Meta: Muse Spark 1.3 Contributor vs DeepSeek: DeepSeek V3.2 Exp: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Muse Spark 1.3 Contributor (meta/muse-spark-1.3-contributor) vs DeepSeek: DeepSeek V3.2 Exp (deepseek/deepseek-v3.2-exp). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
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.
In Etuki October 2026 FinOps telemetry comparing Meta: Muse Spark 1.3 Contributor (API ID: meta/muse-spark-1.3-contributor) vs DeepSeek: DeepSeek V3.2 Exp (API ID: deepseek/deepseek-v3.2-exp), 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). Meta: Muse Spark 1.3 Contributor 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 1.3 Contributor | DeepSeek: DeepSeek V3.2 Exp | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta/muse-spark-1.3-contributor | deepseek/deepseek-v3.2-exp | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1 / $0.2 | $0.27 / $0.41 | Meta: Muse Spark 1.3 Contributor |
| Cache Read Price (per 1M) | $0.05/1M (50% Off) | $0.135/1M (50% Off) | Meta: Muse Spark 1.3 Contributor |
| Context Window / Max Output | 1024K In / 922K Out | 160K In / 144K Out | Meta: Muse Spark 1.3 Contributor |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Muse Spark 1.3 Contributor (-0ms) |
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
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
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 1.3 Contributor to DeepSeek: DeepSeek V3.2 Exp
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 Verified P90 latency of 280ms.