GLM-5.2
FlagshipGLM-5.2 is Z.ai's (Zhipu AI) flagship, shipped June 13, 2026. It is a 744 billion parameter mixture-of-experts model with a 1 million token context window, 131K max output, and a Max-effort reasoning mode, released under an MIT license. API pricing is roughly $1.40 per million input tokens and $4.40 per million output, which is on the order of 80 percent below Claude Opus 4.8. Z.ai reports 62.1 on SWE-Bench Pro, which would top GPT-5.5, and the model sits fourth overall and first among open-weight models on the Artificial Analysis Intelligence Index at 51; treat both as vendor-reported. The detail that makes it strategically interesting is the training run: roughly 100,000 Huawei Ascend 910B chips with no Nvidia silicon in the loop, at an estimated $25 million all-in. Reporting puts it at something like 40 percent of developer tokens on OpenRouter. Self-hosting at full precision needs about 1.5TB of GPU memory, roughly nineteen H100s, so the MIT license buys far more freedom than most teams can actually exercise.
Input Price
$1.40
per 1M tokens
Output Price
$4.40
per 1M tokens
Context Window
1M
tokens
Released
2026-06
Open source
Capabilities
Key Strengths
- ✓First among open-weight models on the Artificial Analysis index
- ✓MIT licensed with 1M context and 131K output
- ✓Roughly 80 percent cheaper than Opus 4.8
- ✓Trained end to end on Huawei Ascend silicon, no Nvidia
- ✓Vendor-reported 62.1 on SWE-Bench Pro
Best For
- ▸Cost-sensitive coding agents
- ▸Sovereign deployments avoiding US silicon
- ▸High-volume agentic pipelines
- ▸Self-hosting where MIT terms matter
Pricing Details
Input tokens
$1.40
per 1M tokens
Output tokens
$4.40
per 1M tokens
Estimated cost per 1K requests
$3.60
~1K input + ~500 output tokens avg
Prices are subject to change. Check the official documentation for current pricing. See the cost calculator for detailed estimates.
Open Source Model
GLM-5.2 is free to download and self-host under the MIT. Hosted API pricing varies by provider (e.g., Together, Fireworks, Groq). See our open source LLM guide for deployment options.