Upstage: Solar Pro 4 vs Z.ai: GLM 5.3 (batch): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Upstage: Solar Pro 4 (upstage/solar-pro4) vs Z.ai: GLM 5.3 (batch) (z-ai/glm-5.3:batch). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Upstage: Solar Pro 4 (upstage/solar-pro4) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Upstage: Solar Pro 4: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.045/1M) or route via asynchronous Batch API for 50% off standard token rates.
Z.ai: GLM 5.3 (batch) (z-ai/glm-5.3:batch) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.3 (batch): Activate ephemeral prefix caching to slash input costs by 50% (down to $0.225/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Upstage: Solar Pro 4 (API ID: upstage/solar-pro4) vs Z.ai: GLM 5.3 (batch) (API ID: z-ai/glm-5.3:batch), Upstage: Solar Pro 4 offers lower standard input pricing at $0.09/1M tokens ($0.36/1M output), while Upstage: Solar Pro 4 leads in cached prefix economics at $0.045/1M tokens (50% discount). Upstage: Solar Pro 4 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 | Upstage: Solar Pro 4 | Z.ai: GLM 5.3 (batch) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | upstage/solar-pro4 | z-ai/glm-5.3:batch | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.09 / $0.36 | $0.45 / $2 | Upstage: Solar Pro 4 |
| Cache Read Price (per 1M) | $0.045/1M (50% Off) | $0.225/1M (50% Off) | Upstage: Solar Pro 4 |
| Context Window / Max Output | 512K In / 128K Out | 1024K In / 128K Out | Upstage: Solar Pro 4 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Upstage: Solar Pro 4 (-0ms) |
Solar Pro 4 is Upstage's cost-efficient large language model, featuring a 524K context window. It is built for long-horizon tasks and agentic workflows, with strong capabilities in
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context w
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 Upstage: Solar Pro 4 to Z.ai: GLM 5.3 (batch)
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context w Verified P90 latency of 280ms.