflow

The most important commercial difference appears in the pricing structure. OpenAI applies long-context rates when a GPT-5.5 prompt contains more than 272K input tokens. The higher rates apply to the full session, not only the tokens above the threshold.

SiliconFlow GLM-5.2 / GLM-5.2 on SiliconFlow currently uses one published set of input, cache-read, and output rates, with no separate long-context pricing tier listed.

Public Pricing Data and Missing Pricing Gaps

The published Standard API rates show a large price difference between the models.

For GPT-5.5, short-context rates apply when input remains at or below 272K tokens. Prompts above that level are billed at twice the Standard input rate and 1.5 times the Standard output rate for the full session.

These numbers are useful, but they are not a complete estimate of production spending.

OpenAI also offers different processing modes. Batch and Flex can reduce token rates, while Priority processing uses higher rates. Regional processing for eligible models can add a surcharge. Built-in tools such as web search, file search, and hosted containers may introduce separate usage fees.

SiliconFlow costs can also extend beyond the simple price table. Teams may need to account for rate limits, traffic peaks, failed calls, response truncation, observability, fallback models, and application-level caching.

Self-hosting GLM-5.2 introduces another cost category. A downloadable model does not remove expenses for:

• Compute hardware or cloud accelerators

• Model storage and loading

• Inference optimization

• Scaling and load balancing

• Monitoring and incident response

• Security and access control

• Engineering time

• Idle capacity

Public token rates are therefore best used as an initial cost signal, not a complete total-cost-of-ownership calculation.

GLM-5.2 Cost Examples on SiliconFlow

GLM-5.2 Serverless is currently priced as follows:

• Input: $1.40 per million tokens

• Cache read: $0.26 per million tokens

• Output: $4.40 per million tokens

Consider a request containing 50,000 uncached input tokens and producing 5,000 output tokens.

Input cost:

Output cost:

Estimated total:

If the same 50,000 input tokens qualified for the published cache-read rate, that portion would cost:

With the same 5,000-token output, the estimated total would become:

This illustrates why cache usage can materially change spending for repeated system prompts, stable reference material, or recurring context. Actual cache eligibility and behavior should be verified in the live implementation rather than assumed from the price alone.

Estimated Cost for Three Workloads

The following examples apply identical token counts to both models. They use uncached input, Standard processing, and no external tools.

Workload 1: A Focused Code or Document Review

Assumption:

• 10,000 input tokens

• 2,000 output tokens

GLM-5.2

• Input: 0.01 × $1.40 = $0.014

• Output: 0.002 × $4.40 = $0.0088

• Total: $0.0228

GPT-5.5

• Input: 0.01 × $5.00 = $0.05

• Output: 0.002 × $30.00 = $0.06

• Total: $0.11

Under these assumptions, GLM-5.2 costs about 79% less per request.

Workload 2: Multi-Document or Repository Analysis

Assumption:

• 100,000 input tokens

• 10,000 output tokens

GLM-5.2

• Input: 0.1 × $1.40 = $0.14

• Output: 0.01 × $4.40 = $0.044

• Total: $0.184

GPT-5.5

• Input: 0.1 × $5.00 = $0.50

• Output: 0.01 × $30.00 = $0.30

• Total: $0.80

Under fixed token counts, GLM-5.2 costs about 77% less.

Workload 3: A Long-Context Knowledge or Codebase Task

Assumption:

• 500,000 input tokens

• 20,000 output tokens

Because GPT-5.5 input exceeds 272K tokens, the long-context rates apply to the full session.

GLM-5.2

• Input: 0.5 × $1.40 = $0.70

• Output: 0.02 × $4.40 = $0.088

• Total: $0.788

GPT-5.5

• Input: 0.5 × $10.00 = $5.00

• Output: 0.02 × $45.00 = $0.90

• Total: $5.90

Under this scenario, GLM-5.2 costs about 87% less.

These figures compare API charges, not task outcomes. A model that requires longer outputs, additional prompts, or more retries may create a different final cost.

Where GLM-5.2 Has a Cost or Access Advantage

GLM-5.2 has the clearest advantage when a team prioritizes lower published token rates, open-weight access, or both.

Lower published API rates: GLM-5.2 on SiliconFlow has lower published rates across uncached input, cached input, and output. In the three fixed-token workload examples above, using GLM-5.2 on SiliconFlow reduced estimated API cost by about 77% to 87% compared with GPT-5.5. This makes GLM-5.2 a cost-conscious GPT-5.5 alternative for teams that need to test large-context AI applications while keeping token spending under control. The difference is especially significant for output-heavy workloads because GPT-5.5 Standard output is priced at $30 per million tokens for short context and $45 per million tokens for long context.

More predictable long-context pricing: GLM-5.2 currently has no separately listed rate increase after a specific long-context threshold. This can make projected pricing easier to calculate for large prompts, although teams should confirm current rates before deployment.

