Z.ai: GLM 4.6 vs DeepSeek: DeepSeek V3.1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.6 (z-ai/glm-4.6) vs DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.6 (z-ai/glm-4.6) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.6: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.25/1M) 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 4.6 (API ID: z-ai/glm-4.6) vs DeepSeek: DeepSeek V3.1 (API ID: deepseek/deepseek-chat-v3.1), DeepSeek: DeepSeek V3.1 offers lower standard input pricing at $0.25/1M tokens ($0.95/1M output), while DeepSeek: DeepSeek V3.1 leads in cached prefix economics at $0.125/1M tokens (50% discount). Z.ai: GLM 4.6 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 4.6 | DeepSeek: DeepSeek V3.1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.6 | deepseek/deepseek-chat-v3.1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $2 | $0.25 / $0.95 | DeepSeek: DeepSeek V3.1 |
| Cache Read Price (per 1M) | $0.25/1M (50% Off) | $0.125/1M (50% Off) | DeepSeek: DeepSeek V3.1 |
| Context Window / Max Output | 200K In / 128K Out | 160K In / 32K Out | Z.ai: GLM 4.6 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.6 (-0ms) |
Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to
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 4.6 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.