Inception: Mercury 3 vs Qwen: Qwen3.5-Flash: Live Promos, Pricing, Hidden Fees & Latency
Inception: Mercury 3 (inception/mercury-3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Inception: Mercury 3: 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.
Qwen: Qwen3.5-Flash (qwen/qwen3.5-flash-02-23) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to 0.0325/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Inception: Mercury 3 (API ID: inception/mercury-3) vs Qwen: Qwen3.5-Flash (API ID: qwen/qwen3.5-flash-02-23), Qwen: Qwen3.5-Flash offers lower standard input pricing at $0.065/1M tokens ($0.26/1M output), while Qwen: Qwen3.5-Flash leads in cached prefix economics at $0.0325/1M tokens (50% discount). Inception: Mercury 3 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 | Inception: Mercury 3 | Qwen: Qwen3.5-Flash | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | inception/mercury-3 | qwen/qwen3.5-flash-02-23 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1 / $0.35 | $0.065 / $0.26 | Qwen: Qwen3.5-Flash |
| Cache Read Price (per 1M) | $0.05/1M (50% Off) | $0.0325/1M (50% Off) | Qwen: Qwen3.5-Flash |
| Context Window / Max Output | 977K In / 128K Out | 977K In / 64K Out | Inception: Mercury 3 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Inception: Mercury 3 (-0ms) |
Mercury 3 is a diffusion-based language model from Inception, and the lab's most capable model to date. It is suited for agentic workflows and real-time applications where latency
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving hi
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 Inception: Mercury 3 to Qwen: Qwen3.5-Flash
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving hi Verified P90 latency of 280ms.