GLM-5.3 is Z.ai (Zhipu AI)'s latest flagship model for complex software engineering and long-horizon agentic tasks. It delivers roughly a 50% improvement in coding experience over GLM-5.2, matches Mythos 5 on selected cybersecurity capabilities, and strikes a better balance between raw performance and token efficiency. It is a text-in / text-out model built for repo-scale coding, autonomous multi-step engineering, and agent workflows that must stay coherent over long horizons. GLM-5.3 uses the same API surface as the GLM-5 line with two changes callers must handle: thinking is always on (thinking.type only accepts enabled; passing disabled now fails the request), and reasoning depth is controlled by reasoning_effort with values low / high / max, defaulting to max. It supports native tool calling and structured JSON output, and speaks the OpenAI-compatible chat-completions format.
GLM 5.3 is a large language model listed under provider z-ai and served through OrcaRouter. The catalog entry describes it as text-only with an input context of up to 1,000,000 tokens and a maximum…
With a 1,000,000-token context, GLM 5.3 can process documents that would otherwise need to be split into many chunks. You can put an entire novel, a long technical manual, or hundreds of pages of conversation history into the prompt. The main practical benefit is that the model can consider all the material at once when answering. That makes tasks like summarization, fact extraction, and comparative analysis possible without custom retrieval logic. It also means you can ask questions about a large body of text in follow-up turns, because the full conversation stays in the context window. However, using a 1M-token prompt is not free; input tokens are billed at $1.40 per million, so a full prompt would cost roughly $1.40, and that count excludes the output. There is no dedicated retrieval feature described in the catalog, so the model simply uses what is in the context.
The maximum output length for GLM 5.3 is 128,000 tokens. This is far above the typical 4,000 to 8,000 default limits on many models. It allows the model to produce long reports, code files, machine translations, or multi-chapter drafts in one call. At the output price of $4.40 per million tokens, a 128,000-token answer would cost roughly $0.56 in output tokens (128,000 / 1,000,000 * 4.40). The actual number of tokens per word varies, but this gives a sense of the upper-bound cost. OrcaRouter billing is token-based, so shorter outputs cost proportionally less. There is no indication that the output limit can be set higher; 128,000 is the ceiling listed in the catalog. When designing an application, you may need to handle a very long output stream or use streaming to present results incrementally, since the model can emit a lot of text.
The catalog lists the input modality for Z.ai GLM 5.3 as text. This means the model accepts plain text messages, including conversation history, system prompts, and user content. It does not accept images, audio, video, or other binary content. This is an important constraint when choosing a model for a specific task. If your pipeline needs to read a screenshot, analyze a recording, or classify a video, you would need a multimodal model or a separate transcription/vision step before calling GLM 5.3. While the context window is large, the content is still text; you cannot attach a PDF file directly unless you extract its text first. The model can process long textual JSON, code, logs, or articles. For text-only workloads, the large context may be useful; for any other modality, look for a different model on OrcaRouter.
GLM 5.3's large context and long output come at a price per token that is higher than some smaller models. If your task only needs a few thousand tokens of context and a short reply, a cheaper model may produce good results at lower cost. OrcaRouter offers many models with different price points, but this catalog entry does not list alternatives. As a general rule, use GLM 5.3 when you need a large context or very long output in a single call, and when those capabilities justify the cost. If you can split your task into smaller pieces or if a shorter context is sufficient, a smaller model will use fewer input tokens and could be more cost-effective. Also consider latency: processing a 1M-token prompt will take longer than processing a short prompt, regardless of the model. The choice depends on your production requirements.
The OrcaRouter catalog entry for Z.ai GLM 5.3 does not include any benchmark scores. That means there are no official numbers in this listing to cite for MMLU, HumanEval, or other standard evaluations. It is still possible to evaluate the model yourself by running your own test prompts and comparing outputs on your domain. Since no benchmark data is provided, any claims about relative quality on specific tasks should be treated with caution. The only hard numbers given are the context window, maximum output, and price. For a language model family like GLM, external publications may offer general information, but the absence of a benchmark table here means the safest approach is to measure on representative tasks. OrcaRouter's API can be used to run side-by-side evaluations against other models, but any results you gather apply to your use case, not global rankings.
