WizardLM-2 8x22B vs Z.ai: GLM 4.6V: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing WizardLM-2 8x22B (microsoft/wizardlm-2-8x22b) vs Z.ai: GLM 4.6V (z-ai/glm-4.6v). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
WizardLM-2 8x22B (microsoft/wizardlm-2-8x22b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for WizardLM-2 8x22B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.31/1M) or route via asynchronous Batch API for 50% off standard token rates.
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.
In Etuki October 2026 FinOps telemetry comparing WizardLM-2 8x22B (API ID: microsoft/wizardlm-2-8x22b) vs Z.ai: GLM 4.6V (API ID: z-ai/glm-4.6v), 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). WizardLM-2 8x22B 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 | WizardLM-2 8x22B | Z.ai: GLM 4.6V | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | microsoft/wizardlm-2-8x22b | z-ai/glm-4.6v | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.62 / $0.62 | $0.3 / $0.9 | Z.ai: GLM 4.6V |
| Cache Read Price (per 1M) | $0.31/1M (50% Off) | $0.15/1M (50% Off) | Z.ai: GLM 4.6V |
| Context Window / Max Output | 64K In / 8K Out | 128K In / 32K Out | Z.ai: GLM 4.6V |
| P90 First-Token / Edge Latency | 280ms | 280ms | WizardLM-2 8x22B (-0ms) |
WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperform
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
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 WizardLM-2 8x22B to Z.ai: GLM 4.6V
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 Verified P90 latency of 280ms.