Z.ai: GLM 4.5 vs Qwen: Qwen3 VL 8B Instruct: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.5 (z-ai/glm-4.5) vs Qwen: Qwen3 VL 8B Instruct (qwen/qwen3-vl-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.
Qwen: Qwen3 VL 8B Instruct (qwen/qwen3-vl-8b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 VL 8B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.125/1M) 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 Qwen: Qwen3 VL 8B Instruct (API ID: qwen/qwen3-vl-8b-instruct), Qwen: Qwen3 VL 8B Instruct offers lower standard input pricing at $0.25/1M tokens ($0.75/1M output), while Qwen: Qwen3 VL 8B Instruct leads in cached prefix economics at $0.125/1M 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 | Qwen: Qwen3 VL 8B Instruct | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.5 | qwen/qwen3-vl-8b-instruct | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.6 / $2.2 | $0.25 / $0.75 | Qwen: Qwen3 VL 8B Instruct |
| Cache Read Price (per 1M) | $0.3/1M (50% Off) | $0.125/1M (50% Off) | Qwen: Qwen3 VL 8B Instruct |
| Context Window / Max Output | 128K In / 96K Out | 256K In / 230K Out | Qwen: Qwen3 VL 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
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features
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 Qwen: Qwen3 VL 8B Instruct
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features Verified P90 latency of 280ms.