GLM-5.1: Open-Source Model That Tops SWE-Bench Pro

On April 7, 2026, Z.ai (the company formerly known as Zhipu AI) released GLM-5.1 and immediately took the top spot on SWE-Bench Pro — the most demanding real-world software engineering benchmark available. With a score of 58.4, it edged out GPT-5.4 (57.7) and Claude Opus 4.6 (57.3).

That alone would be noteworthy. But what makes GLM-5.1 genuinely significant is that it is fully open-weight under an MIT license. You can download it, fine-tune it, and deploy it commercially without restriction. No usage fees, no approval process, no vendor lock-in.

This guide covers what GLM-5.1 is, how the architecture works, where it excels (and falls short), how to call it via API, and whether you should self-host or use the managed API.

Why GLM-5.1 Matters for Developers

The open-source LLM conversation has been stuck in a familiar pattern: open models are free but inferior; closed models are better but expensive and undeployable. GLM-5.1 breaks that frame — at least for software engineering tasks.

With 754 billion total parameters and only 40 billion active per token via Mixture-of-Experts routing, GLM-5.1 delivers frontier-class performance at a fraction of the inference cost. The API is priced at $1.40 per million input tokens and $4.40 per million output tokens — compared to Claude Opus 4.6 at $15.00 input and $75.00 output. That is a 17x difference on output alone.

For production teams running autonomous coding pipelines, multi-step agentic workflows, or large-scale code review tasks, that cost delta is the difference between a viable product and an unaffordable one.

The model was trained entirely on Huawei Ascend chips using the MindSpore framework — no Nvidia GPUs were involved. It represents one of the first frontier-tier models to achieve full hardware independence from the US chip supply chain.

Architecture: 754B Parameters, 40B Active

GLM-5.1 uses a Mixture-of-Experts (MoE) architecture. The full model has 754 billion parameters stored across expert layers, but only 40 billion parameters are activated for any single token during inference. A learned router decides which experts to engage for each token.

This design means:

• Memory footprint at inference is governed by active parameters (40B), not total (754B)
• Training quality benefits from the full 754B parameter space
• Throughput scales well because experts can be parallelized across hardware

The context window is 200,000 tokens for input, with a maximum of 128,000 output tokens in a single response. This is large enough to fit entire codebases, full documentation sets, or extended multi-turn agentic sessions without truncation.

The total model weight on Hugging Face is 1.51TB — one of the largest open-weight model files publicly available.

Benchmark Results: Where GLM-5.1 Leads

GLM-5.1 was evaluated on a wide set of agentic and coding benchmarks. The SWE-Bench Pro result is the headline, but the full picture reveals a model specifically tuned for tool-using, long-horizon tasks.

SWE-Bench Pro differs from the standard SWE-bench Verified in that it uses industrial-scale repositories with greater complexity and fewer test scaffolds. It is a harder benchmark and a better proxy for real software engineering work.

Terminal-Bench 2.0 measures a model's ability to navigate and operate inside shell environments autonomously — a core capability for agentic coding agents. GLM-5.1 scores 66.5 when paired with the Claude Code harness, suggesting the model's strengths compound with strong scaffolding.

CyberGym tests security-relevant coding tasks across 1,507 challenge instances. The 68.7 score is a single-run result, not a multi-attempt aggregate.

Where GLM-5.1 does not lead: on SWE-bench Verified (77.8% vs Claude Sonnet 4.6's 79.6%), suggesting the model has more variance on shorter-horizon tasks with provided test suites. For pure single-shot reasoning, Claude Sonnet 4.6 remains slightly ahead.

The 8-Hour Autonomous Execution Capability

The feature Z.ai emphasizes most heavily is sustained autonomous execution — the ability to work continuously on a single task for up to eight hours.

In a published demo, GLM-5.1 built a full Linux-style desktop environment from scratch during a single autonomous session. The model completed 655 tool-invocation iterations, building a functional file browser, terminal emulator, text editor, system monitor, and playable mini-games — without human intervention.

