OpenAI: o4 Mini (batch) vs MiniMax: MiniMax M1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing OpenAI: o4 Mini (batch) (openai/o4-mini:batch) vs MiniMax: MiniMax M1 (minimax/minimax-m1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
OpenAI: o4 Mini (batch) (openai/o4-mini:batch) 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).
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.
In Etuki October 2026 FinOps telemetry comparing OpenAI: o4 Mini (batch) (API ID: openai/o4-mini:batch) vs MiniMax: MiniMax M1 (API ID: minimax/minimax-m1), OpenAI: o4 Mini (batch) offers lower standard input pricing at $0.55/1M tokens ($2.2/1M output), while OpenAI: o4 Mini (batch) leads in cached prefix economics at $0.275/1M tokens (50% discount). OpenAI: o4 Mini (batch) 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 | OpenAI: o4 Mini (batch) | MiniMax: MiniMax M1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | openai/o4-mini:batch | minimax/minimax-m1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.2 | $0.55 / $2.2 | OpenAI: o4 Mini (batch) |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $0.275/1M (50% Off) | OpenAI: o4 Mini (batch) |
| Context Window / Max Output | 195K In / 98K Out | 977K In / 39K Out | OpenAI: o4 Mini (batch) |
| P90 First-Token / Edge Latency | 280ms | 280ms | OpenAI: o4 Mini (batch) (-0ms) |
OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports
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
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 OpenAI: o4 Mini (batch) to MiniMax: MiniMax M1
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 Verified P90 latency of 280ms.