Qwen: Qwen3.5-35B-A3B vs Inception: Mercury 2: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b) vs Inception: Mercury 2 (inception/mercury-2). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-35B-A3B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.075/1M) or route via asynchronous Batch API for 50% off standard token rates.
Inception: Mercury 2 (inception/mercury-2) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Inception: Mercury 2: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.125/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.5-35B-A3B (API ID: qwen/qwen3.5-35b-a3b) vs Inception: Mercury 2 (API ID: inception/mercury-2), Qwen: Qwen3.5-35B-A3B offers lower standard input pricing at $0.15/1M tokens ($1/1M output), while Qwen: Qwen3.5-35B-A3B leads in cached prefix economics at $0.075/1M tokens (50% discount). Qwen: Qwen3.5-35B-A3B 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 | Qwen: Qwen3.5-35B-A3B | Inception: Mercury 2 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-35b-a3b | inception/mercury-2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.15 / $1 | $0.25 / $0.75 | Qwen: Qwen3.5-35B-A3B |
| Cache Read Price (per 1M) | $0.075/1M (50% Off) | $0.125/1M (50% Off) | Qwen: Qwen3.5-35B-A3B |
| Context Window / Max Output | 256K In / 16K Out | 125K In / 49K Out | Inception: Mercury 2 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5-35B-A3B (-0ms) |
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model,
Mercury 2 is an extremely fast reasoning LLM, and the first reasoning diffusion LLM (dLLM). Instead of generating tokens sequentially, Mercury 2 produces and refines multiple token
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 Qwen: Qwen3.5-35B-A3B to Inception: Mercury 2
Mercury 2 is an extremely fast reasoning LLM, and the first reasoning diffusion LLM (dLLM). Instead of generating tokens sequentially, Mercury 2 produces and refines multiple token Verified P90 latency of 280ms.