Meta: Muse Spark 1.1 vs DeepSeek: DeepSeek V3.2: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Muse Spark 1.1 (meta/muse-spark-1.1) vs DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2) — Automatic Disk KV Cache ($0.1295/1M) & 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.
In Etuki October 2026 FinOps telemetry comparing Meta: Muse Spark 1.1 (API ID: meta/muse-spark-1.1) vs DeepSeek: DeepSeek V3.2 (API ID: deepseek/deepseek-v3.2), DeepSeek: DeepSeek V3.2 offers lower standard input pricing at $0.259/1M tokens ($0.8/1M output), while DeepSeek: DeepSeek V3.2 leads in cached prefix economics at $0.1295/1M tokens (50% discount). Meta: Muse Spark 1.1 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 | Meta: Muse Spark 1.1 | DeepSeek: DeepSeek V3.2 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta/muse-spark-1.1 | deepseek/deepseek-v3.2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1.25 / $4.25 | $0.259 / $0.8 | DeepSeek: DeepSeek V3.2 |
| Cache Read Price (per 1M) | $0.625/1M (50% Off) | $0.1295/1M (50% Off) | DeepSeek: DeepSeek V3.2 |
| Context Window / Max Output | 1024K In / 922K Out | 160K In / 144K Out | Meta: Muse Spark 1.1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Muse Spark 1.1 (-0ms) |
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
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse At
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 Meta: Muse Spark 1.1 to DeepSeek: DeepSeek V3.2
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse At Verified P90 latency of 280ms.