Z.ai's strongest coding-and-agent model in the GLM-5 line; supports streaming tool calls and deep thinking. 200K context.
GLM 5.1 is a flagship language model from Z.ai, accessible through OrcaRouter's OpenAI-compatible API. It is designed for tasks that require handling very large context windows – up to 200,000 tokens…
GLM 5.1 is optimized for tasks that benefit from its large context and high output capacity. These include summarizing or extracting insights from long documents (e.g., 100+ page reports), conducting multi-step research where the model must keep track of many intermediate results, and generating detailed code or technical explanations. Its τ²-Bench score of 97.7 suggests particular strength in tool-use scenarios where the model must plan, execute actions, and incorporate feedback across many steps. It is also effective for complex question-answering that requires referencing a large knowledge base embedded in the prompt.
GLM 5.1 is a text-only model and cannot process images, audio, or video inputs. For multimodal applications, it must be combined with separate vision or audio models. Additionally, its large context window and high output token limit come with proportional computational cost – both in latency and price. For simple tasks like short-form chat or basic classification, smaller, cheaper models may be more efficient. Developers should also note that while the model's context window is 200K tokens, actual performance on very long contexts can depend on the nature of the content.
If your use case involves short prompts (under 10K tokens), straightforward generation (e.g., simple translations or keyword extraction), or high-volume throughput where latency is critical, a smaller or cheaper model may be more appropriate. GLM 5.1's pricing of $1.40 per 1M input and $4.40 per 1M output tokens is competitive for flagship capabilities but can accumulate cost if used for trivial tasks. Examples of cases where a cheaper model suffices include: simple chatbot responses, moderate-length summarization of short articles, or single-turn classification.
Yes, GLM 5.1 can handle multi-turn conversations effectively, due to its 200K-token context window. This allows the model to retain conversation history, including previous user messages and system instructions, across many exchanges without losing context. However, developers should be mindful of token usage: each turn adds to the prompt, so long conversations can become expensive. It is possible to truncate or summarize older turns to stay within the context limit, but the model's native capacity is generous enough for most realistic conversational applications.
GLM 5.1 achieved a score of 97.7 on τ²-Bench, a benchmark designed to evaluate a model's ability to perform multi-step tool use and planning. It simulates realistic tasks where the model must decide which tools to call, in what order, and how to interpret results to achieve a goal. This is distinct from traditional QA benchmarks and focuses on agentic capabilities. A score of 97.7 indicates that GLM 5.1 can successfully complete nearly all tasks in the evaluation set, reflecting strong reasoning and tool-use skills. It is one of the highest reported scores on this benchmark.
Only the τ²-Bench score of 97.7 has been provided for GLM 5.1. No other benchmark results (such as MMLU, HumanEval, or GSM8K) are available in the official specifications. Without additional data, a direct comparison on other common evaluations is not possible. However, the τ²-Bench result suggests that the model is particularly strong in areas of tool use and planning. For tasks that are not covered by this benchmark, developers should evaluate the model empirically using their own test sets.
No specific latency or throughput figures have been provided for GLM 5.1. As a flagship model with a large context window and high output limit, inference times will be longer than smaller models, especially when processing near-maximum context lengths. Throughput depends on the hardware infrastructure behind OrcaRouter and the batch size of requests. For real-time applications, developers should test with their typical input and output sizes to gauge acceptable response times. Streaming mode can help reduce perceived latency by delivering tokens incrementally.
The only available benchmark data for GLM 5.1 is its τ²-Bench score of 97.7. While this indicates strong tool-use planning, it does not cover many other important dimensions such as factual accuracy, mathematical reasoning, coding, or multilingual performance. Therefore, relying solely on this score may give an incomplete picture. Developers should supplement with their own domain-specific evaluations, especially if the application involves tasks not related to tool use. Additionally, benchmarks can be influenced by prompt design and evaluation methodology, so real-world performance may vary.
GLM 5.1 is priced at $1.40 per 1 million input tokens and $4.40 per 1 million output tokens. These rates are set by the provider Z.ai and are passed through by OrcaRouter with zero markup. Both input and output tokens are counted according to the standard tokenization used by the model. There are no additional usage fees or subscription requirements. Payment is billed based on token consumption, making it straightforward to estimate costs for a given workload.
