GLM-5.1 Open Source: #1 on SWE-Bench Pro (What to Know)

Last updated: April 2026

• GLM-5.1 is now open-source under the MIT license. It is the first open-weight model to claim the top score on SWE-Bench Pro (58.4), surpassing GPT-5.4 (57.7) and Claude Opus 4.6 (57.3) on that benchmark.
• The model was trained entirely on Huawei Ascend 910B chips with zero Nvidia hardware involvement — a significant milestone for non-Western AI compute infrastructure.
• At 744 billion parameters (40 billion active), GLM-5.1 is not a model most people can run locally. Practical deployment requires enterprise-grade GPU clusters. For most developers, the value is in API access at a fraction of proprietary model pricing.

What Is GLM-5.1 and Why Does It Matter

GLM-5.1 is a post-training upgrade to the GLM-5 foundation model, built by Z.ai (formerly Zhipu AI). The architecture is unchanged from GLM-5: a 744 billion parameter Mixture-of-Experts model with 40 billion active parameters per token, a 200,000-token context window, and a 131,072-token maximum output length. The upgrade targets coding and agentic performance specifically through refined reinforcement learning and alignment — not additional pre-training.

Z.ai is notable for several reasons. The company, originally a Tsinghua University spinoff, completed a Hong Kong IPO on January 8, 2026, raising approximately HKD 4.35 billion (roughly $558 million USD). That made Zhipu the first publicly traded foundation model company in the world, with a market valuation around $31.3 billion. The capital has visibly accelerated their release cadence: GLM-5 launched on February 11, GLM-5-Turbo (a closed-source agent-focused variant) on March 15, the GLM-5.1 API on March 27, and the open-source weights on April 7.

The weights are available on HuggingFace under the MIT license at huggingface.co/zai-org/GLM-5.1. MIT is one of the most permissive open-source licenses available — no usage restrictions, no commercial limitations, full right to inspect, modify, and redistribute the model.

Benchmark Breakdown: Where GLM-5.1 Stands

The headline numbers from today's announcement represent a significant jump from the initial March 27 benchmarks. Z.ai's updated coding evaluation places GLM-5.1 at the top of SWE-Bench Pro and competitive across other major coding benchmarks.

The coding composite score (54.9) aggregates performance across SWE-Bench Pro, Terminal-Bench 2.0, and NL2Repo — three benchmarks that measure different aspects of real-world coding ability. GLM-5.1 ranks third globally on the composite behind GPT-5.4 (58.0) and Claude Opus 4.6 (57.5), but takes the top spot on SWE-Bench Pro specifically with a score of 58.4.

Z.ai also demonstrated GLM-5.1's performance on the Vector-DB-Bench optimization task, where the model reached 21.5k queries per second over 600+ optimization iterations and 6,000+ tool calls — approximately 6x the performance of a standard 50-turn session. This kind of sustained, iterative optimization is where the model's long-horizon capability shows its value.

A necessary caveat: several of these benchmark numbers are self-reported by Z.ai. Independent verification of the updated April 7 figures is still pending. That said, the GLM-5 base model already demonstrated externally verified results that back up Z.ai's claims — including 77.8% on SWE-bench Verified (the highest score among any open-source model on that benchmark at the time) and leading positions on BrowseComp and MCP-Atlas. Z.ai has a track record of benchmark claims that hold up to third-party testing.

The 8-Hour Autonomous Execution Claim

One of GLM-5.1's most ambitious features is its ability to run autonomously for up to 8 hours, refining strategies through thousands of iterations using a self-review loop. Z.ai demonstrated this by having the model build a functional Linux desktop environment from scratch — planning the architecture, writing code, testing, identifying problems, and iterating — without human intervention.

This is a fundamentally different use case than the quick autocomplete or single-turn code generation that most developers associate with AI coding tools. Long-horizon autonomous execution matters for tasks like full codebase refactoring, complex debugging across distributed systems, and building complete applications from a specification. It is also the area where GLM-5.1's slower inference speed (approximately 44.3 tokens per second, the slowest among frontier models) becomes a feature rather than a bug — the model is designed to think carefully and iterate, not to generate text as fast as possible.

The Huawei Ascend Story: AI Without Nvidia

The training infrastructure behind GLM-5.1 is arguably as significant as the model itself. The entire GLM-5 family — including 5.1 — was trained on approximately 100,000 Huawei Ascend 910B chips using the MindSpore framework. No Nvidia GPUs were used at any point in the training process.

