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Highest Output-Speed GPT-4o Inference Providers

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

OpenAI currently ranks first at 46.0 tok/s via openai.

Provider options:
2
Ranked:
2
Metric:
Output speed

Highest output speed endpoint ranking

Highest Output-Speed GPT-4o Inference Providers
RankProvider / routeOutput speedSpeed measurementsEndpoint details
#1Provider / route
OpenAIopenai
Output speed46.0 tok/sSpeed measurements
500-token response
11.41 s
Response starts
0.54 s
Output speed
46.0 tok/s
Endpoint details
Input price
$2.5 / 1M tokens
Output price
$10 / 1M tokens
Uptime (30 min)
99.93%
Context
128K
#2Provider / route
Azureazure
Output speed33.0 tok/sSpeed measurements
500-token response
16.08 s
Response starts
0.92 s
Output speed
33.0 tok/s
Endpoint details
Input price
$2.5 / 1M tokens
Output price
$10 / 1M tokens
Uptime (30 min)
99.92%
Context
128K

Endpoint data fetched .

GPT-4o endpoint guide

How to interpret the Highest Output-Speed GPT-4o Inference Providers

2 of 2 GPT-4o endpoints currently have the published data required for this ranking. OpenAI leads at 46.0 tok/s via openai. 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 OpenAI with the next option

Azure currently ranks second at 33.0 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

GPT-4o 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.