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DeepSeek V4.1 Flash vs. V4 Pro: ydeevne, priser og migreringsvejledning
Sammenlign DeepSeek V4.1 Flash og V4 Pro på tværs af arkitektur, officielle benchmarks, API-prissætning, vision, samtidighed og migreringsvalg
DeepSeek API-priser: V4 Pro vs. V4.1 Flash
I don’t have live access to current pricing, and “DeepSeek V4 Pro” and “V4.1 Flash” rates vary by provider (DeepSeek native API, OpenRouter, Together, Fireworks, etc.), region, and currency. Share the pricing page or paste the exact numbers and I’ll compute a precise comparison. In the meantime, here’s the checklist and formulas I’ll use: What I need from you - Provider and endpoint (e.g., DeepSeek Console, OpenRouter, Together, Fireworks) - Currency and region - Model IDs as shown by that provider (exact strings) - Input and output prices ($/1K tokens) for both peak and off‑peak - Cache pricing, if applicable (cache write/store $/1K tokens; cache read $/1K tokens; any time/size fees) - Your workload profile: - Number of requests - Average input tokens per request - Average output tokens per request - Peak traffic share (%) vs off‑peak - Expected cache hit rate (%) and whether cache read/write is billed Comparison template (I’ll fill this with your numbers) - Model: DeepSeek V4 Pro - Model ID: - Context window: - Peak: input $/1K, output $/1K - Off‑peak: input $/1K, output $/1K - Cache: write $/1K, read $/1K (or policy) - Model: DeepSeek V4.1 Flash - Model ID: - Context window: - Peak: input $/1K, output $/1K - Off‑peak: input $/1K, output $/1K - Cache: write $/1K, read $/1K (or policy) Real workload cost formulas - Define: - N = total requests - Tin = avg input tokens/request - Tout = avg output tokens/request - p = peak share (0–1); (1−p) off‑peak share - h = cache hit rate for inputs (0–1), if applicable - Prices: - Pin_peak, Pout_peak ($/1K tokens) - Pin_off, Pout_off ($/1K tokens) - Pc_write, Pc_read ($/1K tokens), if billed - Without caching: - Cost_peak = N·p·[(Tin/1000)·Pin_peak + (Tout/1000)·Pout_peak] - Cost_off = N·(1−p)·[(Tin/1000)·Pin_off + (Tout/1000)·Pout_off] - Total = Cost_peak + Cost_off - With input caching (common cases): - If cache hits billed at read price: - Effective input cost per segment: - Peak: (h·Pc_read_peak + (1−h)·Pin_peak) per 1K input tokens - Off: (h·Pc_read_off + (1−h)·Pin_off) per 1K input tokens - Add cache write if charged on first use: - Peak write cost: (Tin/1000)·(1−h)·Pc_write_peak - Off write cost: (Tin/1000)·(1−h)·Pc_write_off - Then: - Cost_peak = N·p·[(Tin/1000)·(h·Pc_read_peak + (1−h)·Pin_peak) + (Tout/1000)·Pout_peak] + N·p·(Tin/1000)·(1−h)·Pc_write_peak - Cost_off = N·(1−p)·[(Tin/1000)·(h·Pc_read_off + (1−h)·Pin_off) + (Tout/1000)·Pout_off] + N·(1−p)·(Tin/1000)·(1−h)·Pc_write_off - Total = Cost_peak + Cost_off If you paste the exact rates and your workload stats, I’ll compute: - Peak vs off‑peak costs per model - Cache savings in absolute $ and % - Effective $ per 1K tokens for your workload - Total monthly estimate and crossover point where one model becomes cheaper than the other
6 metoder til at implementere DeepSeek Harness lokalt
Lær at installere og udrulle DeepSeek Harness lokalt i 2026. Denne vejledning dækker forudsætninger, officielle installationsmetoder, avancerede muligheder