GLM-5.2 is Z.ai (Zhipu AI)'s flagship model for the era of long-horizon tasks. It pairs a truly usable 1M-token context window with up to 128K output tokens, letting it hold project-level engineering context, execute long-running tasks more reliably, follow engineering standards more consistently, and carry a task from requirements through to multi-platform deployment in a single run. It is a text-in / text-out model with hybrid reasoning controlled by reasoning_effort (high / max; deep reasoning by default) and native tool calling. Built coding-first as the latest in the GLM-5 line, GLM-5.2 launched on the GLM Coding Plan with standalone API access and MIT-licensed open weights following shortly after. It targets repo-scale agentic coding, autonomous multi-step engineering workflows, and complex long-horizon delivery.
Z.ai: GLM 5.2 is a text‑only large language model with a 1,000,000‑token context window and a maximum output of 128,000 tokens. It is developed by Z.ai and offered through OrcaRouter’s API. The model…
As a large language model, GLM 5.2 can perform diverse text‑based tasks such as summarization, question answering, translation, code generation, and creative writing. Its primary strength lies in its ability to process very long contexts, so it excels at tasks that involve understanding a complete document or conversation history in a single prompt. Examples include extracting key themes from a 500‑page report, generating meeting minutes from an entire transcript, or maintaining a coherent dialogue across hundreds of turns.
You should choose GLM 5.2 when your task requires a context window larger than what smaller models (e.g., 32k or 128k tokens) can handle. For example, analyzing an entire book, a full legal contract, or a large code repository in one shot. If your task fits within a smaller context, a cheaper model with similar performance may be more cost‑effective. This model is also suitable when you need to generate very long outputs (up to 128k tokens) without splitting the response into multiple calls.
The model accepts and produces only text; it does not process images, audio, or other modalities. Users should also be aware that large context models can be slower and more expensive than smaller alternatives. The 1M‑token context window is a maximum; actual usable context may vary depending on the complexity of the task and the API’s infrastructure. OrcaRouter does not provide token caching or discount tiers, so costs scale linearly with usage.
A 1M‑token context window allows the model to consider huge amounts of text at once, which can improve coherence and accuracy in tasks like long‑form summarization or multi‑step reasoning. However, performance may degrade when the prompt fills a large portion of the window, as the model’s attention mechanism becomes computationally expensive. In practice, tasks that require precise retrieval from the middle of a long context may see lower accuracy compared to tasks with information near the beginning or end.
No specific benchmark scores for GLM 5.2 are provided in the available facts. The model is a text‑only LLM with a 1M context window; its performance on standard evaluations (e.g., MMLU, HellaSwag, or coding benchmarks) is not disclosed. Users should evaluate the model on their own datasets to gauge its effectiveness for their use case. The large context window suggests strengths in tasks that require long‑range dependencies, but without published numbers, comparison to other models must be qualitative.
Due to its very large context window (1M tokens), GLM 5.2 is likely to have higher latency per request than models with smaller context windows, especially when the input is long. The attention mechanism scales quadratically with sequence length, so processing a full million tokens will take significantly longer than a 4k‑token input. For low‑latency use cases (e.g., real‑time chatbots), a smaller model may be preferable. OrcaRouter does not publish latency figures for this model.
The model’s principal strength is its ability to accept up to 1 million tokens of input and generate up to 128,000 tokens of output, enabling tasks that few other models can handle in a single call. This makes it ideal for analyzing entire books, legal documents, or codebases without chunking. Additionally, the zero‑markup pricing model means you pay only Z.ai’s rate through OrcaRouter. However, no official benchmark data is available to confirm performance on specific tasks.
Pricing is based on token count: $1.40 per 1 million input tokens and $4.40 per 1 million output tokens. Both input and output are billed at Z.ai’s provider rate, with no markup added by OrcaRouter. There are no separate costs for caching, prompt prefixes, or special features. This per‑token pricing is straightforward and scales with usage. For example, a request with 100,000 input tokens and 5,000 output tokens would cost roughly $0.16.
