DeepSeek: R1 0528 vs MiniMax: MiniMax M2-her: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: R1 0528 (deepseek/deepseek-r1-0528) vs MiniMax: MiniMax M2-her (minimax/minimax-m2-her). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: R1 0528 (deepseek/deepseek-r1-0528) — Automatic Disk KV Cache ($0.25/1M) & 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.
MiniMax: MiniMax M2-her (minimax/minimax-m2-her) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MiniMax: MiniMax M2-her: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.15/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek: R1 0528 (API ID: deepseek/deepseek-r1-0528) vs MiniMax: MiniMax M2-her (API ID: minimax/minimax-m2-her), MiniMax: MiniMax M2-her offers lower standard input pricing at $0.3/1M tokens ($1.2/1M output), while MiniMax: MiniMax M2-her leads in cached prefix economics at $0.15/1M tokens (50% discount). DeepSeek: R1 0528 achieves a 0ms faster P90 time-to-first-token latency (280ms vs 280ms).
Auto-discovered via live global telemetry registry.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | DeepSeek: R1 0528 | MiniMax: MiniMax M2-her | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1-0528 | minimax/minimax-m2-her | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $2.15 | $0.3 / $1.2 | MiniMax: MiniMax M2-her |
| Cache Read Price (per 1M) | $0.25/1M (50% Off) | $0.15/1M (50% Off) | MiniMax: MiniMax M2-her |
| Context Window / Max Output | 160K In / 32K Out | 64K In / 2K Out | DeepSeek: R1 0528 |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: R1 0528 (-0ms) |
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 67
MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. Designed to stay consistent in
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: R1 0528 to MiniMax: MiniMax M2-her
MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. Designed to stay consistent in Verified P90 latency of 280ms.