On KernelBench Level 3 — a benchmark that requires optimizing real machine learning kernels — GLM-5.1 performed thousands of iterative tool-driven optimizations, achieving a 3.6x geometric mean speedup over baseline.

On a vector database optimization task, the model ran 655 autonomous iterations, boosting query throughput to 6.9x the initial production version's performance.

This is qualitatively different from models that can handle multi-step tasks in 10-20 turns. GLM-5.1 is designed to operate as an autonomous software engineer for the duration of a working day, not as a responsive assistant that completes tasks on demand.

How to Use GLM-5.1 via API

GLM-5.1 exposes an OpenAI-compatible API. You can call it using the standard openai Python SDK by overriding the base URL.

Install the SDK:

pip install openai

Basic usage:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_Z_AI_API_KEY",
    base_url="https://open.bigmodel.cn/api/paas/v4/"
)

response = client.chat.completions.create(
    model="glm-5.1",
    messages=[
        {
            "role": "system",
            "content": "You are an expert software engineer. Think step by step."
        },
        {
            "role": "user",
            "content": "Write a Python function that finds the longest increasing subsequence in O(n log n) time."
        }
    ],
    temperature=0.2,
    max_tokens=4096
)

print(response.choices[0].message.content)

Get your API key at open.bigmodel.cn. The model name is glm-5.1 — do not use a versioned suffix.

Pricing note: Peak hours are 14:00–18:00 UTC+8 (Beijing time), during which usage is billed at 3x quota. Off-peak is 2x. Through the end of April 2026, off-peak is billed at 1x as a promotional rate.

Agentic workflow example with tool use:

tools = [
    {
        "type": "function",
        "function": {
            "name": "run_bash",
            "description": "Execute a bash command and return the output",
            "parameters": {
                "type": "object",
                "properties": {
                    "command": {
                        "type": "string",
                        "description": "The bash command to run"
                    }
                },
                "required": ["command"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="glm-5.1",
    messages=[
        {
            "role": "user",
            "content": "Analyze the Python files in the current directory and identify any functions with cyclomatic complexity above 10."
        }
    ],
    tools=tools,
    tool_choice="auto"
)

Self-Hosting GLM-5.1: Is It Practical?

The MIT license makes self-hosting legally straightforward. The hardware requirement is the real constraint.

At 1.51TB, GLM-5.1 requires a multi-node GPU cluster to run at acceptable latency. A realistic production self-host configuration:

• Minimum: 8x H100 80GB (640GB VRAM) — model loads but throughput is limited
• Recommended: 16x H100 or equivalent Huawei Ascend 910C cluster — 200K context with reasonable throughput
• Training continuation: Use MindSpore on Ascend or adapt to PyTorch (community work ongoing)

For most engineering teams, self-hosting GLM-5.1 for production use is not yet practical unless you already operate a large GPU cluster. The managed API at $1.40/$4.40 per million tokens is more accessible.

A reasonable middle path: use the managed API for development and low-to-medium volume production, and revisit self-hosting if your usage volume crosses the point where the hardware investment pays back within six months.

Community-optimized quantized versions (4-bit, 8-bit) are being developed but were not officially released as of April 2026. When available, these would substantially reduce the VRAM requirement.

Cost Comparison: GLM-5.1 vs Closed-Source Alternatives

The cost advantage of GLM-5.1 is most visible in agentic and high-output workloads. A task that generates 1 million output tokens costs:

For an autonomous coding agent that generates 500K output tokens per task and runs 100 tasks per day, the monthly output cost difference between Claude Opus 4.6 and GLM-5.1 is approximately $106,500 per month. That is a meaningful difference for production AI systems.

The tradeoff is reliability and ecosystem maturity. Claude and GPT have larger communities, more integrations, and longer track records in production. GLM-5.1 is a newer entrant at the frontier tier.

