Z.ai: GLM 5 vs Relace: Relace Search: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 5 (z-ai/glm-5) vs Relace: Relace Search (relace/relace-search). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 5 (z-ai/glm-5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
Relace: Relace Search (relace/relace-search) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Relace: Relace Search: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 5 (API ID: z-ai/glm-5) vs Relace: Relace Search (API ID: relace/relace-search), Z.ai: GLM 5 offers lower standard input pricing at $1/1M tokens ($3.2/1M output), while Z.ai: GLM 5 leads in cached prefix economics at $0.5/1M tokens (50% discount). Z.ai: GLM 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 5 | Relace: Relace Search | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-5 | relace/relace-search | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1 / $3.2 | $1 / $3 | Z.ai: GLM 5 |
| Cache Read Price (per 1M) | $0.5/1M (50% Off) | $0.5/1M (50% Off) | Z.ai: GLM 5 |
| Context Window / Max Output | 200K In / 128K Out | 250K In / 125K Out | Z.ai: GLM 5 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 5 (-0ms) |
GLM-5 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-gr
The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search pe
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 5 to Relace: Relace Search
The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search pe Verified P90 latency of 280ms.