OrcaRouter does not advertise any volume discounts, tiered pricing, or caching benefits for GLM 5.2. The listed price of $1.40 per million input tokens and $4.40 per million output tokens is the rate for all users. Because there is zero markup, the cost you see is Z.ai’s own rate. If you have very high usage, you may want to contact Z.ai directly to inquire about enterprise agreements, but such arrangements are not handled through OrcaRouter.
GLM 5.2’s per‑token price is higher than many smaller models (e.g., those costing $0.15 per million input tokens). The premium reflects its exceptionally large context window and output limit. If your task requires only a few thousand tokens, a cheaper model will be more cost‑effective. However, for tasks that need the full 1M‑token window, this model may be the only option, and its cost may be justified by the reduction in manual chunking and multiple calls.
Use the OpenAI‑compatible API provided by OrcaRouter. Set the base URL to https://api.orcarouter.ai/v1 and the model ID to “z-ai/glm-5.2”. The standard chat‑completion endpoint (/v1/chat/completions) accepts a JSON payload with messages, max_tokens, temperature, and other parameters. Authentication is via an API key that you acquire from OrcaRouter. Example: curl https://api.orcarouter.ai/v1/chat/completions -H "Authorization: Bearer YOUR_KEY" -d '{"model":"z-ai/glm-5.2","messages":[{"role":"user","content":"Summarize this document."}],"max_tokens":1000}'
The API supports parameters typical of OpenAI‑compatible endpoints: model (required), messages (array of message objects with role and content), max_tokens (integer up to 128000), temperature (float), top_p, frequency_penalty, presence_penalty, stop, stream (boolean), and others. Since the model is text‑only, content must be a string. The context window limit of 1M tokens applies to the total of all messages in the request plus the generated output. Exceeding the limit returns an error.
Yes, the API supports streaming via the `stream` parameter. When set to `true`, the response will be sent as a series of server‑sent events (SSE), each containing a partial generation. This is useful for displaying intermediate results to users. Streaming works identically to the OpenAI streaming format. Note that even with streaming, the full output is counted toward your token usage at the provider’s rate.
To migrate from another API provider to OrcaRouter for GLM 5.2, you only need to change the base URL and model name. If you were using OpenAI’s client library, replace the base URL with https://api.orcarouter.ai/v1 and set the model to “z-ai/glm-5.2”. The same JSON format for messages and parameters works. Ensure your API key is from OrcaRouter. No code changes beyond the endpoint are required.
GLM 5.2 offers a 1M‑token context window, which is among the largest available. Many competitors cap at 128k or 200k tokens. Its output limit of 128k tokens is also higher than typical. However, it is text‑only, whereas some rivals support images or audio. Pricing at $1.40/$4.40 per million tokens is moderate for such a large window; some competitors charge higher rates. Without benchmark data, direct quality comparison is not possible.
Choose GLM 5.2 only when your application truly benefits from a million‑token context window. If your prompts and expected outputs fit within 32k or 128k tokens, a less expensive model (e.g., one costing $0.15 per million input tokens) will be much cheaper and likely faster. The advantage of GLM 5.2 is in eliminating the need to split long texts, which can save engineering time and preserve cross‑reference context.
Many high‑quality models (e.g., those with 128k‑token windows) may match GLM 5.2’s performance on typical tasks, but they cannot process documents longer than their window. For tasks that fit within a smaller context, such models are often faster and more cost‑effective. GLM 5.2’s niche is the ability to handle extremely long inputs in one pass, which is essential for use cases like full‑book analysis, complete codebase summarization, or very long‑running conversations.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="z-ai/glm-5.2",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_tokensreasoningreasoning_effortresponse_formatstopstreamtemperaturetool_choicetoolstop_p| Input / 1M tokens | $1.40 |
| Output / 1M tokens | $4.40 |
| Cache read / 1M | $0.260 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
@misc{orcarouter_glm_5_2,
title = {GLM 5.2 API},
author = {Z.ai},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/z-ai/glm-5.2}
}Z.ai. (2026). GLM 5.2 API. OrcaRouter. https://www.orcarouter.ai/models/z-ai/glm-5.2