DeepSeek: DeepSeek V3.2 vs Qwen: Qwen3 30B A3B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2) vs Qwen: Qwen3 30B A3B (qwen/qwen3-30b-a3b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
Qwen: Qwen3 30B A3B (qwen/qwen3-30b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 30B A3B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.06/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek: DeepSeek V3.2 (API ID: deepseek/deepseek-v3.2) vs Qwen: Qwen3 30B A3B (API ID: qwen/qwen3-30b-a3b), Qwen: Qwen3 30B A3B offers lower standard input pricing at $0.12/1M tokens ($0.5/1M output), while Qwen: Qwen3 30B A3B leads in cached prefix economics at $0.06/1M tokens (50% discount). DeepSeek: DeepSeek V3.2 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 | DeepSeek: DeepSeek V3.2 | Qwen: Qwen3 30B A3B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-v3.2 | qwen/qwen3-30b-a3b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.259 / $0.8 | $0.12 / $0.5 | Qwen: Qwen3 30B A3B |
| Cache Read Price (per 1M) | $0.1295/1M (50% Off) | $0.06/1M (50% Off) | Qwen: Qwen3 30B A3B |
| Context Window / Max Output | 160K In / 144K Out | 40K In / 16K Out | DeepSeek: DeepSeek V3.2 |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: DeepSeek V3.2 (-0ms) |
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
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, an
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 DeepSeek: DeepSeek V3.2 to Qwen: Qwen3 30B A3B
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, an Verified P90 latency of 280ms.