GLM 5 is Zhipu AI's flagship open-weight model for complex systems design and long-horizon agent workflows, released 2026-02. It targets expert developers with production-grade performance on large-scale programming tasks, rivaling leading closed-source models. The model emphasizes agentic planning, deep backend reasoning, and iterative self-correction to move beyond code generation toward full-system construction and autonomous execution.
Channels
Every channel serves the same model, but they differ in price and stability. On each API key in the Platform, you pick a primary and a backup channel per vendor, and each billing line shows which channel served the request.
Context pricing
This model’s price depends on how much input context a request carries. The headline price is the cheapest band, and a longer prompt moves the whole request into a higher one.
Capabilities
Parameters
Request parameters this model accepts through the API.
Cost calculator
Pick a channel and a usage scenario, then drag the sliders to match your workload. Each scenario assumes a different prompt-cache hit rate.
Benchmarks
bar = score (0–100) · ▏ medianScores come from vendor reports and public leaderboards, not private evals.
Showcase
Every chat model gets the same three fixed prompts. Outputs are archived verbatim, so you can copy a prompt and reproduce the result.
Build a single-file HTML page using three.js from a CDN: a low-poly planet with a tilted ring of ~200 orbiting particles, a soft key light plus rim light, slow auto-rotation, and drag-to-orbit controls. No build tools — one file only.
Every model will run this exact prompt, and its output will appear here verbatim with token counts and the cost per run.
Find the sum of all positive integers n such that n² + 85n + 2017 is a perfect square. Show your reasoning, then give only the final answer on the last line.
Every model will run this exact prompt, and its output will appear here verbatim with token counts and the cost per run.
Extract strict JSON {vendor, invoice_no, issue_date, currency, total, line_items:[{desc, qty, unit_price}]} from the invoice text. Output JSON only.
"Northwind Cloud Ltd. — Invoice NW-20847, issued 12 Mar 2026. 3× GPU-hour bundle at $18.40, 1× object storage 2TB at $9.90. Total $65.10 (USD)."Every model will run this exact prompt, and its output will appear here verbatim with token counts and the cost per run.
Quick start
import os from openai import OpenAI client = OpenAI( base_url="https://api.mirapi.ai/v1", # changed api_key=os.environ["MIRAPI_API_KEY"], # changed ) resp = client.chat.completions.create( model="glm-5", messages=[{"role": "user", "content": "Hello"}], )
import OpenAI from "openai"; const client = new OpenAI({ baseURL: "https://api.mirapi.ai/v1", // changed apiKey: process.env.MIRAPI_API_KEY, // changed }); const res = await client.chat.completions.create({ model: "glm-5", messages: [{ role: "user", content: "Hello" }], });
curl https://api.mirapi.ai/v1/chat/completions \ -H "Authorization: Bearer $MIRAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "glm-5", "messages": [{"role": "user", "content": "Hello"}] }'
# ~/.zshrc export ANTHROPIC_BASE_URL="https://api.mirapi.ai" export ANTHROPIC_AUTH_TOKEN="$MIRAPI_API_KEY" # start as usual (model names stay the same) claude --model glm-5
# ~/.codex/config.toml model_provider = "mirapi" model = "glm-5" [model_providers.mirapi] name = "MirAPI" base_url = "https://api.mirapi.ai/v1" env_key = "MIRAPI_API_KEY" wire_api = "responses"
Links
1M free tokens to start, no card required. It’s OpenAI-compatible, so you only change the base URL.