Open Models Built for Engineering Work, Not Conversation
GLM is Z.ai's family of open-weight models, and it is aimed squarely at engineering: repository-scale coding, terminal automation, and long-horizon agent execution rather than chat. The family makes an argument worth noticing — that beyond a certain scale, capability comes from post-training on verifiable, executable environments rather than from a larger foundation, and its recent releases demonstrate that on identical base weights. Models here hold a million-token context through hybrid attention designs built specifically to make that window affordable, with reasoning depth selectable per request. On DEVUP AI the family is reachable through the standard OpenAI-compatible endpoint, billed in Algerian Dinar.
GLM is built for work that executes rather than work that reads well. The training targets are repository-scale software changes, terminal task completion, and tool orchestration across long runs — environments where success is verified by whether the tests pass, not by whether the text looks right. That focus shows up in what the models are good at and, just as clearly, in what they are not built for.
Z.ai's recent releases make an unusual claim about where capability comes from. One of them shares its base model entirely with the previous generation — no new pre-training, no larger foundation — and improves across every published benchmark, in some cases by a factor of six. The argument is that total parameters matter up to a threshold sufficient to hold the world, after which capability comes from post-training on verifiable, executable environments. Whether or not that generalises, it is a measurable claim rather than a marketing one, because the base weights are the control.
Long context is treated as an engineering problem rather than a specification. Models in this family combine sparse attention with linear attention, or share a single index across multiple attention layers, specifically to cut the compute and memory cost that make million-token inference impractical. The window is designed to be usable at its full extent, not quoted at it.
The naming deserves attention, because it does not mean what it appears to. A Flash model in this family is not a reduced version of the flagship. The two can be built on entirely different base models, with different attention architectures, different input modalities, and different licences — and the Flash model may carry the newer architecture and the wider input surface while the flagship carries the larger parameter count and the deeper post-training. Choose on modality and workload, and read the individual model page rather than assuming that one is a smaller copy of the other.
Licences also vary within the family. Some models are released permissively; others carry model-specific terms. Review the licence on the model page before building something commercial. On DEVUP AI the family is reachable through the same OpenAI-compatible endpoint as the rest of the catalogue, with billing in Algerian Dinar and local payment methods.
Available GLM Models
Deploy and access all published models in this family with unified DZD pricing and zero foreign card requirement.
| Model | Context | DZD in / 1M tokens | DZD out / 1M tokens | Actions |
|---|---|---|---|---|
| GLM-5.3zai-org/GLM-5.3 | 1M Tokens | 490 | 1540 | View model |
| GLM-5.3-Flashzai-org/GLM-5.3-Flash | 1M Tokens | 60 | 200 | View model |
| GLM-5.2zai-org/GLM-5.2 | 1M Tokens | 490 | 1540 | View model |
| GLM-4.6zai-org/GLM-4.6 | 200K Tokens | 150.5 | 609 | View model |
| GLM-5zai-org/GLM-5 | 200K Tokens | 210 | 728 | View model |
| GLM-4.7-Flashzai-org/GLM-4.7-Flash | 200K Tokens | 21 | 140 | View model |
| GLM-4.7zai-org/GLM-4.7 | 200K Tokens | 140 | 612.5 | View model |
| GLM-5.1zai-org/GLM-5.1 | 200K Tokens | 367.5 | 1225 | View model |
Call any GLM model
Every model on this page uses the same API key and standard OpenAI-compatible endpoint. The example below uses the featured model zai-org/GLM-5.3.
from openai import OpenAI
import os
client = OpenAI(
api_key=os.environ["DEVUP_API_KEY"],
base_url="https://api.devupai.com/v1"
)
response = client.chat.completions.create(
model="zai-org/GLM-5.3-Flash",
messages=[
{"role": "user", "content": "Hello world!"}
],
max_tokens=1024
)
print(response.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.DEVUP_API_KEY,
baseURL: "https://api.devupai.com/v1",
});
async function main() {
const response = await client.chat.completions.create({
model: "zai-org/GLM-5.3-Flash",
messages: [
{ role: "user", content: "Hello world!" },
],
max_tokens: 1024,
});
console.log(response.choices[0]?.message?.content);
}
main();curl -X POST "https://api.devupai.com/v1/chat/completions" \
-H "Authorization: Bearer $DEVUP_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org/GLM-5.3-Flash",
"messages": [
{ "role": "user", "content": "Hello world!" }
],
"max_tokens": 1024
}'Frequently Asked Questions
No, and this is the most common misreading of the family. Flash and flagship models here can be built on different base models with different attention architectures. In the current generation the Flash model is natively multimodal while the flagship is text-only, and the two carry different licences. Choose on what your workload actually needs — modality first, then capability — rather than on the assumption that one is a reduced copy of the other.
Start with a Flash model. They carry the newer architecture, accept a wider range of input, and handle most production work well. Move to the flagship when the task is genuinely hard — long-horizon repository work, difficult coding-agent problems, security analysis — and when your input is text only. Check the individual model page for input types and licence before committing.
No. Some models in this family are released under a permissive licence and others carry model-specific terms. This varies within a single generation, so a permissive licence on one model tells you nothing about the model next to it. Read the licence on the individual model page before any commercial deployment.
Through a reasoning effort parameter with three levels. Set it explicitly on every request — the default is the deepest and most expensive setting. One behaviour worth guarding against: on some models in this family an unrecognised value is not rejected, it silently falls back to the maximum. Validate the value against the levels that model actually accepts before sending it, especially if the same code path serves models from several families.
Some can. The current generation includes a natively multimodal model that accepts images alongside text, while the flagship in the same generation is text-only. Support is per model, not family-wide — check the individual model page rather than assuming.
Repository-scale engineering, terminal and infrastructure automation, and long-horizon agentic execution. Recent releases also show notable strength in code security analysis and vulnerability discovery. If your workload is a long task that must finish rather than a question that must be answered, this family is worth testing against your own case.
Yes. Parameter counts, attention designs, expert configurations, and training approach are documented, and weights are published for models in this family. Where a specific figure is not disclosed for a given release, the model page says so rather than estimating it.
