DeepSeek-V4 Pro vs Qwen: Qwen3.5-122B-A10B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-V4 Pro (deepseek-v4-pro) vs Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Zero-Config Disk KV Cache ($0.07/1M) & Off-Peak UTC Batch Arbitrage
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: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-122B-A10B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.13/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek-V4 Pro (API ID: deepseek-v4-pro) vs Qwen: Qwen3.5-122B-A10B (API ID: qwen/qwen3.5-122b-a10b), DeepSeek-V4 Pro offers lower standard input pricing at $0.14/1M tokens ($0.28/1M output), while DeepSeek-V4 Pro leads in cached prefix economics at $0.07/1M tokens (50% discount). Qwen: Qwen3.5-122B-A10B achieves a 180ms faster P90 time-to-first-token latency (280ms vs 460ms).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | DeepSeek-V4 Pro | Qwen: Qwen3.5-122B-A10B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-v4-pro | qwen/qwen3.5-122b-a10b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.14 / $0.28 | $0.26 / $2.08 | DeepSeek-V4 Pro |
| Cache Read Price (per 1M) | $0.07/1M (50% Off) | $0.13/1M (50% Off) | DeepSeek-V4 Pro |
| Context Window / Max Output | 1000K In / 128K Out | 256K In / 64K Out | DeepSeek-V4 Pro |
| P90 First-Token / Edge Latency | 460ms | 280ms | Qwen: Qwen3.5-122B-A10B (-180ms) |
1.6-Trillion parameter (49B active) MoE architecture with Fine-Grained Sparse Attention and $0.07/1M disk KV cache hits.
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving
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-V4 Pro to Qwen: Qwen3.5-122B-A10B
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving Verified P90 latency of 280ms.