DeepSeek: R1 vs Qwen: Qwen3 30B A3B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: R1 (deepseek/deepseek-r1) vs Qwen: Qwen3 30B A3B (qwen/qwen3-30b-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 30B A3B (qwen/qwen3-30b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 30B A3B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.06/1M) 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 30B A3B (API ID: qwen/qwen3-30b-a3b), Qwen: Qwen3 30B A3B offers lower standard input pricing at $0.12/1M tokens ($0.5/1M output), while Qwen: Qwen3 30B A3B leads in cached prefix economics at $0.06/1M 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 30B A3B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1 | qwen/qwen3-30b-a3b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.7 / $2.5 | $0.12 / $0.5 | Qwen: Qwen3 30B A3B |
| Cache Read Price (per 1M) | $0.35/1M (50% Off) | $0.06/1M (50% Off) | Qwen: Qwen3 30B A3B |
| Context Window / Max Output | 63K In / 16K Out | 40K 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
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, an
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 30B A3B
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, an Verified P90 latency of 280ms.