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TL;DR
Zhipu AI GLM-5.2 is a flagship open-weight coding and reasoning model built on a Mixture-of-Experts (MoE) architecture, with roughly 744 billion total parameters and a 1-million-token context window.
GLM-5.2 is now available on NeevCloud through the Model API, with no deployment or GPU setup required.
On independent evaluations like the Artificial Analysis Intelligence Index, GLM-5.2 ranks as the top open-weights model, trading blows with closed frontier models like Claude Opus 4.8 and GPT-5.5 at a fraction of the cost.
GLM-5.2 pricing on NeevCloud is ₹89.06 per million input tokens and ₹287.29 per million output tokens.
The GLM-5.2 API uses an OpenAI-compatible API format, making integration with cURL, Python, and JavaScript straightforward.
GLM-5.2, developed by Zhipu AI, is now available on NeevCloud through the GLM-5.2 Model API. This guide covers its key capabilities, GLM-5.2 pricing on NeevCloud, and how to use the GLM-5.2 API with cURL, Python, and JavaScript.
Frontier AI models are becoming increasingly capable, but developers still face practical challenges around cost, context length, and access to open models. Long-context workloads can become expensive, while many of the most capable models are closed and require developers to work within specific platforms or APIs.
For software development and agentic applications, developers need models that can handle large amounts of context, sustain reasoning across multiple steps, and integrate easily into existing workflows. Open-weight models can also give developers more flexibility when building AI applications and integrating them into existing workflows.
This is where GLM-5.2 stands out. It combines a 1-million-token context window, strong coding and reasoning capabilities, agentic tool use, and an OpenAI-compatible API, making it a practical option for long-context and software development workloads.
GLM-5.2 is the flagship model from Zhipu AI. It's the latest release in the GLM series, built specifically for long-horizon coding, multi-step reasoning, and agentic tool-use - workloads that stretch across a lot of context and a lot of steps, rather than a single quick reply.
Mixture-of-Experts (MoE) architecture - GLM-5.2 has roughly 744 billion total parameters, but only about 40 billion are "active" for any given token. This is what lets it deliver large-model capability at a much smaller model's running cost.
1-million-token context window - Enough to hold an entire codebase, a long document set, or a multi-day conversation history in a single request.
Two thinking modes: High and Max - Dial reasoning effort up or down per request, trading speed for depth as needed.
IndexShare sparse attention - A technique that reduces per-token compute at long context, keeping 1M-token requests fast and cost-efficient.
Up to 131,072 output tokens per response.
Native function calling and JSON mode - Built for structured, tool-driven applications and agent workflows.
MIT license - fully open-weight, with no regional restrictions.
"Frontier models" refers to the current top tier of AI models by capability - right now, that's models like Claude Opus 4.8, GPT-5.5, and Gemini 3.1 Pro. What makes GLM-5.2 notable is that it's an open-weight model competing directly with these closed frontier models on real benchmarks:
| Benchmark | What it measures |
|---|---|
| SWE-bench Pro | Tests how well the model can understand real-world software issues and fix bugs in existing codebases. |
| Terminal-Bench 2.1 | Evaluates how well the model can complete tasks in a command-line or terminal environment. |
| NL2Repo | Tests the ability to turn natural-language instructions into code changes across a software repository. |
| FrontierSWE | Measures performance on long-horizon software engineering tasks that require multiple steps and sustained reasoning. |
| MCP-Atlas | Evaluates tool-use capabilities through tasks involving the Model Context Protocol (MCP). |
| Humanity's Last Exam | Tests broad reasoning and problem-solving across challenging questions and subjects. |
On independent evaluations such as the Artificial Analysis Intelligence Index, GLM-5.2 ranks as the top open-weights model, and sits on the Pareto frontier of intelligence versus cost - meaning it's the cheapest model available at its performance tier. It comes within roughly a percentage point of Claude Opus 4.8 on long-horizon coding benchmarks like FrontierSWE, while outperforming GPT-5.5 on several of the same tests.
GLM-5.2 also demonstrates strong performance across a range of software engineering and reasoning benchmarks. The model performs competitively against leading frontier models across tasks covering code generation, repository-level problem solving, tool use, and complex reasoning.
These results highlight GLM-5.2’s ability to handle diverse technical workloads, making it suitable for coding, agentic workflows, and other long-horizon tasks that require sustained reasoning and tool use
How to Access GLM-5.2 on NeevCloud
GLM-5.2 is available today through the NeevCloud Model API, found under the AI Inference section of the console. Like the rest of the Model API catalog, there's nothing to deploy or provision - select the model, grab your endpoint, and start sending requests.
GLM-5.2 API Endpoint and Model ID
Endpoint | https://inference.ai.neevcloud.com/v1/chat/completions |
Model ID | glm-5-2 |
GLM-5.2 Pricing on NeevCloud
| Token type | Price |
|---|---|
| Input tokens | ₹89.06 / M tokens |
| Output tokens | ₹287.29 / M tokens |
Compared to Claude Opus 4.8, GLM-5.2 on NeevCloud runs roughly 5.4x cheaper on input tokens and 8.3x cheaper on output tokens, at competitive quality on coding and agentic benchmarks. Since it's pay-per-token, you're never billed for idle GPU capacity.
GLM-5.2 Coding Model for Software Development
GLM-5.2 is designed for software engineering tasks such as fixing bugs, working across repositories, and operating in terminal environments. Its long-horizon coding capabilities make it suitable for multi-step development tasks.
Reasoning with High and Max Thinking Modes
GLM-5.2 offers High and Max thinking modes, allowing developers to balance reasoning depth, speed, and computational cost based on the complexity of the task.
