DeepSeek: DeepSeek V3.2 Exp vs Meta: Muse Spark 1.3 Contributor: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: DeepSeek V3.2 Exp (deepseek/deepseek-v3.2-exp) vs Meta: Muse Spark 1.3 Contributor (meta/muse-spark-1.3-contributor). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: DeepSeek V3.2 Exp (deepseek/deepseek-v3.2-exp) — Automatic Disk KV Cache ($0.135/1M) & 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 1.3 Contributor (meta/muse-spark-1.3-contributor) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Muse Spark 1.3 Contributor: 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 DeepSeek: DeepSeek V3.2 Exp (API ID: deepseek/deepseek-v3.2-exp) vs Meta: Muse Spark 1.3 Contributor (API ID: meta/muse-spark-1.3-contributor), Meta: Muse Spark 1.3 Contributor offers lower standard input pricing at $0.1/1M tokens ($0.2/1M output), while Meta: Muse Spark 1.3 Contributor leads in cached prefix economics at $0.05/1M tokens (50% discount). DeepSeek: DeepSeek V3.2 Exp 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 | DeepSeek: DeepSeek V3.2 Exp | Meta: Muse Spark 1.3 Contributor | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-v3.2-exp | meta/muse-spark-1.3-contributor | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.27 / $0.41 | $0.1 / $0.2 | Meta: Muse Spark 1.3 Contributor |
| Cache Read Price (per 1M) | $0.135/1M (50% Off) | $0.05/1M (50% Off) | Meta: Muse Spark 1.3 Contributor |
| Context Window / Max Output | 160K In / 144K Out | 1024K In / 922K Out | Meta: Muse Spark 1.3 Contributor |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: DeepSeek V3.2 Exp (-0ms) |
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attentio
Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and codi
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: DeepSeek V3.2 Exp to Meta: Muse Spark 1.3 Contributor
Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and codi Verified P90 latency of 280ms.