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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

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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. API Yi now offers glm-5.1 with OpenAI-compatible API access.

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Detailed Analysis

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Core Features

SWE-Bench Pro #1

58.4 score beats all closed-source flagships, the first open-source model to top SWE-Bench Pro

744B MoE Architecture

744B total / 256 experts / 8 active, ~40B effective compute, balancing performance and efficiency

8-Hour Long-Horizon Tasks

Autonomously handle a single coding task for up to 8 hours — full plan/execute/test/optimize loop

MIT License

Weights on HuggingFace and ModelScope, with vLLM and SGLang inference support

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Benchmark Performance

BenchmarkGLM-5.1Comparison
SWE-Bench Pro58.4Global #1, beats GPT-5.4 / Claude Opus 4.6 / Gemini 3.1 Pro
Terminal-Bench 2.063.5Top-tier terminal operations
NL2Repo42.7Repo-level code generation
CyberGym68.7Security coding benchmark
BrowseComp68.0Browser agent tasks
vs GLM-5+28%Major coding capability jump over predecessor
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.
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

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Technical Specifications

ParameterGLM-5.1
ArchitectureMoE (Mixture of Experts)
Total Parameters744B
Expert Count256 (8 active per token)
Active Parameters~40B
Context Window200,000 tokens
Max Output128,000 tokens
Training Hardware100,000 Huawei Ascend 910B
LicenseMIT License
Model Nameglm-5.1

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Practical Applications

Long-Horizon Coding

8-hour continuous coding tasks, ideal for complex refactoring, large feature development, automated code migration

Coding Agent

#1 on SWE-Bench Pro — an excellent open-source alternative to Claude Code, Cursor, and similar tools

Code Security Audit

CyberGym 68.7 security coding capability, suitable for code audits, vulnerability analysis, and security fixes

On-Premise Deployment

MIT-licensed open source — enterprises can download weights for local deployment with full data control

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Code Examples

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)

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);

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Best Practices

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.
  • 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

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Pricing & Availability

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Pricing

ItemPrice
Input$1.00 / M tokens
Output$3.20 / M tokens

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Deposit Bonus

Current deposit bonus promotion is ongoing — the more you deposit, the bigger the bonus. See Deposit Promotions for details.

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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
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.