glm-5.3.
GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks
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-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 5.2
Open-weights text model from Z.ai for long-horizon coding and agent work, wit...
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. $/hr is the cheapest rate on offer now. A multi-card setup uses a real instance of that size when one is rented; otherwise it is the single-card rate times the count.
| Quantisation | Cheapest GPU config | Total VRAM | Live $/hr | tokens/sec | |
|---|---|---|---|---|---|
|
FP16
FP16 — half precision (default)
|
2048 GB |
$24.57/hr
Cyfuture AI · on-demand
|
— | Compare → | |
|
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
est. 8 × single-card rate
Nosana · on-demand
|
— | Compare → |
What it costs per month across providers.
Estimate your monthly bill for glm-5.3 across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
Cheapest provider on the left.
Total monthly cost — input + output tokens combined.
Bill breakdown.
Rent the GPU instead of paying per token.
For an open-weights model like glm-5.3, you can rent a GPU and serve inference yourself. The math: cheapest GPU rental × 730 hours/month. Power is included in a rental's hourly price.
Assumes the GPU runs 24/7 at ~85% utilisation. If your traffic is bursty, you'll pay less for the API and probably more for the GPU (idle hours still cost rental). The breakeven analysis lives on the Self-host vs API breakeven tool.