GLM-5.2 Context Window: 1 Million Tokens
Its 1-million-token context window makes GLM-5.2 suitable for large codebases, technical documentation, and extended workflows without repeatedly splitting or summarizing context.
GLM-5.2 for Coding Agents and Agentic Workflows
GLM-5.2 supports agentic workflows involving planning, tool use, code changes, testing, and multiple iterations, making it useful for long-running software development tasks.
Accessing the NeevCloud Model Playground
Go to AI Inference → Model API in the NeevCloud console.
Select GLM 5.2 from the model catalog.
Click Go To Playground to send prompts interactively, or View Endpoint to get the request details for your own code.
Testing GLM-5.2 Without Writing Code
The Model Playground is the fastest way to try GLM-5.2 before writing any code - test both thinking modes, try a real coding prompt, and check the response format before wiring it into an application.
The GLM-5.2 API on NeevCloud follows an OpenAI-compatible API format, so developers can integrate it into existing applications with minimal changes.
GLM-5.2 API cURL Example
export NEEV_API_KEY="your-api-key-here"
curl --max-time 120 https://inference.ai.neevcloud.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $NEEV_API_KEY" \
-d '{
"model": "glm-5-2",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Explain inference API design in 3 short paragraphs."
}
],
"max_tokens": 2048
}'
GLM-5.2 API Integration via Python
from openai import OpenAI
client = OpenAI(
api_key="your-api-key-here",
base_url="https://inference.ai.neevcloud.com/v1"
)
response = client.chat.completions.create(
model="glm-5-2",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain inference API design in 3 short paragraphs."}
],
max_tokens=2048,
)
print(response.choices[0].message.content)
GLM-5.2 API Integration via JavaScript
const response = await fetch("https://inference.ai.neevcloud.com/v1/chat/completions", {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${process.env.NEEVCLOUD_API_KEY}`
},
body: JSON.stringify({
model: "glm-5-2",
messages: [
{ role: "user", content: "Write a Python function to check if a string is a palindrome." }
]
})
});
const data = await response.json();
console.log(data.choices[0].message.content);
Note: Store your API key as an environment variable rather than hardcoding it in source. API keys can be generated from API Keys in the NeevCloud console.
Understanding the GLM-5.2 API Response Fields
A successful call returns a response in the standard chat completions format:
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "An inference API serves as the bridge between a trained machine learning model and the end-user application. Its primary function is to accept raw input data, route it to the model for prediction, and return the generated output in a structured format...",
"role": "assistant"
}
}
],
"created": 1785331687,
"id": "chatcmpl-897ecee0-32d1-4df7-b8f7-8b7be3d4eab1",
"model": "zai-org/GLM-5.2",
"object": "chat.completion",
"usage": {
"completion_tokens": 753,
"prompt_tokens": 30,
"total_tokens": 783
}
}
id / object - unique identifiers for the request.
model - should match zai-org/GLM-5.2.
choices[0].message.content - the generated completion.
finish_reason - "stop" means the model finished naturally, rather than being cut off.
usage - token counts for the request, useful for checking cost against the ₹89.06 / ₹287.29 per-million-token pricing above.
How to Confirm a Successful Inference Request
To verify a deployment end to end, check that the model field matches glm-5-2, choices[0].message.content is non-empty and coherent, and finish_reason reads "stop". If any of these look off, double-check your model ID, endpoint URL, and API key before troubleshooting further.
GLM-5.2 is a strong choice when your workload requires more than short, single-turn responses. Consider GLM-5.2 when you need:
Long-context processing: Work with large codebases, technical documentation, or extended task histories using its 1-million-token context window.
Coding agents: Build AI coding agents that can reason through tasks, use tools, modify files, run tests, and iterate across multiple steps.
Cost-efficient frontier-level performance: Get strong coding and reasoning capabilities with pay-per-token pricing, without managing GPU infrastructure.
OpenAI-compatible deployments: Integrate GLM-5.2 into existing applications using familiar API patterns with minimal changes.
Long-horizon software development: Handle complex engineering tasks that require sustained reasoning, repository-level changes, and multiple tool calls.
If your application needs long-context reasoning, coding automation, agentic workflows, or a cost-conscious API deployment, GLM-5.2 is worth considering.
What Developers Can Build with GLM-5.2 on NeevCloud
GLM-5.2, developed by Zhipu AI, gives developers direct, pay-per-token access to an open-weight model without managing GPU infrastructure. With a 1-million-token context window, flexible thinking modes, and native tool calling, GLM-5.2 is well suited for coding agents, long-context AI applications, and software development workflows.
With the OpenAI-compatible GLM-5.2 API on NeevCloud, developers can integrate the model using cURL, Python, or JavaScript and move from testing to production with minimal changes.
1. What is GLM-5.2?
GLM-5.2 is a flagship open-weight AI model developed by Zhipu AI , built on a Mixture-of-Experts architecture with a 1-million-token context window, for long-horizon coding, reasoning, and agentic tasks.
2. Is GLM-5.2 available on NeevCloud?
Yes. GLM-5.2 is live in the NeevCloud Model API catalog and ready to use with no deployment or GPU setup required.
3. How much does GLM-5.2 cost on NeevCloud?
Pricing is pay-per-token: ₹89.06 per million input tokens and ₹287.29 per million output tokens.
4. Is the GLM-5.2 API on NeevCloud OpenAI-compatible?
Yes. You can call it from cURL, Python, or JavaScript using the standard chat completions request format.
5. How does GLM-5.2 compare to Claude Opus 4.8 and GPT-5.5?
GLM-5.2 ranks as the top open-weights model on independent evaluations like the Artificial Analysis Intelligence Index, coming within about a percentage point of Claude Opus 4.8 on long-horizon coding benchmarks, while costing roughly 5.4x less on input tokens and 8.3x less on output tokens on NeevCloud.