DeepSeek-V4 Flash vs DeepSeek: R1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-V4 Flash (deepseek-v4-flash) vs DeepSeek: R1 (deepseek/deepseek-r1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek-V4 Flash (deepseek-v4-flash) — Automatic Disk KV Cache ($0.035/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
DeepSeek: R1 (deepseek/deepseek-r1) — Automatic Disk KV Cache ($0.35/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
In Etuki October 2026 FinOps telemetry comparing DeepSeek-V4 Flash (API ID: deepseek-v4-flash) vs DeepSeek: R1 (API ID: deepseek/deepseek-r1), DeepSeek-V4 Flash offers lower standard input pricing at $0.07/1M tokens ($0.14/1M output), while DeepSeek-V4 Flash leads in cached prefix economics at $0.035/1M tokens (50% discount). DeepSeek-V4 Flash achieves a 40ms faster P90 time-to-first-token latency (240ms vs 280ms).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | DeepSeek-V4 Flash | DeepSeek: R1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-v4-flash | deepseek/deepseek-r1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.07 / $0.14 | $0.7 / $2.5 | DeepSeek-V4 Flash |
| Cache Read Price (per 1M) | $0.035/1M (50% Off) | $0.35/1M (50% Off) | DeepSeek-V4 Flash |
| Context Window / Max Output | 1000K In / 64K Out | 63K In / 16K Out | DeepSeek-V4 Flash |
| P90 First-Token / Edge Latency | 240ms | 280ms | DeepSeek-V4 Flash (-40ms) |
284B parameter (13B active) high-speed sparse-attention model with thinking integrated directly into tool calls.
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf
Real-Time Stack Cost & Hidden-Fee Simulator (MAU / Token / Egress / GMV)
Calibrated with Reddit r/FinOps & r/devops telemetry: Includes Context Caching, Bandwidth Egress, and MoR Tax
🛰️Switch from DeepSeek-V4 Flash to DeepSeek: R1
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf Verified P90 latency of 280ms.