deepseek/deepseek-r1-distill-llama-70bDeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across...
Billing follows the provider's own reported cost rather than the catalogue price — cached input is cheaper, and reasoning tokens bill as output. Your usage page is the record of what you actually paid.
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curl https://macdecloud.com/v1/chat/completions \
-H "Authorization: Bearer dcld-sk-..." \
-H "Content-Type: application/json" \
-d '{"model":"deepseek/deepseek-r1-distill-llama-70b","messages":[{"role":"user","content":"Hello"}]}'Add "stream": true for token-by-token output over standard SSE.
curl -N https://macdecloud.com/v1/chat/completions \
-H "Authorization: Bearer dcld-sk-..." \
-H "Content-Type: application/json" \
-d '{"model":"deepseek/deepseek-r1-distill-llama-70b","stream":true,"messages":[{"role":"user","content":"hi"}]}'This is a reasoning model: it produces a thinking pass before answering, and those tokens are billed as output — often far more of them than the answer itself. Two consequences. Too small a max_tokens gets consumed during reasoning and returns finish_reason: "length" with a null content, which looks like a failure but is just an exhausted budget (2000+ is a safer floor). And cost runs well above a non-reasoning model at the same headline price.