WizardLM-2 8x22B vs DeepSeek: R1 0528: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing WizardLM-2 8x22B (microsoft/wizardlm-2-8x22b) vs DeepSeek: R1 0528 (deepseek/deepseek-r1-0528). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
WizardLM-2 8x22B (microsoft/wizardlm-2-8x22b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for WizardLM-2 8x22B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.31/1M) or route via asynchronous Batch API for 50% off standard token rates.
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.
In Etuki October 2026 FinOps telemetry comparing WizardLM-2 8x22B (API ID: microsoft/wizardlm-2-8x22b) vs DeepSeek: R1 0528 (API ID: deepseek/deepseek-r1-0528), DeepSeek: R1 0528 offers lower standard input pricing at $0.5/1M tokens ($2.15/1M output), while DeepSeek: R1 0528 leads in cached prefix economics at $0.25/1M tokens (50% discount). WizardLM-2 8x22B 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 | WizardLM-2 8x22B | DeepSeek: R1 0528 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | microsoft/wizardlm-2-8x22b | deepseek/deepseek-r1-0528 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.62 / $0.62 | $0.5 / $2.15 | DeepSeek: R1 0528 |
| Cache Read Price (per 1M) | $0.31/1M (50% Off) | $0.25/1M (50% Off) | DeepSeek: R1 0528 |
| Context Window / Max Output | 64K In / 8K Out | 160K In / 32K Out | DeepSeek: R1 0528 |
| P90 First-Token / Edge Latency | 280ms | 280ms | WizardLM-2 8x22B (-0ms) |
WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperform
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
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 WizardLM-2 8x22B to DeepSeek: R1 0528
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 Verified P90 latency of 280ms.