Z.ai: GLM 4.6V vs Meta: Muse Wonder: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.6V (z-ai/glm-4.6v) vs Meta: Muse Wonder (meta/muse-wonder). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.6V (z-ai/glm-4.6v) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.6V: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.15/1M) or route via asynchronous Batch API for 50% off standard token rates.
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 Z.ai: GLM 4.6V (API ID: z-ai/glm-4.6v) vs Meta: Muse Wonder (API ID: meta/muse-wonder), Z.ai: GLM 4.6V offers lower standard input pricing at $0.3/1M tokens ($0.9/1M output), while Z.ai: GLM 4.6V leads in cached prefix economics at $0.15/1M tokens (50% discount). Z.ai: GLM 4.6V 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 | Z.ai: GLM 4.6V | Meta: Muse Wonder | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.6v | meta/muse-wonder | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.3 / $0.9 | $4 / $20 | Z.ai: GLM 4.6V |
| Cache Read Price (per 1M) | $0.15/1M (50% Off) | $2/1M (50% Off) | Z.ai: GLM 4.6V |
| Context Window / Max Output | 128K In / 32K Out | 1024K In / 922K Out | Meta: Muse Wonder |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.6V (-0ms) |
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K to
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 Z.ai: GLM 4.6V 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.