No latency figures are listed in the catalog for GLM 5.3, so there is no exact speed to report. In general, response time depends on several factors: the length of the input prompt, the number of tokens requested in the output, the upstream provider's current load, and network conditions. A request with a 1,000,000-token prompt will take considerably longer to process than a short prompt because the model must read all of that text before generating. The output generation time also increases with the number of output tokens; a 128,000-token response can be very slow. For interactive applications, you may want to use streaming to start receiving text as it is generated. OrcaRouter's OpenAI-compatible API supports standard streaming parameters, but actual throughput is determined by the z-ai provider. You should test a representative request to understand the latency for your workload.
The main limitations of GLM 5.3 are tied to its catalog specifications. It is text-only, so it cannot natively process images, audio, or video; content in those forms must be converted to text first. The context window is 1,000,000 tokens, but using it fully means your request is large, which has cost and latency implications. The maximum output is 128,000 tokens, but generating such an output is time-consuming and may run into provider timeouts if you do not use streaming. The catalog does not list any benchmark scores, so you should not assume it outperforms other models on every task. Finally, as with all language models, outputs can be inaccurate or hallucinated; you should verify important information. The token count is approximate, so a 1M-token prompt is a practical ceiling, not a guarantee that the model will handle every long prompt perfectly.
GLM 5.3 is billed per token. The listed input price is $1.40 per 1,000,000 tokens, and the output price is $4.40 per 1,000,000 tokens. OrcaRouter adds no markup; the amount you are charged is exactly the provider rate. Input tokens include the prompt, any system instructions, and the conversation history that you send. Output tokens are the ones the model generates. Most APIs count both the prompt and the generated text, and this model follows the standard per-token billing. There is no monthly subscription in this listing, and there is no mention of a separate fixed fee. The total cost of a request is calculated as (input tokens / 1,000,000) * 1.40 plus (output tokens / 1,000,000) * 4.40. For a request with 10,000 input tokens and 1,000 output tokens, the cost would be $0.014 plus $0.0044, for a total of about $0.0184.
Because input and output tokens are priced differently for GLM 5.3, the cost distribution depends on how you use the model. Input tokens cost $1.40 per million, and output tokens cost $4.40 per million, making output more than three times as expensive per token. This is a common pricing structure for many language models. A task that reads a long document and returns a short summary will be dominated by input cost. A task that starts with a short prompt and generates a very long response will be dominated by output cost. The maximum output of 128,000 tokens means a single maximally long generation could cost roughly $0.56 in output tokens. In a conversation that accumulates many turns, both sides grow; the historical input is resent each time unless you use shorter context windows or truncate history. You can reduce cost by keeping prompts concise and limiting output length when possible.
The catalog entry for GLM 5.3 does not mention prompt caching, discounts, or special pricing tiers. The listed price is straightforward: $1.40 per million input tokens and $4.40 per million output tokens. If OrcaRouter or the upstream provider introduces caching in the future, the effective cost for repeated prompts may change, but no such mechanism is described here. Similarly, there is no mention of batch processing or volume discounts. To control costs, you can manage the number of tokens you send. For example, instead of resending a whole conversation, keep only the relevant recent turns. You can also set a lower max_tokens value to cap output length. Because OrcaRouter bills at the provider rate with zero markup, the cost you see in the model listing is the base rate. Always check current documentation for any updates to pricing or new features.
To call Z.ai GLM 5.3, point your HTTP client at OrcaRouter's OpenAI-compatible API. The base URL is https://api.orcarouter.ai/v1. Use the model ID z-ai/glm-5.3 in the request body. If you are using the official OpenAI SDK, set the base_url to OrcaRouter's endpoint and use your OrcaRouter API key. The request format is the standard chat completions shape: a JSON object with a model field and a messages array. Each message contains a role and content. The model will generate a text response. OrcaRouter forwards the request to the z-ai provider. Since the API is OpenAI-compatible, libraries and tools that support OpenAI endpoints can be pointed to OrcaRouter without a custom integration. For authentication, include an Authorization header with your OrcaRouter key. The endpoint accepts normal chat-completion calls; there is no separate RESTful resource for this model.
