Zhipu AI: GLM 5.2

GLM 5.2 is Zhipu AI's large-scale reasoning model, released 2026-06, built for long-horizon agent workflows and project-level software engineering. It maintains engineering context and follows standards across full development cycles, from requirements to multi-platform deployment, and is particularly strong at coding and tool use in long-running tasks.

Modalities
inTextoutText
Price / 1M−66%
$0.30 / $1.05$0.97 / $3.04
Context
1.049M
Knowledge cutoff

Capabilities

ReasoningTool useLong contextPrompt cachingStructured output

Channels

Same weights on every channel. Requests route to the cheapest healthy one; the channel used is printed on each billing line.

Provider
Discount
Input /M
Output /M
Cache Read /M
Cache Create 5m /M
Cache Create 1h /M
Defaultstable
−66%
$0.30
$1.05
$0.05

Cost Calculator

Pick a channel and a usage scenario, then drag to your workload — each scenario assumes a different prompt-cache hit rate.

Channel
Scenario
Unit $0.30 / $1.05 /1MDaily chat: 40% cache hit assumed · cache read $0.05 /1M
List price $207.55On MirAPI$69.60/ moYou save $137.95Create an API key

Benchmarks

bar = score (0–100) · ▏ median

Scores from vendor reports and public leaderboards. No private evals.

Intelligence Index
General ability
52.6
#18
Coding Index
Code
68.8
#21
Agentic Index
Agentic
45.7
#19
GPQA Diamond
Science QA
86.0
#25
τ-bench (airline)
Agentic tool use
75.5
#23

Showcase

Three fixed prompts, identical for every chat model. Outputs are archived verbatim — copy the prompt to reproduce.

Task prompt · identical for all models
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.
Archived output
Archive not published yet

Every model will run this exact prompt; the output lands here verbatim, with tokens and per-run cost.

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="z-ai/glm-5.2",
    messages=[{"role": "user", "content": "Hello"}],
)
Wire GLM 5.2 into your product

1M free tokens to start · no card · OpenAI-compatible, one base_url change

Start free