Reflection: Beam 1.0 501B A23B vs Z.ai: GLM 4.5: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Reflection: Beam 1.0 501B A23B (reflection/beam-1.0-501b-a23b) vs Z.ai: GLM 4.5 (z-ai/glm-4.5). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Reflection: Beam 1.0 501B A23B (reflection/beam-1.0-501b-a23b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Reflection: Beam 1.0 501B A23B: 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.
Z.ai: GLM 4.5 (z-ai/glm-4.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.3/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Reflection: Beam 1.0 501B A23B (API ID: reflection/beam-1.0-501b-a23b) vs Z.ai: GLM 4.5 (API ID: z-ai/glm-4.5), Z.ai: GLM 4.5 offers lower standard input pricing at $0.6/1M tokens ($2.2/1M output), while Z.ai: GLM 4.5 leads in cached prefix economics at $0.3/1M tokens (50% discount). Reflection: Beam 1.0 501B A23B 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 | Reflection: Beam 1.0 501B A23B | Z.ai: GLM 4.5 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | reflection/beam-1.0-501b-a23b | z-ai/glm-4.5 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1 / $3 | $0.6 / $2.2 | Z.ai: GLM 4.5 |
| Cache Read Price (per 1M) | $0.5/1M (50% Off) | $0.3/1M (50% Off) | Z.ai: GLM 4.5 |
| Context Window / Max Output | 1024K In / 922K Out | 128K In / 96K Out | Reflection: Beam 1.0 501B A23B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Reflection: Beam 1.0 501B A23B (-0ms) |
Beam 1.0 is Reflection's open-weight reasoning model for coding and long-horizon agentic workflows. It uses a sparse mixture-of-experts architecture with 501B total and 23B active
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of u
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 Reflection: Beam 1.0 501B A23B to Z.ai: GLM 4.5
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of u Verified P90 latency of 280ms.