Qwen2.5 72B Instruct vs DeepSeek: R1 0528: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen2.5 72B Instruct (qwen/qwen-2.5-72b-instruct) vs DeepSeek: R1 0528 (deepseek/deepseek-r1-0528). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen2.5 72B Instruct (qwen/qwen-2.5-72b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen2.5 72B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.18/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: R1 0528 (deepseek/deepseek-r1-0528) — Automatic Disk KV Cache ($0.25/1M) & 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 Qwen2.5 72B Instruct (API ID: qwen/qwen-2.5-72b-instruct) vs DeepSeek: R1 0528 (API ID: deepseek/deepseek-r1-0528), Qwen2.5 72B Instruct offers lower standard input pricing at $0.36/1M tokens ($0.4/1M output), while Qwen2.5 72B Instruct leads in cached prefix economics at $0.18/1M tokens (50% discount). Qwen2.5 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 | Qwen2.5 72B Instruct | DeepSeek: R1 0528 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen-2.5-72b-instruct | deepseek/deepseek-r1-0528 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.36 / $0.4 | $0.5 / $2.15 | Qwen2.5 72B Instruct |
| Cache Read Price (per 1M) | $0.18/1M (50% Off) | $0.25/1M (50% Off) | Qwen2.5 72B Instruct |
| Context Window / Max Output | 32K In / 16K Out | 160K In / 32K Out | DeepSeek: R1 0528 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen2.5 72B Instruct (-0ms) |
Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabi
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 67
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 Qwen2.5 72B Instruct to DeepSeek: R1 0528
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 67 Verified P90 latency of 280ms.