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

Get started: call GLM-5.1 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 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="<the base URL shown in your Lyceum dashboard>",
 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 serverless inference API, priced per token. 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, so requests to GLM-5.1 are processed in European data centres, with no training on customer data and nothing retained after processing. This can reduce transfer-related 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.

What GLM-5.1 is good at

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. Z.ai reports that the model can work continuously and autonomously on a single task for up to 8 hours, completing the full loop from planning and execution to iterative optimization, which makes it effective for agentic workflows where a system must run experiments, read results, and identify blockers. The later release is documented separately in our notes on GLM-5.2 specs, benchmarks and how to run it.

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 smoothly 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.

Limitations and trade-offs

Where GLM-5.1 falls short

While GLM-5.1 is a breakthrough in open-weight engineering models, its massive scale and specialized architecture introduce specific trade-offs. First, inference speed can be a limiting factor. Because it is a Mixture-of-Experts model with 754B parameters and 40B active per token that relies heavily on deep reasoning steps, time-to-first-token and overall generation speed are noticeably slower than smaller, chat-optimized models. Developers using it for real-time, user-facing applications may find the latency too high, as it is fundamentally designed for asynchronous, long-horizon background tasks rather than instant conversational responses.

Second, the hardware requirements for self-hosting are extreme. Running the unquantized model requires about 1.5TB of disk space and more VRAM than the 640GB in a single 8x H100 node, so a BF16 deployment spans several nodes. Even heavily quantized versions require hundreds of gigabytes of unified memory. This makes local deployment impractical for most teams, reinforcing the value of accessing it via a managed API.

Finally, GLM-5.1 is highly specialized for software engineering, coding, and mathematical reasoning. If your workload consists primarily of creative writing, general summarization, or basic customer support routing, using a 754B parameter model is computationally wasteful. For those simpler tasks, smaller models will provide faster responses at a fraction of the cost.

Benchmarks and how it compares

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.

Every figure above is taken from the GLM-5.1 model card on Hugging Face, read 3 August 2026.

When evaluating these numbers, the SWE-Bench Pro score is the most critical metric for engineering teams. By scoring 58.4, GLM-5.1 edges past both Claude Opus 4.6 and Gemini 3.1 Pro on complex, real-world GitHub issue resolution. That is a rare instance of an open-weight model leading closed-source frontier systems on a rigorous software engineering benchmark, though the same card puts it behind Claude Opus 4.6 on NL2Repo and behind both on Terminal-Bench 2.0.

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 isolated test performance.

Using it in production

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 Serverless Inference, billed per token, with no service tiers to pick between. The model itself sets the trade-off: it is built for deep reasoning and complex agentic workflows rather than raw throughput. GLM-5.1 is hosted in our eu-north1 region, so requests to it are processed in European data centres.

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. Consider 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 $0.013, for a total of $0.034 per task. Because we bill per-token, you only pay for the exact compute consumed.

Running GLM-5.1 on EU-sovereign infrastructure

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 can introduce Chapter V transfer 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, your prompts and outputs are processed in European data centres, with no training on customer data and nothing retained after processing. 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 serve GLM-5.1 from European data centres in Spain, Paris and the Nordics and bill per token with no base fee, structural cost advantages reach you directly. 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.

Sources

[1] GLM-5.1 on Hugging Face; [2] GLM-5 GitHub Repository; [3] NVIDIA NIM: GLM-5.1 Model; [4] Ollama Library: GLM-5.1