DeepSeek: R1 vs Qwen: Qwen3.5-35B-A3B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: R1 (deepseek/deepseek-r1) vs Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b). 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: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-35B-A3B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.075/1M) 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: Qwen3.5-35B-A3B (API ID: qwen/qwen3.5-35b-a3b), Qwen: Qwen3.5-35B-A3B offers lower standard input pricing at $0.15/1M tokens ($1/1M output), while Qwen: Qwen3.5-35B-A3B leads in cached prefix economics at $0.075/1M 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: Qwen3.5-35B-A3B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1 | qwen/qwen3.5-35b-a3b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.7 / $2.5 | $0.15 / $1 | Qwen: Qwen3.5-35B-A3B |
| Cache Read Price (per 1M) | $0.35/1M (50% Off) | $0.075/1M (50% Off) | Qwen: Qwen3.5-35B-A3B |
| Context Window / Max Output | 63K In / 16K Out | 256K In / 16K Out | DeepSeek: R1 |
| 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 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model,
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: Qwen3.5-35B-A3B
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, Verified P90 latency of 280ms.