Qwen: Qwen2.5 VL 72B Instruct vs DeepSeek: DeepSeek V4 Flash 0423: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen2.5 VL 72B Instruct (qwen/qwen2.5-vl-72b-instruct) vs DeepSeek: DeepSeek V4 Flash 0423 (deepseek/deepseek-v4-flash). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen2.5 VL 72B Instruct (qwen/qwen2.5-vl-72b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen2.5 VL 72B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.4/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: DeepSeek V4 Flash 0423 (deepseek/deepseek-v4-flash) — Automatic Disk KV Cache ($0.0455/1M) & 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: Qwen2.5 VL 72B Instruct (API ID: qwen/qwen2.5-vl-72b-instruct) vs DeepSeek: DeepSeek V4 Flash 0423 (API ID: deepseek/deepseek-v4-flash), DeepSeek: DeepSeek V4 Flash 0423 offers lower standard input pricing at $0.091/1M tokens ($0.182/1M output), while DeepSeek: DeepSeek V4 Flash 0423 leads in cached prefix economics at $0.0455/1M tokens (50% discount). Qwen: Qwen2.5 VL 72B Instruct 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: Qwen2.5 VL 72B Instruct | DeepSeek: DeepSeek V4 Flash 0423 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen2.5-vl-72b-instruct | deepseek/deepseek-v4-flash | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.8 / $1 | $0.091 / $0.182 | DeepSeek: DeepSeek V4 Flash 0423 |
| Cache Read Price (per 1M) | $0.4/1M (50% Off) | $0.0455/1M (50% Off) | DeepSeek: DeepSeek V4 Flash 0423 |
| Context Window / Max Output | 125K In / 113K Out | 1024K In / 922K Out | DeepSeek: DeepSeek V4 Flash 0423 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen2.5 VL 72B Instruct (-0ms) |
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. I
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: Qwen2.5 VL 72B Instruct to DeepSeek: DeepSeek V4 Flash 0423
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. I Verified P90 latency of 280ms.