DeepSeek: R1 0528 vs Qwen: Qwen3 VL 235B A22B Instruct: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: R1 0528 (deepseek/deepseek-r1-0528) vs Qwen: Qwen3 VL 235B A22B Instruct (qwen/qwen3-vl-235b-a22b-instruct). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
Qwen: Qwen3 VL 235B A22B Instruct (qwen/qwen3-vl-235b-a22b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 VL 235B A22B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.105/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek: R1 0528 (API ID: deepseek/deepseek-r1-0528) vs Qwen: Qwen3 VL 235B A22B Instruct (API ID: qwen/qwen3-vl-235b-a22b-instruct), Qwen: Qwen3 VL 235B A22B Instruct offers lower standard input pricing at $0.21/1M tokens ($1.9/1M output), while Qwen: Qwen3 VL 235B A22B Instruct leads in cached prefix economics at $0.105/1M tokens (50% discount). DeepSeek: R1 0528 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 0528 | Qwen: Qwen3 VL 235B A22B Instruct | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1-0528 | qwen/qwen3-vl-235b-a22b-instruct | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $2.15 | $0.21 / $1.9 | Qwen: Qwen3 VL 235B A22B Instruct |
| Cache Read Price (per 1M) | $0.25/1M (50% Off) | $0.105/1M (50% Off) | Qwen: Qwen3 VL 235B A22B Instruct |
| Context Window / Max Output | 160K In / 32K Out | 256K In / 32K Out | DeepSeek: R1 0528 |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: R1 0528 (-0ms) |
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
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets gen
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 0528 to Qwen: Qwen3 VL 235B A22B Instruct
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets gen Verified P90 latency of 280ms.