MiniMax: MiniMax M1 vs ByteDance: UI-TARS 7B : Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MiniMax: MiniMax M1 (minimax/minimax-m1) vs ByteDance: UI-TARS 7B (bytedance/ui-tars-1.5-7b). 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.
ByteDance: UI-TARS 7B (bytedance/ui-tars-1.5-7b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for ByteDance: UI-TARS 7B : Activate ephemeral prefix caching to slash input costs by 50% (down to $0.05/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing MiniMax: MiniMax M1 (API ID: minimax/minimax-m1) vs ByteDance: UI-TARS 7B (API ID: bytedance/ui-tars-1.5-7b), ByteDance: UI-TARS 7B offers lower standard input pricing at $0.1/1M tokens ($0.2/1M output), while ByteDance: UI-TARS 7B leads in cached prefix economics at $0.05/1M 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 | ByteDance: UI-TARS 7B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | minimax/minimax-m1 | bytedance/ui-tars-1.5-7b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.2 | $0.1 / $0.2 | ByteDance: UI-TARS 7B |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $0.05/1M (50% Off) | ByteDance: UI-TARS 7B |
| Context Window / Max Output | 977K In / 39K Out | 125K In / 2K 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
UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. Built by ByteDance, i
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 ByteDance: UI-TARS 7B
UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. Built by ByteDance, i Verified P90 latency of 280ms.