Z.ai: GLM 4.5 Air vs DeepSeek: DeepSeek V3.1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.5 Air (z-ai/glm-4.5-air) vs DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.5 Air (z-ai/glm-4.5-air) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.5 Air: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.065/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1) — Automatic Disk KV Cache ($0.125/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 4.5 Air (API ID: z-ai/glm-4.5-air) vs DeepSeek: DeepSeek V3.1 (API ID: deepseek/deepseek-chat-v3.1), Z.ai: GLM 4.5 Air offers lower standard input pricing at $0.13/1M tokens ($0.85/1M output), while Z.ai: GLM 4.5 Air leads in cached prefix economics at $0.065/1M tokens (50% discount). Z.ai: GLM 4.5 Air 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 Air | DeepSeek: DeepSeek V3.1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.5-air | deepseek/deepseek-chat-v3.1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.13 / $0.85 | $0.25 / $0.95 | Z.ai: GLM 4.5 Air |
| Cache Read Price (per 1M) | $0.065/1M (50% Off) | $0.125/1M (50% Off) | Z.ai: GLM 4.5 Air |
| Context Window / Max Output | 128K In / 96K Out | 160K In / 32K Out | Z.ai: GLM 4.5 Air |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.5 Air (-0ms) |
GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE)
DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 ba
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 Air to DeepSeek: DeepSeek V3.1
DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 ba Verified P90 latency of 280ms.