MiniMax: MiniMax M2 vs OpenAI: o1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MiniMax: MiniMax M2 (minimax/minimax-m2) vs OpenAI: o1 (openai/o1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MiniMax: MiniMax M2 (minimax/minimax-m2) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MiniMax: MiniMax M2: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.1275/1M) or route via asynchronous Batch API for 50% off standard token rates.
OpenAI: o1 (openai/o1) Startup Credits & Batch API 50% Off Promo
OpenAI GPT-6.1 Sol ($2/$10 base rate) qualifies for immediate 75% automatic prefix caching ($0.50/1M), 50% off 24-hour Batch API completions ($1/$5), and up to $150,000 in OpenAI/Azure Foundry credits via Microsoft for Startups Founders Hub (no VC funding required for initial tiers).
In Etuki October 2026 FinOps telemetry comparing MiniMax: MiniMax M2 (API ID: minimax/minimax-m2) vs OpenAI: o1 (API ID: openai/o1), MiniMax: MiniMax M2 offers lower standard input pricing at $0.255/1M tokens ($1.02/1M output), while MiniMax: MiniMax M2 leads in cached prefix economics at $0.1275/1M tokens (50% discount). MiniMax: MiniMax M2 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 | OpenAI: o1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | minimax/minimax-m2 | openai/o1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.255 / $1.02 | $15 / $60 | MiniMax: MiniMax M2 |
| Cache Read Price (per 1M) | $0.1275/1M (50% Off) | $7.5/1M (50% Off) | MiniMax: MiniMax M2 |
| Context Window / Max Output | 200K In / 128K Out | 195K In / 98K Out | MiniMax: MiniMax M2 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MiniMax: MiniMax M2 (-0ms) |
MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it de
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learn
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 to OpenAI: o1
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learn Verified P90 latency of 280ms.