MiniMax: MiniMax M1 vs DeepSeek: R1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MiniMax: MiniMax M1 (minimax/minimax-m1) vs DeepSeek: R1 (deepseek/deepseek-r1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MiniMax: MiniMax M1 (minimax/minimax-m1) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MiniMax: MiniMax M1: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.275/1M) or route via asynchronous Batch API for 50% off standard token rates.
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 MiniMax: MiniMax M1 (API ID: minimax/minimax-m1) vs DeepSeek: R1 (API ID: deepseek/deepseek-r1), MiniMax: MiniMax M1 offers lower standard input pricing at $0.55/1M tokens ($2.2/1M output), while MiniMax: MiniMax M1 leads in cached prefix economics at $0.275/1M tokens (50% discount). MiniMax: MiniMax M1 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 | MiniMax: MiniMax M1 | DeepSeek: R1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | minimax/minimax-m1 | deepseek/deepseek-r1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.2 | $0.7 / $2.5 | MiniMax: MiniMax M1 |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $0.35/1M (50% Off) | MiniMax: MiniMax M1 |
| Context Window / Max Output | 977K In / 39K Out | 63K In / 16K Out | MiniMax: MiniMax M1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MiniMax: MiniMax M1 (-0ms) |
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture pa
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 MiniMax: MiniMax M1 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.