Reka Edge vs Z.ai: GLM 4.6: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Reka Edge (rekaai/reka-edge) vs Z.ai: GLM 4.6 (z-ai/glm-4.6). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Reka Edge (rekaai/reka-edge) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Reka Edge: 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 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.
In Etuki October 2026 FinOps telemetry comparing Reka Edge (API ID: rekaai/reka-edge) vs Z.ai: GLM 4.6 (API ID: z-ai/glm-4.6), Reka Edge offers lower standard input pricing at $0.1/1M tokens ($0.1/1M output), while Reka Edge leads in cached prefix economics at $0.05/1M tokens (50% discount). Reka Edge 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 | Reka Edge | Z.ai: GLM 4.6 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | rekaai/reka-edge | z-ai/glm-4.6 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1 / $0.1 | $0.5 / $2 | Reka Edge |
| Cache Read Price (per 1M) | $0.05/1M (50% Off) | $0.25/1M (50% Off) | Reka Edge |
| Context Window / Max Output | 16K In / 14K Out | 200K In / 128K Out | Z.ai: GLM 4.6 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Reka Edge (-0ms) |
Reka Edge is an extremely efficient 7B multimodal vision-language model that accepts image/video+text inputs and generates text outputs. This model is optimized specifically to del
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
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 Reka Edge to Z.ai: GLM 4.6
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 Verified P90 latency of 280ms.