MiniMax: MiniMax M2.7 vs Meta: Muse Spark 1.3: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MiniMax: MiniMax M2.7 (minimax/minimax-m2.7) vs Meta: Muse Spark 1.3 (meta/muse-spark-1.3). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
Meta: Muse Spark 1.3 (meta/muse-spark-1.3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Muse Spark 1.3: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.625/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing MiniMax: MiniMax M2.7 (API ID: minimax/minimax-m2.7) vs Meta: Muse Spark 1.3 (API ID: meta/muse-spark-1.3), MiniMax: MiniMax M2.7 offers lower standard input pricing at $0.3/1M tokens ($1.2/1M output), while MiniMax: MiniMax M2.7 leads in cached prefix economics at $0.15/1M tokens (50% discount). MiniMax: MiniMax M2.7 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 M2.7 | Meta: Muse Spark 1.3 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | minimax/minimax-m2.7 | meta/muse-spark-1.3 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.3 / $1.2 | $1.25 / $4.25 | MiniMax: MiniMax M2.7 |
| Cache Read Price (per 1M) | $0.15/1M (50% Off) | $0.625/1M (50% Off) | MiniMax: MiniMax M2.7 |
| Context Window / Max Output | 200K In / 128K Out | 1024K In / 922K Out | Meta: Muse Spark 1.3 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MiniMax: MiniMax M2.7 (-0ms) |
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
Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended ta
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 M2.7 to Meta: Muse Spark 1.3
Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended ta Verified P90 latency of 280ms.