Qwen: Qwen3 VL 30B A3B Thinking vs DeepSeek: DeepSeek V3.2: Live Promos, Pricing, Hidden Fees & Latency
Qwen: Qwen3 VL 30B A3B Thinking (qwen/qwen3-vl-30b-a3b-thinking) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 VL 30B A3B Thinking: Activate ephemeral prefix caching to slash input costs by 50% (down to 0.145/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2) — Automatic Disk KV Cache (0.14/1M) & 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 VL 30B A3B Thinking (API ID: qwen/qwen3-vl-30b-a3b-thinking) vs DeepSeek: DeepSeek V3.2 (API ID: deepseek/deepseek-v3.2), DeepSeek: DeepSeek V3.2 offers lower standard input pricing at $0.28/1M tokens ($0.42/1M output), while DeepSeek: DeepSeek V3.2 leads in cached prefix economics at $0.14/1M tokens (50% discount). Qwen: Qwen3 VL 30B A3B Thinking 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 VL 30B A3B Thinking | DeepSeek: DeepSeek V3.2 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3-vl-30b-a3b-thinking | deepseek/deepseek-v3.2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.29 / $1 | $0.28 / $0.42 | DeepSeek: DeepSeek V3.2 |
| Cache Read Price (per 1M) | $0.145/1M (50% Off) | $0.14/1M (50% Off) | DeepSeek: DeepSeek V3.2 |
| Context Window / Max Output | 256K In / 230K Out | 160K In / 64K Out | Qwen: Qwen3 VL 30B A3B Thinking |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3 VL 30B A3B Thinking (-0ms) |
Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM
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 VL 30B A3B Thinking 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.