by Zhipu AI

GLM-4.6.

text open weights datacenter 355B params 203K ctx
Cheapest input
$0.43/M
on Zhipu BigModel
Cheapest output
$1.74/M
on Zhipu BigModel
Fastest
45 tok/s
on OpenRouter
Smallest GPU
1× AMD MI325
Run it yourself

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 refreshed hourly from each provider's API.

Quantisation Cheapest GPU config Total VRAM Live $/hr tokens/sec
FP16
FP16 — half precision (default)
1024 GB Compare →
FP8
FP8 — 8-bit float (Hopper / Blackwell)
512 GB Compare →
INT4
INT4 — 4-bit integer (~4× VRAM saving)
256 GB Compare →
Just need an API?
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