GLM 5.2 — Spezifikationen
| Entwickler | Zhipu AI |
|---|---|
| Typ | LLM (Programmierung/Agentik, MoE) |
| Modality | Text → Text |
| Parameter | 744 Mrd. insgesamt / ~40 Mrd. aktiv (MoE) |
| Kontextfenster | 1 Mio. |
| Maximale Ausgabe | 131 K |
| Lizenz | MIT (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2026-06 |
| Eingabepreis | 1,40 $ / 1 Mio. |
| Ausgabepreis | 4,40 $ / 1 Mio. |
| API-Anbieter | Zhipu (Z.ai), OpenRouter |
Lokal ausführen
| VRAM (4-Bit) | ~370 GB |
|---|---|
| Mindest-GPU | Multi-GPU-Server (z. B. 5× H100 mit 80 GB VRAM) |
What is GLM 5.2?
GLM 5.2 is Zhipu AI’s open 1M-context model — a 744-billion-parameter mixture-of-experts
activating roughly 40B parameters per token, built on a new “IndexShare” sparse-attention
design, and notably trained entirely on Huawei chips. The weights are MIT-licensed and it is
strong on coding, design and agentic tasks. Pricing is $1.40 in / $4.40 out per million
tokens.
The Huawei training detail is the strategically interesting one: it is a demonstration that
a frontier-scale model can be trained end to end outside the NVIDIA ecosystem, which has
implications for anyone modelling long-term compute supply. For a buyer, though, the relevant
numbers are simpler. GLM 5.2 blends to roughly $2 per million tokens while scoring near the
top of the open-weight field, which makes it one of the best capability-per-dollar picks
available for agentic and coding workloads and a serious alternative to frontier models
costing five times more. Self-hosting needs around 370 GB at 4-bit — a five-H100 server — so
in practice most teams will use the API and treat the MIT licence as portability insurance.
GLM 5.2 pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $1.40 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $4.40 |
| Output/input ratio | 3,1× |
| Blended (4:1 in:out) | $2.00 per 1M tokens |
What GLM 5.2 costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Workload | Tokens/Monat | Cost / month |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $2.50 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $50 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $500 |
Run your own numbers in the KI-API-Kostenrechner.
Cheaper alternatives to GLM 5.2
| Modell | Gewichteter Preis pro Million US-Dollar | You save |
|---|---|---|
| DeepSeek V4-Pro offenere | $0.522 | 74% cheaper |
| Kimi K2.7 Code offenere | $0.980 | 51% cheaper |
| DeepSeek V4-Flash offenere | $0.168 | 92% cheaper |
Self-host or pay the API?
GLM 5.2 is open-weight, so you can run it yourself. It needs ~370 GB of VRAM at 4-bit (Multi-GPU server (e.g. 5× H100 80GB)). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the Selbsthosting-vs.-API-Rechner works out the break-even point for your token volume.
Häufig gestellte Fragen
How much does GLM 5.2 cost per 1M tokens?
GLM 5.2 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $2.00 per 1M tokens.
How much does GLM 5.2 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $50 on GLM 5.2. A side project (1M in / 0.25M out) costs roughly $2.50.
What is a cheaper alternative to GLM 5.2?
DeepSeek V4-Pro is the strongest cheaper option in our database at $0.522 per 1M blended — about 74% less than GLM 5.2. It is also open-weight, so self-hosting is an option.
Can I run GLM 5.2 locally?
Yes. GLM 5.2 is open-weight and needs about ~370 GB of VRAM at 4-bit quantisation (Multi-GPU server (e.g. 5× H100 80GB)).
Why does GLM 5.2 charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. GLM 5.2 charges 3.1× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Prices are the published list rates for the model's primary API and are reviewed as providers change them. Volume, batch and cached-input discounts are not included. Compare every model side by side in the Datenbank für KI-Modelle oder das LLM-Leaderboard.
