GLM-5.1: specs, benchmarks, and how to run it on Lyceum

Integrating GLM-5.1 into your application is straightforward with Lyceum. Because our Serverless Inference API is fully OpenAI-compatible, you can switch to this 754B parameter model by updating just two lines of code: your base URL and your API key. There is no need to rewrite your application logic.

from openai import OpenAI

client = OpenAI(
 base_url="https://api.lyceum.technology/api/v2/external/serverless",
 api_key="<your lyceum api key>",
)
response = client.chat.completions.create(
 model="zai-org/GLM-5.1",
 messages=[{"role": "user", "content": "Hello!"}],
 max_tokens=256,
)
print(response.choices[0].message.content)

Pricing and region for GLM-5.1

When you deploy this model, you benefit from transparent, pay-per-token pricing with zero base fees. GLM-5.1 is available on our Standard tier, which is optimized for high-capability reasoning and complex tasks. The pricing is $1.40 per million input tokens and $4.40 per million output tokens.

This model is hosted in our eu-north1 region to ensure strict data privacy. This guarantees that your proprietary codebases and generated outputs remain entirely within European borders. This avoids the compliance risks associated with routing sensitive engineering data through US-based infrastructure, while still accessing one of the most powerful open-weight coding models available today.

Agentic engineering and long-horizon tasks

GLM-5.1 was engineered by Z.ai specifically for sustained, multi-step software development. Unlike standard conversational models that exhaust their repertoire early, GLM-5.1 is built to stay effective over much longer horizons. The model can work continuously and autonomously on a single task for up to 8 hours. It successfully completes the full loop from initial planning and execution to iterative optimization, making it highly effective for agentic workflows where a system must run experiments, read results, and identify blockers.

Complex software development

The model excels at complex software engineering tasks, repository generation, and terminal-based automation. It handles ambiguous problems with precise judgment, breaking down large architectural challenges into manageable components. By revising its strategy through repeated iteration, GLM-5.1 sustains optimization over hundreds of rounds and thousands of tool calls. This makes it an exceptional choice for backend refactoring and applied machine learning research.

Tool calling and structured output

To support its agentic capabilities, GLM-5.1 features robust native support for function calling and structured data generation. It integrates seamlessly with the Model Context Protocol (MCP) and can reliably output complex JSON structures required by external APIs. This precision in tool use allows developers to connect the model directly to IDEs and continuous integration pipelines, enabling true autonomous execution without constant human intervention.

GLM-5.1 benchmark results

GLM-5.1 establishes a new state-of-the-art for open-weight models in software engineering, competing directly with the most advanced proprietary systems. Its performance on industry-standard coding evaluations demonstrates a significant leap over its predecessor, GLM-5, and places it ahead of several major frontier models.

Source: Z.ai GLM-5.1 Technical Announcement and GitHub Repository.

When evaluating these numbers, the SWE-Bench Pro score is the most critical metric for engineering teams. By scoring 58.4%, GLM-5.1 outperforms both GPT-5.4 and Claude Opus 4.6 on complex, real-world GitHub issue resolution. This marks a rare instance where an open-source model surpasses the leading closed-source US models on a rigorous software engineering benchmark.

Compared to its sibling model, GLM-5, the 5.1 release shows a marked improvement in sustained execution. While GLM-5 was already a strong performer, it often plateaued during long-horizon tasks. GLM-5.1 resolves this by maintaining its reasoning quality over thousands of tool calls, making the benchmark scores reflective of actual production reliability rather than just isolated test performance.

Production configuration for GLM-5.1

When deploying GLM-5.1 for enterprise workloads, understanding its context limits and pricing structure is essential for optimizing your architecture. The model supports a massive 200,000-token context window (specifically 202,752 tokens), allowing you to input entire code repositories, extensive API documentation, or long execution logs in a single prompt. Furthermore, it supports a maximum output of 128,000 tokens, which is critical for tasks like full repository generation where standard models would cut off prematurely.

On Lyceum, GLM-5.1 is served on our Standard tier. This tier is dedicated to high-capability models that require significant compute resources to execute deep reasoning and complex agentic workflows. The model is hosted in our eu-north1 region, ensuring that your data processing complies with strict European data residency requirements.

The pricing for GLM-5.1 is highly competitive for a frontier-class model: $1.40 per million input tokens and $4.40 per million output tokens. In an agentic coding task where you provide 15,000 tokens of context and the model generates a 3,000-token refactored file. The input cost would be $0.021, and the output cost would be $0.013, resulting in a total cost of just $0.034 per task. Because we bill per-token, you only pay for the exact compute consumed.

Why run GLM-5.1 on Lyceum

For European enterprises and AI startups, running a 754B parameter model like GLM-5.1 locally is prohibitively expensive, requiring massive capital expenditure on hardware. Conversely, using US-based API providers introduces unacceptable compliance risks for sensitive data. Lyceum bridges this gap by offering GLM-5.1 on our EU-sovereign GPU cloud.

By routing your inference traffic through our eu-north1 region, you guarantee that your proprietary source code, internal architecture documents, and customer data never leave the European Union. This provides a clear path to GDPR compliance for LLM inference, which is a critical requirement for teams operating in regulated industries like healthcare, finance, and automotive manufacturing.

Furthermore, Lyceum provides an open-stack, transparent infrastructure. Because we own our GPU hardware, we pass structural cost advantages directly to you through our per-token billing model. You get the performance of a frontier model without the vendor lock-in of proprietary black-box ecosystems.

Switching to Lyceum is frictionless. Our Serverless Inference API is a drop-in replacement for the OpenAI SDK, meaning your engineers can migrate existing agentic workflows to GLM-5.1 in minutes. You benefit from scale-to-zero economics, paying only when the model is actively processing tokens, with no minimum commitments or egress fees.