GLM-5.2 Launch: Zhipu's 1M-Context Flagship Coding Model

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Key Takeaways

• 1M context window: Up from GLM-5.1’s 200K to 1,000,000 tokens — large enough to hold project-level engineering context
• Top open-weight intelligence score: 51 on the Artificial Analysis Intelligence Index, the highest open-weight score
• New coding high: 81.0 on Terminal-Bench 2.1 and 62.1 on SWE-bench Pro, leading on long-horizon coding benchmarks
• 744B MoE architecture: Continues the mixture-of-experts + dynamic sparse attention design with ~40B active parameters
• Alibaba Cloud official authorized channel: Live on APIYI via Alibaba Cloud’s official relay partnership, pricing aligned to official rates — $1.142 input / $3.997 output per million tokens

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Background

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Deep Dive

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Core Features

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Performance Highlights

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Specifications

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Use Cases

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Recommended Scenarios

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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-5.2",
    messages=[
        {"role": "system", "content": "You are a senior software engineer skilled at project-level long-horizon coding."},
        {"role": "user", "content": "Refactor this project's auth module using the full repository context."}
    ],
    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-5.2",
  messages: [
    { role: "user", content: "Analyze these 700k server log lines and find the root cause." }
  ],
  max_tokens: 16384,
});

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

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Best Practices

• Project-level coding: Feed the full repo as context and let GLM-5.2 plan and execute the refactor
• Long-document analysis: Feed massive logs and multiple long documents at once for root-cause analysis and clause-conflict detection
• On-prem deployment: The MIT license lets enterprises self-host the weights with vLLM or SGLang for efficient inference

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Pricing & Availability

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Pricing

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Stack Recharge Bonuses

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Summary & Recommendations

• Developers and teams needing 1M context for project-level coding and long-document analysis
• Users seeking a cost-effective open alternative to Claude Code / GPT-5.5
• Teams building long-horizon coding agents and autonomous workflows
• Enterprises needing on-prem deployment with full data control