This matters because Zhipu AI has been on the US Entity List since January 2025, meaning the company has no legal access to Nvidia's data center GPUs (H100, H200, B200) that power training runs at virtually every other frontier AI lab globally. The fact that Z.ai produced a model competitive with GPT-5.4 and Claude Opus 4.6 on a fully domestic Chinese compute stack demonstrates that the US export controls on AI chips, while impactful, have not prevented China from reaching frontier-class AI performance.

For anyone following the global AI landscape, this is a geopolitically significant data point. It also has practical implications for the broader open-source AI ecosystem: more capable training infrastructure options means more competition, which historically benefits end users through lower prices and more model choices.

Can You Run GLM-5.1 Locally?

The honest answer for most readers: no. Not on consumer hardware.

The full BF16 (unquantized) GLM-5.1 model requires approximately 1.49 terabytes of storage and a comparable amount of GPU VRAM to run at full precision. In practice, even the FP8 quantized version requires 8-way tensor parallelism across enterprise GPUs — think 8x Nvidia H200s or H20s. This is data center hardware, not something that fits under your desk.

For context, if you are interested in running open-source AI models that actually fit on consumer hardware, our guide to the best hardware for running local AI models covers what you actually need at every budget level — from Raspberry Pi to dedicated GPU builds. Google's Gemma 4 model family, released just days before GLM-5.1, includes models that run on a smartphone. And our mini PC guide for local AI covers dedicated always-on AI servers starting under $300.

If you do have the infrastructure to self-host, GLM-5.1 is deployable via vLLM, SGLang, and xLLM with Docker support. The deployment commands are straightforward:

vllm serve zai-org/GLM-5.1 \
  --tensor-parallel-size 8 \
  --gpu-memory-utilization 0.85 \
  --served-model-name glm-5.1

For anyone running large model inference, fast and spacious NVMe storage is essential for loading model weights. The Samsung 990 EVO Plus 4TB is one of the better options for storing multiple large model files locally, and the Samsung 990 EVO Plus 2TB remains the best value for most setups. (Affiliate disclosure: ModemGuides earns from qualifying Amazon purchases.)

The practical reality is that most developers will access GLM-5.1 through API providers — and the pricing is where the model becomes genuinely compelling for a much wider audience.

How to Access GLM-5.1 Today

There are several ways to use GLM-5.1 right now, ranging from free-tier access to direct API integration.

GLM-5.1 is compatible with Claude Code, Cursor, Cline, Kilo Code, OpenCode, and OpenClaw via Z.ai's API compatibility layer. If you are already using one of these tools, switching to GLM-5.1 is a configuration change, not a platform migration.

The pricing comparison against proprietary alternatives is stark. Claude Opus 4.6 costs $5 per million input tokens and $25 per million output tokens. GPT-5.4 is priced similarly. GLM-5.1 through the Z.ai API is $1.00/$3.20 — roughly 5-8x cheaper depending on your input/output ratio. For developers running agents continuously or processing large codebases, that cost difference compounds fast.

Security Considerations for Open-Weight Models

Whenever a major open-source model drops — especially one originating from a company on the US Entity List — security questions are inevitable. Here is a practical, non-alarmist assessment.

First, what open weights actually means from a security perspective: model weights are passive numerical data files. They are not executable code. The files themselves cannot install malware, exfiltrate data, or open network connections. The security surface of running an AI model locally is in the inference stack — the software that loads and runs the weights (vLLM, SGLang, Ollama, etc.) — not in the weights themselves. This is an important distinction that often gets lost in headlines.

Second, MIT-licensed open weights are inherently more trustworthy than closed-source alternatives in one specific and important way: they can be independently inspected, tested, and audited by anyone. The global AI research community routinely examines open model weights for embedded behaviors, biases, and anomalies. Closed-source models, by definition, cannot receive this level of scrutiny. You are trusting the provider's claims about what the model does and does not do.

That said, reasonable security practices apply regardless of model origin. If you are deploying any AI model on infrastructure you control, the same principles that apply to all local AI deployments apply here:

• Run inference in a sandboxed environment (Docker containers are the standard approach)
• Isolate the AI workload on a dedicated VLAN or network segment, separate from devices that handle sensitive data
• Monitor outbound network traffic from any machine running inference — model inference should not be making unexpected external connections
• Keep your inference framework (vLLM, SGLang) updated, as vulnerabilities in these tools have been discovered and patched regularly

These are the same best practices we recommend for any local AI deployment, and the same principles behind our OpenClaw security guide. The recent Claude Code source leak and the Cisco/Trivy supply chain breach are reminders that security risks in AI tooling are not limited to any one country or company — they are an infrastructure-wide concern.

