> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apiyi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# GLM-5.1 Launch: Zhipu's Most Powerful Open-Source Coding Agent

> Zhipu Z.AI's flagship open-source model GLM-5.1 is now on APIYI — SWE-Bench Pro 58.4 surpassing GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. 744B MoE, 200K context, autonomous 8-hour coding tasks, MIT licensed.

## Key Highlights

* **#1 on SWE-Bench Pro**: 58.4 score surpasses GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro — first open-source model to top this benchmark
* **Massive MoE Architecture**: 744B total params / 256 experts / 8 active per token, \~40B effective compute
* **Long-Horizon Tasks**: Can autonomously execute a single coding task for up to 8 hours — planning, executing, testing, and optimizing in a continuous loop
* **200K Context Window**: 200,000 token context, supports 128,000 token output
* **Open Source + Cost-Effective**: MIT licensed, API at \$1.00/M input and \$3.20/M output tokens

## Background

On April 7, 2026, Zhipu Z.AI officially released its flagship open-source model **GLM-5.1**, a major upgrade to the GLM-5 series. On the rigorous SWE-Bench Pro coding benchmark, GLM-5.1 scored 58.4 — surpassing GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro to become the new global leader, and the first open-source model to beat all closed-source flagships on this benchmark.

GLM-5.1 is purpose-built for "agentic engineering" and "long-horizon software development." It can autonomously work on a single coding task for up to 8 hours, continuously planning, executing, testing, and optimizing. The model was trained entirely on 100,000 Huawei Ascend 910B chips, showcasing the capability of domestic Chinese compute for frontier large-model training.

APIYI now offers `glm-5.1` with OpenAI-compatible API access.

## Detailed Analysis

### Core Features

<CardGroup cols={2}>
  <Card title="SWE-Bench Pro #1" icon="trophy">
    58.4 score beats all closed-source flagships, the first open-source model to top SWE-Bench Pro
  </Card>

  <Card title="744B MoE Architecture" icon="bolt">
    744B total / 256 experts / 8 active, \~40B effective compute, balancing performance and efficiency
  </Card>

  <Card title="8-Hour Long-Horizon Tasks" icon="clock">
    Autonomously handle a single coding task for up to 8 hours — full plan/execute/test/optimize loop
  </Card>

  <Card title="MIT License" icon="git-branch">
    Weights on HuggingFace and ModelScope, with vLLM and SGLang inference support
  </Card>
</CardGroup>

### Benchmark Performance

| Benchmark              | GLM-5.1  | Comparison                                                  |
| ---------------------- | -------- | ----------------------------------------------------------- |
| **SWE-Bench Pro**      | **58.4** | Global #1, beats GPT-5.4 / Claude Opus 4.6 / Gemini 3.1 Pro |
| **Terminal-Bench 2.0** | **63.5** | Top-tier terminal operations                                |
| **NL2Repo**            | **42.7** | Repo-level code generation                                  |
| **CyberGym**           | **68.7** | Security coding benchmark                                   |
| **BrowseComp**         | **68.0** | Browser agent tasks                                         |
| **vs GLM-5**           | **+28%** | Major coding capability jump over predecessor               |

<Info>
  Data sources: Zhipu Z.AI official docs (`docs.z.ai`), Dataconomy, Z.AI developer docs. GLM-5.1 was officially released on April 7, 2026, with independent benchmark results updated the same day.
</Info>

**vs Competitors**:

* **vs Claude Opus 4.6**: GLM-5.1 (58.4) beats Opus 4.6 (47.9) on coding benchmarks, achieving 94.6%–122% performance levels
* **vs GPT-5.4 / Gemini 3.1 Pro**: GLM-5.1 surpasses both on SWE-Bench Pro
* **Price advantage**: API pricing significantly lower than closed-source flagships, exceptional cost-effectiveness

