DeepSeek: DeepSeek V4.1 Flash (batch) vs Meta: Muse Wonder: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: DeepSeek V4.1 Flash (batch) (deepseek/deepseek-v4.1-flash:batch) vs Meta: Muse Wonder (meta/muse-wonder). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: DeepSeek V4.1 Flash (batch) (deepseek/deepseek-v4.1-flash:batch) — Automatic Disk KV Cache ($0.056/1M) & 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 Wonder (meta/muse-wonder) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Muse Wonder: Activate ephemeral prefix caching to slash input costs by 50% (down to $2/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek: DeepSeek V4.1 Flash (batch) (API ID: deepseek/deepseek-v4.1-flash:batch) vs Meta: Muse Wonder (API ID: meta/muse-wonder), DeepSeek: DeepSeek V4.1 Flash (batch) offers lower standard input pricing at $0.112/1M tokens ($0.336/1M output), while DeepSeek: DeepSeek V4.1 Flash (batch) leads in cached prefix economics at $0.056/1M tokens (50% discount). DeepSeek: DeepSeek V4.1 Flash (batch) 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 V4.1 Flash (batch) | Meta: Muse Wonder | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-v4.1-flash:batch | meta/muse-wonder | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.112 / $0.336 | $4 / $20 | DeepSeek: DeepSeek V4.1 Flash (batch) |
| Cache Read Price (per 1M) | $0.056/1M (50% Off) | $2/1M (50% Off) | DeepSeek: DeepSeek V4.1 Flash (batch) |
| Context Window / Max Output | 1024K In / 128K Out | 1024K In / 922K Out | Meta: Muse Wonder |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: DeepSeek V4.1 Flash (batch) (-0ms) |
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters o
Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and retu
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 V4.1 Flash (batch) to Meta: Muse Wonder
Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and retu Verified P90 latency of 280ms.