Meta: Llama 4 Scout vs Z.ai: GLM 5.3: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Llama 4 Scout (meta-llama/llama-4-scout) vs Z.ai: GLM 5.3 (z-ai/glm-5.3). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
Z.ai: GLM 5.3 (z-ai/glm-5.3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.3: 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.
In Etuki October 2026 FinOps telemetry comparing Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout) vs Z.ai: GLM 5.3 (API ID: z-ai/glm-5.3), Z.ai: GLM 5.3 offers lower standard input pricing at $0.1/1M tokens ($4.8/1M output), while Z.ai: GLM 5.3 leads in cached prefix economics at $0.05/1M tokens (50% discount). Meta: Llama 4 Scout 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: Llama 4 Scout | Z.ai: GLM 5.3 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta-llama/llama-4-scout | z-ai/glm-5.3 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.18 / $0.59 | $0.1 / $4.8 | Z.ai: GLM 5.3 |
| Cache Read Price (per 1M) | $0.09/1M (50% Off) | $0.05/1M (50% Off) | Z.ai: GLM 5.3 |
| Context Window / Max Output | 1280K In / 115K Out | 1024K In / 922K Out | Z.ai: GLM 5.3 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Llama 4 Scout (-0ms) |
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
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context w
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: Llama 4 Scout to Z.ai: GLM 5.3
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context w Verified P90 latency of 280ms.