What GLM-5.1 Means for Open-Source AI in 2026

The trajectory is clear and accelerating. In 2023, open-source AI models were roughly two years behind frontier proprietary models. In 2024, that gap closed to about one year. In 2025, six months. And on April 7, 2026, an open-source model claimed the top score on one of the most respected coding benchmarks in AI — beating both GPT-5.4 and Claude Opus 4.6.

GLM-5.1 is not an isolated achievement. Chinese AI labs — Z.ai, Alibaba (Qwen), DeepSeek, and Moonshot AI (Kimi) — now hold most of the top positions among open-weight models on major leaderboards. Google's Gemma 4, released under Apache 2.0, is pushing open-source capability at the smaller end of the model spectrum. Meta's Llama continues to iterate. The competitive pressure on closed-source providers is real and growing.

For anyone who cares about digital sovereignty — the ability to run capable AI without depending on a single cloud provider, a single country's export policy, or a single company's terms of service — this is unambiguously good news. More high-quality open models means more choices, more competition on pricing, and a stronger foundation for local-first AI infrastructure.

The technical story underneath the benchmarks is worth paying attention to as well. The 28% coding performance improvement from GLM-5 to GLM-5.1 came entirely from post-training optimization — same base model, same architecture, same parameter count. Z.ai's progressive alignment pipeline (multi-task supervised fine-tuning, multi-stage reinforcement learning, and cross-stage distillation) is producing substantial real-world gains without the enormous cost of pre-training a new model from scratch. This suggests that the next wave of open-source AI improvement may come more from better training techniques than from bigger models — a dynamic that favors open research and collaboration.

Frequently Asked Questions

What is GLM-5.1?

GLM-5.1 is Z.ai's (formerly Zhipu AI) latest open-source AI model, released on April 7, 2026. It is a post-training upgrade to GLM-5, built on a 744 billion parameter Mixture-of-Experts architecture with 40 billion active parameters, a 200K token context window, and a 131K token maximum output. It targets coding and agentic task performance.

Is GLM-5.1 open source?

Yes. The model weights are available on HuggingFace under the MIT license, one of the most permissive open-source licenses available. You can download, inspect, modify, and commercially use the model without restrictions.

How does GLM-5.1 compare to Claude Opus 4.6?

On SWE-Bench Pro (a coding benchmark), GLM-5.1 scores 58.4 compared to Claude Opus 4.6's 57.3 — giving the open-source model the top position on that specific benchmark. On the broader coding composite (which includes Terminal-Bench 2.0 and NL2Repo), Claude Opus 4.6 leads at 57.5 versus GLM-5.1's 54.9. Claude also retains advantages in ultra-long context processing (1M tokens), extreme-depth reasoning, and complex multi-step agent workflows. The practical recommendation for many developers is to use GLM-5.1 for daily coding tasks at lower cost and reserve Claude for tasks requiring maximum capability.

Can I run GLM-5.1 on my home computer?

Not at full scale. The model requires approximately 1.49 terabytes of storage and enterprise-grade multi-GPU infrastructure (8x Nvidia H200 or equivalent) for inference. Consumer hardware — even high-end gaming PCs — cannot run GLM-5.1. If you want to run AI models locally on affordable hardware, see our local AI hardware guide and best mini PCs for local AI, which cover models that actually fit on consumer devices.

Is it safe to use AI models from Chinese companies?

Open-weight models are inherently more transparent than closed-source alternatives because anyone can inspect the weights. Model weights are passive data files, not executable code — they cannot independently install software or access your network. The security risks in running any AI model (regardless of origin) are in the inference software stack and your deployment configuration, not the weights themselves. Apply standard security practices: run in Docker containers, isolate on a separate VLAN, and monitor network traffic. These are the same precautions we recommend for all local AI deployments.

How much does GLM-5.1 cost to use?

The Z.ai API charges $1.00 per million input tokens and $3.20 per million output tokens — approximately 5-8x cheaper than Claude Opus 4.6's pricing of $5/$25 per million tokens. The GLM Coding Plan offers subscription access starting at $10/month (Lite) up to $30/month (Max, unlimited requests). The model weights are free to download under the MIT license if you have the infrastructure to self-host.

What is the Huawei Ascend 910B?

The Huawei Ascend 910B is a Chinese-manufactured AI training chip designed as an alternative to Nvidia's data center GPUs (H100, H200, B200). Z.ai used approximately 100,000 Ascend 910B chips to train the entire GLM-5 model family. The chip is significant because it demonstrates that frontier-class AI training is achievable on non-Nvidia, non-Western hardware — a geopolitically important milestone given US export controls on AI chips to China that have been in effect since 2022.