Z.ai: GLM 4.5 vs Meta: Llama 3.1 8B Instruct: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.5 (z-ai/glm-4.5) vs Meta: Llama 3.1 8B Instruct (meta-llama/llama-3.1-8b-instruct). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.5 (z-ai/glm-4.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.3/1M) or route via asynchronous Batch API for 50% off standard token rates.
Meta: Llama 3.1 8B Instruct (meta-llama/llama-3.1-8b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 3.1 8B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.025/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 4.5 (API ID: z-ai/glm-4.5) vs Meta: Llama 3.1 8B Instruct (API ID: meta-llama/llama-3.1-8b-instruct), Meta: Llama 3.1 8B Instruct offers lower standard input pricing at $0.05/1M tokens ($0.08/1M output), while Meta: Llama 3.1 8B Instruct leads in cached prefix economics at $0.025/1M tokens (50% discount). Z.ai: GLM 4.5 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 4.5 | Meta: Llama 3.1 8B Instruct | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.5 | meta-llama/llama-3.1-8b-instruct | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.6 / $2.2 | $0.05 / $0.08 | Meta: Llama 3.1 8B Instruct |
| Cache Read Price (per 1M) | $0.3/1M (50% Off) | $0.025/1M (50% Off) | Meta: Llama 3.1 8B Instruct |
| Context Window / Max Output | 128K In / 96K Out | 128K In / 115K Out | Meta: Llama 3.1 8B Instruct |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.5 (-0ms) |
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of u
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance co
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 4.5 to Meta: Llama 3.1 8B Instruct
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance co Verified P90 latency of 280ms.