DeepSeek-R1 (Reasoner) vs Meta: Muse Spark 1.1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-R1 (Reasoner) (deepseek-reasoner) vs Meta: Muse Spark 1.1 (meta/muse-spark-1.1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek-R1 (Reasoner) (deepseek-reasoner) — Automatic Disk KV Cache ($0.275/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
Meta: Muse Spark 1.1 (meta/muse-spark-1.1) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Muse Spark 1.1: 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 DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner) vs Meta: Muse Spark 1.1 (API ID: meta/muse-spark-1.1), DeepSeek-R1 (Reasoner) offers lower standard input pricing at $0.55/1M tokens ($2.19/1M output), while DeepSeek-R1 (Reasoner) leads in cached prefix economics at $0.275/1M tokens (50% discount). Meta: Muse Spark 1.1 achieves a 260ms faster P90 time-to-first-token latency (280ms vs 540ms).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | DeepSeek-R1 (Reasoner) | Meta: Muse Spark 1.1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-reasoner | meta/muse-spark-1.1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.19 | $1.25 / $4.25 | DeepSeek-R1 (Reasoner) |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $0.625/1M (50% Off) | DeepSeek-R1 (Reasoner) |
| Context Window / Max Output | 400K In / 128K Out | 1024K In / 922K Out | Meta: Muse Spark 1.1 |
| P90 First-Token / Edge Latency | 540ms | 280ms | Meta: Muse Spark 1.1 (-260ms) |
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, and PDF documents and returns text, with a 1M-token context windo
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 DeepSeek-R1 (Reasoner) to Meta: Muse Spark 1.1
Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, and PDF documents and returns text, with a 1M-token context windo Verified P90 latency of 280ms.