OrcaRouter's OpenAI-compatible API for GLM 5.3 accepts the same request parameters that are common in the OpenAI chat completions format. You can set model to z-ai/glm-5.3, provide messages, and control generation with parameters such as max_tokens, temperature, top_p, and stream. The exact list of supported parameters may vary by provider. The catalog does not list a complete parameter schema, so you should refer to OrcaRouter's API documentation for the current supported fields. Because the model's maximum output is 128,000 tokens, you can set max_tokens well beyond the default of many clients; if your client caps this value, you may need to adjust it. The context window of 1,000,000 tokens means the total token count of your messages can be very large; sending that much text as JSON is fine, but be mindful of request size and latency. Streaming is generally available for OpenAI-compatible APIs, but OrcaRouter's exact response format follows the standard.
Migrating an application from OpenAI to GLM 5.3 via OrcaRouter typically requires two changes. First, change the base_url to https://api.orcarouter.ai/v1. Second, change the model name to z-ai/glm-5.3. The messages array, roles, and JSON response structure remain the same as OpenAI's chat completions format. If you currently use the OpenAI SDK, update the environment variables or client configuration to point at OrcaRouter. Use an OrcaRouter API key in place of the previous key. No code changes are needed for the request body itself, although you may want to review parameters. Because GLM 5.3 is text-only, remove any image_url or multimodal attachments from your prompts. Also, the model's context window is much larger than many OpenAI models, so you can increase the amount of conversation history you send. Test with a small request first to confirm that the response format matches what your code expects.
Here is a minimal chat completion request for GLM 5.3 through OrcaRouter. Send a POST to https://api.orcarouter.ai/v1/chat/completions with an Authorization header containing your OrcaRouter API key. The body should include the fields: model set to z-ai/glm-5.3, and messages with at least one message, for example {"role":"user","content":"What is the capital of France?"}. The response will contain the model's reply in the standard OpenAI chat completion format. You can add optional parameters like temperature and max_tokens. If max_tokens is omitted, the provider uses its default; to take advantage of the 128,000-token output limit, set max_tokens to the desired value. For streaming, set stream to true and read the data lines as they arrive. The exact JSON formatting follows OpenAI's convention, so existing helper functions for parsing choices and message content should work.
The main difference between GLM 5.3 and models with smaller context windows is the amount of text that can fit in a single prompt. GLM 5.3 supports 1,000,000 tokens, while many common models support between 4,000 and 200,000 tokens. For tasks like analyzing an entire book, a single request with GLM 5.3 can avoid the complexity of chunking and retrieval. However, a larger context window does not automatically mean better performance; shorter-context models are sometimes tuned to be highly accurate on focused tasks. Cost is another factor: the per-token input and output prices for GLM 5.3 are $1.40 and $4.40 per million tokens, and a very long prompt will consume many tokens. If your data fits within a smaller context, a cheaper model may be more efficient. OrcaRouter lets you choose the model that fits your workload; the catalog entry for GLM 5.3 does not include a comparison table, so you should test with your own data.
GLM 5.3 is text-only, so it does not accept images, audio, or video as input. Multimodal models can combine these modalities with text, allowing you to ask questions about a photo, a recording, or a video frame. If your application needs visual understanding, GLM 5.3 is not the right fit. However, for text-only workloads, GLM 5.3's 1,000,000-token context and 128,000-token output are distinctive. Many multimodal models have a much smaller context window or limited output length. Also, multimodal models often charge higher input prices because the image tokens can be expensive. If you only have text, you may prefer a text-only model to avoid the overhead of encoding images. The choice between GLM 5.3 and a multimodal model should be based on the input types your application must handle. For a text corpus, GLM 5.3's large context is a clear advantage.
Z.ai, also known as Zhipu AI, develops a family of GLM models. This catalog entry covers GLM 5.3 specifically, and it does not describe older or smaller GLM versions. The listed context window of 1,000,000 tokens and maximum output of 128,000 tokens are larger than typical default limits, but no comparative specifications for other GLM models are provided in this entry. Smaller Z.ai models may have different price points and lower latency, but no specific numbers appear alongside this catalog record. Because OrcaRouter serves models from multiple providers, you can compare GLM 5.3 against other text models side by side using the OpenAI-compatible API. The best way to decide is to run a small, representative workload through each model and measure quality, speed, and cost. Without official comparative scores, any direct performance ranking between GLM 5.3 and other versions would be speculative.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="z-ai/glm-5.3",
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.
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@misc{orcarouter_glm_5_3,
title = {GLM 5.3 API},
author = {Z.ai},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/z-ai/glm-5.3}
}Z.ai. (2026). GLM 5.3 API. OrcaRouter. https://www.orcarouter.ai/models/z-ai/glm-5.3