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Ranks providers by their recent typical output speed in tokens per second. Providers without enough recent data remain visible after ranked rows.
Phala currently ranks first at 89.5 tok/s via phala.
| Rank | Provider / route | Output speed | Speed measurements | Endpoint details |
|---|---|---|---|---|
| #1 | Provider / route Phala phala | Output speed89.5 tok/s | Speed measurements
| Endpoint details
|
| #2 | Provider / route Alibaba Cloud Int. alibaba | Output speed79.0 tok/s | Speed measurements
| Endpoint details
|
| #3 | Provider / route DekaLLM dekallm | Output speed77.0 tok/s | Speed measurements
| Endpoint details
|
| #4 | Provider / route Nebius nebius/fp8 | Output speed71.0 tok/s | Speed measurements
| Endpoint details
|
| #5 | Provider / route Weights & Biases wandb/bf16 | Output speed62.0 tok/s | Speed measurements
| Endpoint details
|
| #6 | Provider / route StreamLake streamlake | Output speed46.0 tok/s | Speed measurements
| Endpoint details
|
| #7 | Provider / route SiliconFlow siliconflow/fp8 | Output speed21.0 tok/s | Speed measurements
| Endpoint details
|
Endpoint data fetched .
Qwen3 30B A3B Instruct 2507 endpoint guide
7 of 7 Qwen3 30B A3B Instruct 2507 endpoints currently have the published data required for this ranking. Phala leads at 89.5 tok/s via phala. The table keeps unranked routes visible so missing measurements do not look like missing provider availability.
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.
Alibaba Cloud Int. currently ranks second at 79.0 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.
Qwen3 30B A3B Instruct 2507 has 7 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.
