Start typing to search.
Ranks providers by estimated cost using a 1,000-input/500-output token ratio, shown per 1 million total tokens.
DekaLLM currently ranks first at $0.42 / 1M tokens via dekallm.
| Rank | Provider / route | Estimated cost / 1M tokens | Speed measurements | Endpoint details |
|---|---|---|---|---|
| #1 | Provider / route DekaLLM dekallm | Estimated cost / 1M tokens$0.42 / 1M tokens | Speed measurements
| Endpoint details
|
| #2 | Provider / route AkashML akashml/fp8 | Estimated cost / 1M tokens$0.426667 / 1M tokens | Speed measurements
| Endpoint details
|
| #3 | Provider / route Parasail parasail/fp8 | Estimated cost / 1M tokens$0.433333 / 1M tokens | Speed measurements
| Endpoint details
|
| #4 | Provider / route AtlasCloud atlas-cloud/fp8 | Estimated cost / 1M tokens$0.49525 / 1M tokens | Speed measurements
| Endpoint details
|
| #5 | Provider / route Weights & Biases wandb/fp8 | Estimated cost / 1M tokens$0.583333 / 1M tokens | Speed measurements
| Endpoint details
|
| #6 | Provider / route SiliconFlow siliconflow/fp8 | Estimated cost / 1M tokens$0.666667 / 1M tokens | Speed measurements
| Endpoint details
|
Endpoint data fetched .
Qwen3.6 35B A3B endpoint guide
6 of 6 Qwen3.6 35B A3B endpoints currently have the published data required for this ranking. DekaLLM leads at $0.42 / 1M tokens via dekallm. The table keeps unranked routes visible so missing measurements do not look like missing provider availability.
The cheapest ranking uses a 1,000-input/500-output-token ratio and reports the blended result per 1 million total tokens. Because providers can price input and output differently, a workload with a different token ratio may produce a different order.
AkashML currently ranks second at $0.426667 / 1M tokens. 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.
Qwen3.6 35B A3B has 6 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.
