DeepSeek-V4 Flash vs Magnum v4 72B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-V4 Flash (deepseek-v4-flash) vs Magnum v4 72B (anthracite-org/magnum-v4-72b). 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.
Magnum v4 72B (anthracite-org/magnum-v4-72b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Magnum v4 72B: Activate ephemeral prefix caching to slash input costs by 50% (down to $1.25/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek-V4 Flash (API ID: deepseek-v4-flash) vs Magnum v4 72B (API ID: anthracite-org/magnum-v4-72b), 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 | Magnum v4 72B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-v4-flash | anthracite-org/magnum-v4-72b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.07 / $0.14 | $2.5 / $5 | DeepSeek-V4 Flash |
| Cache Read Price (per 1M) | $0.035/1M (50% Off) | $1.25/1M (50% Off) | DeepSeek-V4 Flash |
| Context Window / Max Output | 1000K In / 64K Out | 32K In / 4K 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.
This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet(https://openrouter.ai/anthropic/claude-3.5-sonnet) and Opus(https://o
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 Magnum v4 72B
This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet(https://openrouter.ai/anthropic/claude-3.5-sonnet) and Opus(https://o Verified P90 latency of 280ms.