GLM 5.2.
Open-weights text model from Z.ai for long-horizon coding and agent work, with a one million token context and adjustable thinking effort, under the MIT licence.
8× Nvidia A100 SXM4 · $5.73/hr.
Most-aggressive quantisation we have a working recommendation for. Lower precision = less VRAM = cheaper hardware, at a small accuracy cost.
Cheapest hosted endpoints.
Variants in the GLM family.
GLM 5.3 Flash
Open-weights multimodal model from Z.ai with 320B total and 18B active parame...
glm-5.3
GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-trai...
GLM-4.5
Zhipu's frontier open-weight MoE — 355B total, 32B active. Strong agentic + r...
GLM-4.5-Air
Smaller, cheaper sibling of GLM-4.5. 106B total, 12B active.
GLM-5.1
Zhipu's GLM 5.1 series — successor to GLM-5 on z.ai's API.
GLM-5
Zhipu's GLM 5 generation — closed flagship between GLM-4.7 and GLM-5.1.
GLM-5 Turbo
Faster, cheaper sibling of GLM-5 on z.ai.
GLM-4.7
Open-weights coding model from Z.ai for English and Chinese, improving on GLM...
GLM-4.6
Open-weights English and Chinese text model from Z.ai with a 200K token conte...
GLM-4.5-Flash
Lowest-latency, lowest-cost variant of GLM-4.5 on z.ai.
GLM 5.3 FlashX
GLM-5.3-FlashX is the high-speed variant of Z.ai's GLM-5.3-Flash, a native mu...
GLM 5.3 FlashX Legal
GLM-5.3-FlashX-Legal (GLM-Legal) is Z.ai's legal-domain variant of GLM-5.3-Fl...
GLM 4 32B
GLM 4 32B is a cost-effective foundation language model. It can efficiently p...
GLM 4.7 Flash
As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances pe...
GLM 5V Turbo
GLM-5V-Turbo is Z.ai’s first native multimodal agent foundation model, built ...
Cheapest hardware per quantisation.
Each row is one quantisation tier (the same weights compressed differently). Lower precision → lower VRAM → cheaper hardware, at the cost of small accuracy loss. $/Std. ist der günstigste aktuelle Preis. Ein Setup mit mehreren Karten nutzt eine echte Instanz dieser Größe, wenn eine vermietet wird; sonst ist es der Einzelkartenpreis mal Anzahl.
| Quantisation | Cheapest GPU config | Total VRAM | Live $/hr | tokens/sec | |
|---|---|---|---|---|---|
|
FP16
FP16 — half precision (default)
|
2048 GB |
$24.57/hr
Cyfuture AI · On-Demand
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— | Compare → | |
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FP8
FP8 — 8-bit float (Hopper / Blackwell)
|
1024 GB |
$12.41/hr
Cyfuture AI · On-Demand
|
— | Compare → | |
|
INT4
INT4 — 4-bit integer (~4× VRAM saving)
|
640 GB |
$5.73/hr
geschätzt 8 × Einzelkartenpreis
Nosana · On-Demand
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— | Compare → |
What it costs per month across providers.
Estimate your monthly bill for GLM 5.2 across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
Günstigster Anbieter links.
Gesamte Monatskosten — Eingabe- und Ausgabe-Tokens zusammen.
Rechnung im Detail.
| Anbieter | Monatssumme | |
|---|---|---|
| $12.3 | Registrieren ↗ | |
| $13.66 | Registrieren ↗ | |
| $22.8 | Registrieren ↗ |
GPU mieten statt pro Token zahlen.
Bei einem Open-Weights-Modell wie GLM 5.2 kannst du eine GPU mieten und die Inferenz selbst betreiben. Die Rechnung: günstigste GPU-Miete × 730 Stunden/Monat. Strom ist im Stundenpreis der Miete enthalten.
Angenommen wird, dass die GPU rund um die Uhr mit ca. 85 % Auslastung läuft. Bei schwankendem Traffic zahlst du für die API weniger und für die GPU wahrscheinlich mehr (Leerlaufstunden kosten trotzdem Miete). Die Break-even-Analyse findest du im Break-even-Tool Selbsthosting vs. API.
About GLM 5.2.
A Z.ai model for long-horizon tasks, released with open weights under the MIT licence for English and Chinese text generation. It steps up from GLM-5.1 by sustaining work over a one million token context. It offers several thinking effort levels to trade quality against latency, and an indexer-sharing design that cuts per-token compute at that context length. Its multi-token prediction layer also helps speculative decoding.