Before running large batch jobs on a Nebius reasoning model, estimate the hidden reasoning token overhead: budget several times your expected output tokens. Prefer a non-reasoning model for straightforward tasks and reserve reasoning models for genuinely hard ones. Set max_tokens as a safety cap on every batch request so one pathological input cannot burn the budget.

Context: Nebius batch inference notes document the hidden cost of reasoning models. A reasoning model generates a silent reasoning trace before the answer, billed as completion tokens at roughly 4x overhead. The notes suggest switching to a non-reasoning model to eliminate the reasoning tokens entirely, trying a reasoning_effort parameter if supported, or capping max_tokens to bound runaway reasoning, since p99 reasoning runs can be far larger than p50.