Z.ai: GLM 4.6V vs Meta: Llama 3.1 70B Instruct: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.6V (z-ai/glm-4.6v) vs Meta: Llama 3.1 70B Instruct (meta-llama/llama-3.1-70b-instruct). 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: Llama 3.1 70B Instruct (meta-llama/llama-3.1-70b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 3.1 70B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.2/1M) 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: Llama 3.1 70B Instruct (API ID: meta-llama/llama-3.1-70b-instruct), 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: Llama 3.1 70B Instruct | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.6v | meta-llama/llama-3.1-70b-instruct | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.3 / $0.9 | $0.4 / $0.4 | Z.ai: GLM 4.6V |
| Cache Read Price (per 1M) | $0.15/1M (50% Off) | $0.2/1M (50% Off) | Z.ai: GLM 4.6V |
| Context Window / Max Output | 128K In / 32K Out | 128K In / 16K Out | Z.ai: GLM 4.6V |
| 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
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonst
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: Llama 3.1 70B Instruct
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonst Verified P90 latency of 280ms.