No specific caching or discount options have been announced for GLM 5.1. The pricing model is strictly per-token at the listed rates. OrcaRouter may offer prompts caching or similar features at the platform level, but these are not disclosed in the model's specifications. Developers looking to reduce costs should consider optimizing prompt length, using shorter output when possible, and batching requests. For high-volume usage, contacting OrcaRouter directly might provide additional options.
Direct comparison with other flagship models is limited because only the pricing for GLM 5.1 has been provided: $1.40 per 1M input and $4.40 per 1M output. Other flagship models from different providers often have similar or higher rates, but exact figures depend on the specific model and provider. GLM 5.1's pricing is considered competitive for a model with a 200K context window and 128K output limit. For budget-conscious projects, smaller models from Z.ai or other providers may be more economical.
GLM 5.1 is offered on a pay-as-you-go basis through OrcaRouter. There are no upfront commitments or subscription fees. You pay only for the tokens you consume at the per-token rates. This model is ideal for variable workloads where usage fluctuates. Billing is handled by OrcaRouter using the provided API keys.
To use GLM 5.1 via OrcaRouter, set your API base URL to https://api.orcarouter.ai/v1 and use the model ID "z-ai/glm-5.1". For example, with the OpenAI Python library: openai.base_url = "https://api.orcarouter.ai/v1"; openai.api_key = "your-key"; response = openai.ChatCompletion.create(model="z-ai/glm-5.1", messages=[...]). All standard parameters such as temperature, top_p, max_tokens, and stop sequences are supported. Streaming is enabled by setting stream=True.
GLM 5.1 supports the same chat completion parameters as the OpenAI API. This includes temperature (range 0 to 2, default 0.7), top_p (0 to 1, default 1), max_tokens (up to the model's limit of 128,000), stop sequences (list of strings), frequency_penalty, presence_penalty, and logit_bias. The model also supports the "n" parameter for generating multiple completions per request. All parameters are passed in the standard JSON body. System messages, user messages, and assistant messages are all supported.
Migrating to OrcaRouter to use GLM 5.1 is straightforward. In your existing code, change the base URL from your previous provider to https://api.orcarouter.ai/v1. Then replace the model name in your API calls with "z-ai/glm-5.1". No other changes are needed for standard chat completions. If you were using a different API key format, ensure you use the OrcaRouter API key. The response format is identical to OpenAI's, so parsing logic remains the same.
Yes, OrcaRouter supports streaming responses for GLM 5.1 exactly as the OpenAI API does. Set stream=True in the request and iterate over the response chunks. Each chunk contains delta updates to the content field. The streaming format is SSE (Server-Sent Events). This allows you to display tokens incrementally to users, reducing perceived latency. The same streaming parameters (temperature, max_tokens, etc.) apply when streaming.
GLM 5.1 is the flagship model from Z.ai, meaning it sits at the top of their lineup in terms of capability and price. Lower-tier Z.ai models typically have smaller context windows, lower output limits, and lower benchmark scores, but also lower per-token prices. The exact specifications of other Z.ai models are not provided, but potential trade-offs include reduced context (e.g., 128K), lower output caps, and possibly support for multimodal inputs. Developers should choose GLM 5.1 when the full 200K context and high output are necessary.
Compared to other flagship models from different providers, GLM 5.1 offers a very large context window (200K tokens) and output limit (128K tokens), which is among the highest available. Its τ²-Bench score of 97.7 places it at the top for tool-use planning. However, it is text-only, whereas several competing flagship models support vision or audio inputs. Pricing at $1.40/$4.40 per 1M tokens is competitive but may be higher or lower depending on the specific provider and model. Developers should evaluate based on their exact requirements for context, modalities, and benchmark performance.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="z-ai/glm-5.1",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)do_sampleinclude_reasoningmax_tokensreasoningrequest_idresponse_formatstopstreamtemperaturethinkingtool_choicetool_streamtoolstop_puser_id| 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_1,
title = {GLM 5.1 API},
author = {Z.ai},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/z-ai/glm-5.1}
}Z.ai. (2026). GLM 5.1 API. OrcaRouter. https://www.orcarouter.ai/models/z-ai/glm-5.1