Qwen: Qwen2.5 7B Instruct vs Z.ai: GLM 5.1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen2.5 7B Instruct (qwen/qwen-2.5-7b-instruct) vs Z.ai: GLM 5.1 (z-ai/glm-5.1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen2.5 7B Instruct (qwen/qwen-2.5-7b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen2.5 7B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.05/1M) or route via asynchronous Batch API for 50% off standard token rates.
Z.ai: GLM 5.1 (z-ai/glm-5.1) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.1: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.483/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen2.5 7B Instruct (API ID: qwen/qwen-2.5-7b-instruct) vs Z.ai: GLM 5.1 (API ID: z-ai/glm-5.1), Qwen: Qwen2.5 7B Instruct offers lower standard input pricing at $0.1/1M tokens ($0.2/1M output), while Qwen: Qwen2.5 7B Instruct leads in cached prefix economics at $0.05/1M tokens (50% discount). Qwen: Qwen2.5 7B Instruct 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 | Qwen: Qwen2.5 7B Instruct | Z.ai: GLM 5.1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen-2.5-7b-instruct | z-ai/glm-5.1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1 / $0.2 | $0.966 / $3.036 | Qwen: Qwen2.5 7B Instruct |
| Cache Read Price (per 1M) | $0.05/1M (50% Off) | $0.483/1M (50% Off) | Qwen: Qwen2.5 7B Instruct |
| Context Window / Max Output | 32K In / 29K Out | 200K In / 125K Out | Z.ai: GLM 5.1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen2.5 7B Instruct (-0ms) |
Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabil
GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interaction
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 Qwen: Qwen2.5 7B Instruct to Z.ai: GLM 5.1
GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interaction Verified P90 latency of 280ms.