Use the GLM API via RunAPI with a model skill, unified auth, and pay-as-you-go pricing.
# Base URL
https://runapi.ai
# Endpoints
POST /v1/chat/completions
POST /v1/responses
POST /v1/messages
POST /v1beta/models/{model}:generateContent
POST /v1beta/models/{model}:streamGenerateContent
curl https://runapi.ai/v1/chat/completions \
-H "Authorization: Bearer $RUNAPI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.3",
"messages": [
{
"role": "user",
"content": "Read this multi-file repository, find the failing integration test, and propose a patch with an explanation of the root cause."
}
]
}'from openai import OpenAI
client = OpenAI(
base_url="https://runapi.ai/v1",
api_key="your-runapi-key"
)
response = client.chat.completions.create(
model="glm-5.3",
messages=[{"role": "user", "content": "Read this multi-file repository, find the failing integration test, and propose a patch with an explanation of the root cause."}]
)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://runapi.ai/v1",
apiKey: "your-runapi-key"
});
const response = await client.chat.completions.create({
model: "glm-5.3",
messages: [{ role: "user", content: "Read this multi-file repository, find the failing integration test, and propose a patch with an explanation of the root cause." }]
});GLM is Z.ai's family of MIT-licensed Mixture-of-Experts language models. GLM-4.5 (355B total / 32B active, 128K context) introduced the open-weight MoE line with a flagship and a lighter Air tier. GLM-4.6 and 4.7 extend to 200K context with stronger code generation — 4.7 reaches 73.8% on SWE-bench. The GLM-5 series (744B / 40B active, 200K context) pushes further to 77.8% SWE-bench Verified, and GLM-5.1 holds the top open-weight score on SWE-bench Pro at 58.4%. All are available through RunAPI with one key and per-token billing.
| Endpoint | Protocol |
|---|---|
POST /v1/chat/completions | OpenAI compatible |
POST /v1/responses | OpenAI Responses |
POST /v1/messages | Anthropic compatible |
POST /v1beta/models/{model}:generateContent | Gemini generateContent |
POST /v1beta/models/{model}:streamGenerateContent | Gemini streamGenerateContent |
| Variant | Billing | Pricing | |
|---|---|---|---|
| glm-4.5 | 1K tokens | Input $0.41 / 1M tokens | Output $1.61 / 1M tokens | View → |
| glm-4.5-air | 1K tokens | Input $0.10 / 1M tokens | Output $0.55 / 1M tokens | View → |
| glm-4.6 | 1K tokens | Input $0.60 / 1M tokens | Output $1.22 / 1M tokens | View → |
| glm-4.7 | 1K tokens | Input $0.60 / 1M tokens | Output $1.19 / 1M tokens | View → |
| glm-5 | 1K tokens | Input $1.00 / 1M tokens | Output $1.60 / 1M tokens | View → |
| glm-5-turbo | 1K tokens | Input $1.20 / 1M tokens | Output $2.00 / 1M tokens | View → |
| glm-5.1 | 1K tokens | Input $1.40 / 1M tokens | Output $2.20 / 1M tokens | View → |
| glm-5.2 | 1K tokens | Input $0.70 / 1M tokens | Output $2.20 / 1M tokens | View → |
| glm-5.3 | 1K tokens | Input $1.40 / 1M tokens | Output $4.40 / 1M tokens | View → |
| Endpoint | Resolution | Duration | Price | |
|---|---|---|---|---|
| chat_completion | — | — | Input $0.41 / 1M tokens | Output $1.61 / 1M tokens |
Prices shown for glm-4.5. Other variants retain their own endpoint pricing.
Sign up and generate an API key from the dashboard.
Choose the version that best matches your quality and cost requirements.
Submit your prompt and parameters to the model's documented endpoint.
Poll the task or set a webhook, then download the completed output.
GLM models from Z.ai are MIT-licensed MoE LLMs spanning 128K–200K context. GLM-5.1 leads open-weight models on SWE-bench Pro. Through RunAPI they share a single API key with pay-as-you-go token billing, callable from the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages surfaces.
A single RunAPI API key unlocks the whole model catalog across all providers. No separate accounts to create, no API keys to rotate per integration, and no credential management overhead. Add a new model to your app by changing one parameter.
Per-call pricing in USD, billed monthly into a single invoice. No subscription tiers, no minimum spend, and failed generations are never charged. The pricing page and check_pricing API show exact costs before you commit to a model.
Typed schemas, parameter constraints, and setup notes are packaged in the model skill so your implementation starts from the right contract. The skill loads into Claude Code, Codex, Gemini CLI, Cursor, and VS Code — your agent knows the correct request shape before you write a line of code.
Coding and agentic tool use. GLM-5.1 leads open-weight models on SWE-bench Pro (58.4%) and scores 95.3% on AIME 2026. The full line is strong at multi-step tool calling, code generation across large codebases, and mathematical reasoning.
glm-4.5 is the 355B flagship (32B active parameters) with 128K context; glm-4.5-air is a lighter 106B MoE (12B active) for faster, lower-cost everyday work.
GLM-5 scales to 744B total / 40B active with 256 experts, versus GLM-4.7's 358B / 32B. SWE-bench Verified jumps from 73.8% to 77.8%. Both share 200K context.
Point the OpenAI SDK (Chat Completions or Responses) or the Anthropic Messages SDK at RunAPI and pass the GLM model id; the proxy adapts the protocol for you.
Per token, pay-as-you-go, at RunAPI's published input and output rates for each GLM model — no subscription.
Pick the cheapest variant that meets your quality bar. Most teams start on the fast variant and graduate to pro for production.