Meta: Muse Spark 1.2 vs Qwen: Qwen3.8 2.4T A95B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Muse Spark 1.2 (meta/muse-spark-1.2) vs Qwen: Qwen3.8 2.4T A95B (qwen/qwen3.8-2.4t-a95b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Meta: Muse Spark 1.2 (meta/muse-spark-1.2) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Muse Spark 1.2: 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.
Qwen: Qwen3.8 2.4T A95B (qwen/qwen3.8-2.4t-a95b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.8 2.4T A95B: Activate ephemeral prefix caching to slash input costs by 50% (down to $1/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Meta: Muse Spark 1.2 (API ID: meta/muse-spark-1.2) vs Qwen: Qwen3.8 2.4T A95B (API ID: qwen/qwen3.8-2.4t-a95b), Meta: Muse Spark 1.2 offers lower standard input pricing at $1.25/1M tokens ($4.25/1M output), while Meta: Muse Spark 1.2 leads in cached prefix economics at $0.625/1M tokens (50% discount). Meta: Muse Spark 1.2 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 1.2 | Qwen: Qwen3.8 2.4T A95B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta/muse-spark-1.2 | qwen/qwen3.8-2.4t-a95b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1.25 / $4.25 | $2 / $6 | Meta: Muse Spark 1.2 |
| Cache Read Price (per 1M) | $0.625/1M (50% Off) | $1/1M (50% Off) | Meta: Muse Spark 1.2 |
| Context Window / Max Output | 1024K In / 922K Out | 1024K In / 128K Out | Meta: Muse Spark 1.2 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Muse Spark 1.2 (-0ms) |
Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks. It accepts text, images, video, and PDF documents, returns text, and offers a 1M-token context wi
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out
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 1.2 to Qwen: Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out Verified P90 latency of 280ms.