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DeepSeek API 요금제: V4 Pro vs. V4.1 Flash
AI Comparisons

DeepSeek API 요금제: V4 Pro vs. V4.1 Flash

I don’t have real-time access to DeepSeek’s latest pricing or peak/off-peak schedules beyond Oct 2024. “Current” rates can also differ by provider (DeepSeek’s own API vs aggregators like OpenRouter, Together, etc.). If you share the pricing page or specify the provider, I can fill in exact numbers and compute workload costs. In the meantime, here’s a precise checklist and calculator you can plug rates into. What to collect for each model - Provider and endpoint: e.g., DeepSeek official vs OpenRouter (peak/off-peak often differs by provider). - Model IDs: the exact strings you call in the API (e.g., v4-pro vs v4.1-flash; provider-specific IDs may differ). - Context window and output cap: informs max tokens and cache effectiveness. - Prices per 1K tokens: - Off-peak input price (P_in_off) - Off-peak output price (P_out_off) - Peak input price (P_in_peak) or a peak multiplier (M_peak) - Peak output price (P_out_peak) or use same M_peak - Prompt/cache pricing: - Cached-input price per 1K tokens (P_cache_in), if supported - Cache retention and eviction rules (affects reuse rate) - Operational details: - Peak window definition and timezone - Minimum billing increments and rounding - Rate limits, retry policies, and any free tier credits Comparison template (fill with your numbers) - Model IDs: - V4 Pro: - V4.1 Flash: - Off-peak prices per 1K tokens: - V4 Pro: input P_in_off = , output P_out_off = - V4.1 Flash: input P_in_off = , output P_out_off = - Peak prices per 1K tokens: - Either list P_in_peak/P_out_peak directly, or give M_peak (e.g., 1.25×) - Cache pricing (if available): - P_cache_in (per 1K tokens), note whether output is cache-billed (usually not) - Any first-hit vs subsequent-hit differences - Latency/throughput notes: - V4 Pro: typically higher quality, higher latency/cost - V4.1 Flash: typically lower latency, lower cost, better for high-throughput Real workload cost calculator Define: - N = total requests - f_peak = fraction of requests during peak (0–1) - T_in = average input tokens per request - T_out = average output tokens per request - r_cache = fraction of input tokens served from cache (0–1; subsequent calls that reuse a cached system/prompt segment) - P_in_off, P_out_off = off-peak prices per 1K tokens - P_cache_in = cache price per 1K tokens (if supported) - M_peak = peak multiplier (if peak prices are given directly, use those instead) Per-request off-peak cost: - C_off = (T_in_noncache × P_in_off) + (T_in_cache × P_cache_in) + (T_out × P_out_off) - where T_in_noncache = T_in × (1 − r_cache), T_in_cache = T_in × r_cache Per-request peak cost: - If using multiplier: C_peak = M_peak × C_off - If using separate peak prices: replace P_* with peak equivalents and recompute Total cost: - C_total = N × [ (1 − f_peak) × C_off + f_peak × C_peak ] Operational adjustments to reflect “real” costs - Retries/timeouts: multiply N by (1 + retry_rate) - Token rounding: some providers bill to nearest 1K tokens; apply ceil(T_in/1000) and ceil(T_out/1000) - Streaming partials: still billed as output tokens; ensure T_out includes them - Cache warm-up: first call pays full P_in_off; subsequent calls pay P_cache_in for reused segments - Mixed traffic: compute separate T_in/T_out and r_cache by route (chat vs tool calls), then sum costs How I can finalize this for you - Tell me your provider (e.g., DeepSeek official API vs OpenRouter) and paste the current price table or a link. - Provide your workload profile: N, T_in, T_out, r_cache (if using caching), f_peak, and retry rate. - I’ll return a filled comparison with exact peak/off-peak and cache savings, plus your total monthly cost and per-request cost for V4 Pro vs V4.1 Flash.

D
Deon Goodwin