### Technical Specifications

| Parameter             | GLM-5.1                    |
| --------------------- | -------------------------- |
| **Architecture**      | MoE (Mixture of Experts)   |
| **Total Parameters**  | 744B                       |
| **Expert Count**      | 256 (8 active per token)   |
| **Active Parameters** | \~40B                      |
| **Context Window**    | 200,000 tokens             |
| **Max Output**        | 128,000 tokens             |
| **Training Hardware** | 100,000 Huawei Ascend 910B |
| **License**           | MIT License                |
| **Model Name**        | `glm-5.1`                  |

## Practical Applications

### Recommended Use Cases

<CardGroup cols={2}>
  <Card title="Long-Horizon Coding" icon="code">
    8-hour continuous coding tasks, ideal for complex refactoring, large feature development, automated code migration
  </Card>

  <Card title="Coding Agent" icon="bot">
    \#1 on SWE-Bench Pro — an excellent open-source alternative to Claude Code, Cursor, and similar tools
  </Card>

  <Card title="Code Security Audit" icon="shield">
    CyberGym 68.7 security coding capability, suitable for code audits, vulnerability analysis, and security fixes
  </Card>

  <Card title="On-Premise Deployment" icon="server">
    MIT-licensed open source — enterprises can download weights for local deployment with full data control
  </Card>
</CardGroup>

### Code Examples

```python theme={null}
from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://api.apiyi.com/v1"
)

response = client.chat.completions.create(
    model="glm-5.1",
    messages=[
        {"role": "system", "content": "You are a senior software engineer skilled at long-horizon coding tasks."},
        {"role": "user", "content": "Refactor this Python project from monolithic to microservices architecture."}
    ],
    max_tokens=16384
)

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

```javascript theme={null}
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-api-key",
  baseURL: "https://api.apiyi.com/v1",
});

const response = await client.chat.completions.create({
  model: "glm-5.1",
  messages: [
    { role: "user", content: "Audit this codebase for security vulnerabilities and propose fixes." }
  ],
  max_tokens: 16384,
});

console.log(response.choices[0].message.content);
```

### Best Practices

<Warning>
  GLM-5.1 is purpose-built for long-horizon tasks. For long jobs, set a generous timeout (e.g. 600 seconds or more) to fully leverage its 8-hour continuous reasoning capability.
</Warning>

* **Long-horizon coding**: Pass the entire project codebase as context, let GLM-5.1 plan and execute refactoring autonomously
* **Agent workflows**: Leverage its BrowseComp 68.0 browser operation capability to build autonomous Web Agents
* **On-premise**: MIT license allows enterprises to deploy weights locally with vLLM or SGLang for efficient inference

## Pricing & Availability

### Pricing

| Item       | Price             |
| ---------- | ----------------- |
| **Input**  | \$1.00 / M tokens |
| **Output** | \$3.20 / M tokens |

### Deposit Bonus

Current deposit bonus promotion is ongoing — the more you deposit, the bigger the bonus. See [Deposit Promotions](/en/faq/recharge-promotions) for details.

## Summary & Recommendations

GLM-5.1 is currently the most powerful open-source coding Agent model, beating all closed-source flagships on SWE-Bench Pro. The 744B MoE architecture delivers an excellent balance of performance and efficiency. With up to 8-hour long-horizon task execution + 200K context + MIT open-source licensing, it's the ideal choice for coding agents, long-horizon code tasks, and enterprise on-premise deployments.

**Recommended for**:

* Developers and teams needing top-tier coding Agent capabilities
* Users seeking cost-effective open-source alternatives to Claude Opus / GPT-5.4
* Technical teams building long-horizon coding tasks and autonomous Agent workflows
* Enterprise users requiring on-premise deployment with full data control

<Info>
  Sources: Zhipu Z.AI official developer docs (`docs.z.ai`), Dataconomy, TechBriefly, OpenRouter. Data retrieved: April 9, 2026. GLM-5.1 was trained entirely on domestic Huawei Ascend 910B chips.
</Info>