Qwen: Qwen3.5-35B-A3B vs DeepSeek: DeepSeek V3.2: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b) vs DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2). 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.
DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2) — Automatic Disk KV Cache ($0.1295/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.5-35B-A3B (API ID: qwen/qwen3.5-35b-a3b) vs DeepSeek: DeepSeek V3.2 (API ID: deepseek/deepseek-v3.2), 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 | DeepSeek: DeepSeek V3.2 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-35b-a3b | deepseek/deepseek-v3.2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.15 / $1 | $0.259 / $0.8 | Qwen: Qwen3.5-35B-A3B |
| Cache Read Price (per 1M) | $0.075/1M (50% Off) | $0.1295/1M (50% Off) | Qwen: Qwen3.5-35B-A3B |
| Context Window / Max Output | 256K In / 16K Out | 160K In / 144K Out | DeepSeek: DeepSeek V3.2 |
| 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,
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse At
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 DeepSeek: DeepSeek V3.2
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse At Verified P90 latency of 280ms.