Greater deployment flexibility: The MIT-licensed weights create more deployment choice. An organization may begin with SiliconFlow's hosted API and later evaluate private or self-managed infrastructure. This can matter when teams need greater control over hosting, networking, model operations, or deployment architecture.

A familiar API integration path: SiliconFlow supports OpenAI-library access. Developers can use a familiar client structure while changing the API key, base URL, and model identifier. This may reduce initial integration work for applications built around compatible chat-completion patterns.

However, these advantages have boundaries. Lower token prices do not prove lower cost per successful task. Open weights do not guarantee economical self-hosting. An OpenAI-compatible endpoint also does not guarantee feature parity with GPT-5.5.

GLM-5.2 is most compelling when its lower API rates and deployment flexibility align with a workload that it can complete reliably.

What Public Data Cannot Prove

Public pricing and model cards cannot determine which model will produce the better result for a specific application.

This comparison does not establish:

• Which model writes more accurate production code

• Which model follows complex instructions more consistently

• Which model retrieves information more reliably from a one-million-token prompt

• Which API has lower latency at a given traffic level

• Which model needs fewer retries

• Which model produces shorter or more useful answers

• Which service delivers better uptime for a particular region

• Which option creates the lowest total cost per completed task

Vendor benchmarks can provide useful signals, but they may use different prompts, tools, inference settings, or evaluation methods. A fair production decision requires an internal evaluation set.

Teams should test both models with representative inputs and measure:

• Task success rate

• Human or automated quality scores

• Input and output token use

• Retry frequency

• Latency

• Tool-call accuracy

• Failure and truncation rates

• Cost per accepted result

The lowest rate per million tokens is valuable only when the model also meets the application's quality and reliability requirements.

Evaluate GLM-5.2 Against Your Real Workload

The central result of this GLM-5.2 vs GPT-5.5 comparison is not that one model wins every category. Both provide approximately one-million-token context windows, but they serve different access and cost priorities.

GPT-5.5 offers a managed proprietary model with OpenAI's API and tool ecosystem. GLM-5.2 combines lower published Serverless rates on SiliconFlow with MIT-licensed model-weight access.

For teams processing large documents, code repositories, or extended agent context, GLM-5.2's pricing creates a strong reason to test it. Run representative prompts through the SiliconFlow API, measure accepted-task cost rather than token price alone, and compare the results against your quality, latency, and deployment requirements.

FAQs

Q1. Is GLM-5.2 Open Source?

GLM-5.2 is listed with an MIT License, and downloadable model weights are available. "Open-weight" is the most precise description because it confirms access to the weights without implying that every dataset, training process, and supporting system is fully disclosed.

Q2. Is GPT-5.5 Open Source?

No downloadable GPT-5.5 weights or open model license are listed in OpenAI's official model documentation. Developers access GPT-5.5 through OpenAI's managed products and APIs.

Q3. Which Model Has the Larger Context Window?

Their advertised capacities are effectively equal. SiliconFlow lists a 1,049K context length for GLM-5.2, while OpenAI lists a 1,050,000-token window for GPT-5.5. The small numerical difference is unlikely to affect a practical model decision.

Q4. How Much Does GLM-5.2 Cost on SiliconFlow?

As of June 25, 2026, SiliconFlow lists GLM-5.2 at $1.40 per million input tokens, $0.26 per million cache-read tokens, and $4.40 per million output tokens. Pricing can change, so developers should verify the live model page before estimating production spending.

Q5. Does GPT-5.5 Cost More for Long-Context Requests?

Yes. OpenAI states that prompts with more than 272K input tokens use twice the normal input rate and 1.5 times the normal output rate for the full session under Standard, Batch, and Flex processing.

Q6. Can GLM-5.2 Be Called With the OpenAI Python Library?

Yes. SiliconFlow provides an OpenAI-compatible API interface, so developers can call GLM-5.2 with the OpenAI Python library by changing the API key, base URL, and model identifier. This can make it easier to migrate from OpenAI or test GLM-5.2 as a GPT-5.5 alternative in existing applications. Teams migrating from GPT-5.5 should still confirm that their required parameters, tool behavior, output format, and application logic work as expected with GLM-5.2 on SiliconFlow.

Q7. Does Open-Weight Access Make GLM-5.2 Free to Run?

No. Downloading the weights does not eliminate infrastructure and operational expenses. A model with hundreds of billions of total parameters may require substantial compute, memory, storage, optimization, and engineering resources.

Q8. Is GLM-5.2 Always Cheaper Than GPT-5.5?

Its published SiliconFlow token rates are lower in the scenarios calculated here. However, final cost depends on token use, output length, retries, processing mode, caching, tools, and task success. A controlled workload test is needed before making a production decision.