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Fastest GLM 4.5V Inference Providers

Ranks providers by estimated time to return 500 tokens, combining the wait for the first token with output speed.

Z.ai currently ranks first at 23.91 s via z-ai/fp8.

Provider options:
2
Ranked:
2
Metric:
Estimated response time

Fastest response endpoint ranking

Fastest GLM 4.5V Inference Providers
RankProvider / routeEstimated response timeSpeed measurementsEndpoint details
#1Provider / route
Z.aiz-ai/fp8
Estimated response time23.91 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
Estimated response time25.86 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 Fastest 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 23.91 s via z-ai/fp8. The table keeps unranked routes visible so missing measurements do not look like missing provider availability.

How complete response time is ranked

The fastest ranking estimates a 500-token answer by adding the recent median first-token wait to 500 divided by median output throughput. It represents an example complete response, not a guarantee for every prompt or request size.

Compare Z.ai with the next option

NovitaAI currently ranks second at 25.86 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.