Z.ai: GLM Latest vs Meta: Llama 4 Scout: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM Latest (~z-ai/glm-latest) vs Meta: Llama 4 Scout (meta-llama/llama-4-scout). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM Latest (~z-ai/glm-latest) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM Latest: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.012/1M) or route via asynchronous Batch API for 50% off standard token rates.
Meta: Llama 4 Scout (meta-llama/llama-4-scout) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 4 Scout: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.09/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM Latest (API ID: ~z-ai/glm-latest) vs Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout), Z.ai: GLM Latest offers lower standard input pricing at $0.024/1M tokens ($4.4/1M output), while Z.ai: GLM Latest leads in cached prefix economics at $0.012/1M tokens (50% discount). Z.ai: GLM Latest 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 Latest | Meta: Llama 4 Scout | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | ~z-ai/glm-latest | meta-llama/llama-4-scout | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.024 / $4.4 | $0.18 / $0.59 | Z.ai: GLM Latest |
| Cache Read Price (per 1M) | $0.012/1M (50% Off) | $0.09/1M (50% Off) | Z.ai: GLM Latest |
| Context Window / Max Output | 1024K In / 128K Out | 1280K In / 115K Out | Z.ai: GLM Latest |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM Latest (-0ms) |
This model always redirects to the latest GLM model from Z.ai.
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimo
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 Latest to Meta: Llama 4 Scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimo Verified P90 latency of 280ms.