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Highest Output-Speed GLM 4.5V Inference Providers

Ranks providers by their recent typical output speed in tokens per second. Providers without enough recent data remain visible after ranked rows.

Z.ai currently ranks first at 23.0 tok/s via z-ai/fp8.

Provider options:
2
Ranked:
2
Metric:
Output speed

Highest output speed endpoint ranking

Highest Output-Speed GLM 4.5V Inference Providers
RankProvider / routeOutput speedSpeed measurementsEndpoint details
#1Provider / route
Z.aiz-ai/fp8
Output speed23.0 tok/sSpeed measurements
500-token response
23.91 s
Response starts
2.17 s
Output speed
23.0 tok/s
Endpoint details
Input price
$0.6 / 1M tokens
Output price
$1.8 / 1M tokens
Uptime (30 min)
Insufficient recent data
Context
65.5K
#2Provider / route
NovitaAInovita/fp8
Output speed20.0 tok/sSpeed measurements
500-token response
25.86 s
Response starts
0.86 s
Output speed
20.0 tok/s
Endpoint details
Input price
$0.6 / 1M tokens
Output price
$1.8 / 1M tokens
Uptime (30 min)
Insufficient recent data
Context
65.5K

Endpoint data fetched .

GLM 4.5V endpoint guide

How to interpret the Highest Output-Speed GLM 4.5V Inference Providers

2 of 2 GLM 4.5V endpoints currently have the published data required for this ranking. Z.ai leads at 23.0 tok/s via z-ai/fp8. The table keeps unranked routes visible so missing measurements do not look like missing provider availability.

How generation throughput is ranked

The output-speed ranking compares recent median generated tokens per second. It favors routes that complete long generations quickly, while first-token latency remains a separate measure of how responsive the endpoint feels at the beginning.

Compare Z.ai with the next option

NovitaAI currently ranks second at 20.0 tok/s. Compare that gap with input and output price, response-start time, output speed, 30-minute uptime, context length, and the exact route before deciding whether first place is meaningful for your workload.

Use the route, not only the provider name

GLM 4.5V has 2 published provider options, and performance data is matched to each exact OpenRouter routing tag. Different quantization, context, regional deployment, or provider configuration can change price and behavior even when the underlying model name is identical.