DeepSeek: R1 vs Qwen: Qwen3.5-Flash: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: R1 (deepseek/deepseek-r1) vs Qwen: Qwen3.5-Flash (qwen/qwen3.5-flash-02-23). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: R1 (deepseek/deepseek-r1) — Automatic Disk KV Cache ($0.35/1M) & 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: Qwen3.5-Flash (qwen/qwen3.5-flash-02-23) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0325/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek: R1 (API ID: deepseek/deepseek-r1) vs Qwen: Qwen3.5-Flash (API ID: qwen/qwen3.5-flash-02-23), Qwen: Qwen3.5-Flash offers lower standard input pricing at $0.065/1M tokens ($0.26/1M output), while Qwen: Qwen3.5-Flash leads in cached prefix economics at $0.0325/1M tokens (50% discount). DeepSeek: R1 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: R1 | Qwen: Qwen3.5-Flash | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1 | qwen/qwen3.5-flash-02-23 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.7 / $2.5 | $0.065 / $0.26 | Qwen: Qwen3.5-Flash |
| Cache Read Price (per 1M) | $0.35/1M (50% Off) | $0.0325/1M (50% Off) | Qwen: Qwen3.5-Flash |
| Context Window / Max Output | 63K In / 16K Out | 977K In / 64K Out | Qwen: Qwen3.5-Flash |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: R1 (-0ms) |
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving hi
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: R1 to Qwen: Qwen3.5-Flash
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving hi Verified P90 latency of 280ms.