Common Mistakes When Using GLM-5.1

Using the wrong model ID. The model name is glm-5.1 — not glm5.1, glm-5-1, or any versioned variant. The API will return a model not found error with incorrect IDs.

Underestimating peak-hour cost. The 3x quota multiplier during Beijing business hours (14:00–18:00 UTC+8) is significant. If you are running high-volume jobs, schedule them for off-peak hours or use the off-peak promotional rate through April 2026.

Treating it like a short-context model. GLM-5.1's 200K context window is a genuine differentiator. For agentic tasks, pass the full codebase context rather than chunking it — the model performs better with complete information than with retrieval-augmented partial context.

Setting temperature too high for coding tasks. For software engineering and code generation, temperatures above 0.3 increase variance without improving quality. The model's strength is precision in long-horizon tasks; randomness undermines this.

Not using tool definitions for agentic tasks. GLM-5.1 is explicitly optimized for tool use. For multi-step tasks, define tools (bash, file I/O, web fetch) and let the model invoke them iteratively rather than generating the entire solution in one pass.

FAQ

Q: Is GLM-5.1 actually better than Claude Opus 4.6?

On SWE-Bench Pro, yes — GLM-5.1 scores 58.4 versus Claude Opus 4.6's 57.3. On SWE-bench Verified, Claude Sonnet 4.6 scores higher at 79.6% versus GLM-5.1's 77.8%. The honest answer is that GLM-5.1 leads on long-horizon agentic tasks, while Claude holds its own (or leads) on shorter-horizon tasks with provided test infrastructure. For most practical software engineering automation, the gap is small enough that cost becomes the deciding factor.

Q: Can I use GLM-5.1 commercially without a license fee?

Yes. The MIT license permits commercial use, modification, and redistribution without royalties. You must retain the copyright notice, but there are no usage restrictions or approval requirements.

Q: What hardware do I need to self-host GLM-5.1?

At minimum, you need approximately 640GB of VRAM (8x H100 80GB) to load the full model. Practical production deployments typically use 16+ H100-class GPUs. Community quantized versions that would reduce this requirement were in development but not officially released as of April 2026.

Q: How does the 200K context window compare to other frontier models?

Gemini 3.1 Ultra has a 2 million token context window, which is 10x larger. Claude Opus 4.6 and GPT-5.4 also have larger windows. GLM-5.1's 200K is competitive with the previous generation of flagship models but is not the largest available in April 2026.

Q: Why was it trained on Huawei chips instead of Nvidia?

Z.ai's use of Huawei Ascend 910 chips and the MindSpore framework was driven by US export restrictions on Nvidia H100 and A100 chips to China. The achievement demonstrates that frontier-tier training is possible without Nvidia GPUs, which has significant implications for AI hardware supply chain diversification.

Q: Is GLM-5.1 safe to use in production?

Z.ai has not published a detailed safety evaluation comparable to Anthropic's model cards. For applications requiring rigorous safety guarantees — particularly in consumer-facing or regulated contexts — the absence of published red-teaming results is a gap to address before deployment. For internal developer tooling and coding automation, this is less likely to be a blocking concern.

Key Takeaways

• GLM-5.1 leads SWE-Bench Pro at 58.4, narrowly above GPT-5.4 (57.7) and Claude Opus 4.6 (57.3) — the first open-weight model to hold the top position on this benchmark
• MIT license with no restrictions means commercial deployment, fine-tuning, and redistribution are all permitted without fees
• 754B total / 40B active via MoE architecture delivers frontier-tier quality with lower inference cost than a dense model of comparable capability
• API pricing is 17x cheaper than Claude Opus 4.6 on output tokens, making it viable for high-volume agentic pipelines
• 8-hour autonomous execution is a documented, demonstrated capability — not a marketing claim
• Self-hosting requires significant hardware (640GB+ VRAM minimum); the managed API is the practical path for most teams
• Use OpenAI SDK with base URL override — the integration is straightforward for teams already using OpenAI-compatible tooling