MiniMax: MiniMax M2-her vs MiniMax: MiniMax M2.7: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MiniMax: MiniMax M2-her (minimax/minimax-m2-her) vs MiniMax: MiniMax M2.7 (minimax/minimax-m2.7). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
MiniMax: MiniMax M2.7 (minimax/minimax-m2.7) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MiniMax: MiniMax M2.7: 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 MiniMax: MiniMax M2-her (API ID: minimax/minimax-m2-her) vs MiniMax: MiniMax M2.7 (API ID: minimax/minimax-m2.7), 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). MiniMax: MiniMax M2-her 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 M2-her | MiniMax: MiniMax M2.7 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | minimax/minimax-m2-her | minimax/minimax-m2.7 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.3 / $1.2 | $0.3 / $1.2 | MiniMax: MiniMax M2-her |
| Cache Read Price (per 1M) | $0.15/1M (50% Off) | $0.15/1M (50% Off) | MiniMax: MiniMax M2-her |
| Context Window / Max Output | 64K In / 2K Out | 200K In / 128K Out | MiniMax: MiniMax M2.7 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MiniMax: MiniMax M2-her (-0ms) |
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
MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evoluti
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 M2-her to MiniMax: MiniMax M2.7
MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evoluti Verified P90 latency of 280ms.