WizardLM-2 8x22B vs DeepSeek: R1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing WizardLM-2 8x22B (microsoft/wizardlm-2-8x22b) vs DeepSeek: R1 (deepseek/deepseek-r1). 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 (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.
In Etuki October 2026 FinOps telemetry comparing WizardLM-2 8x22B (API ID: microsoft/wizardlm-2-8x22b) vs DeepSeek: R1 (API ID: deepseek/deepseek-r1), WizardLM-2 8x22B offers lower standard input pricing at $0.62/1M tokens ($0.62/1M output), while WizardLM-2 8x22B leads in cached prefix economics at $0.31/1M 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 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | microsoft/wizardlm-2-8x22b | deepseek/deepseek-r1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.62 / $0.62 | $0.7 / $2.5 | WizardLM-2 8x22B |
| Cache Read Price (per 1M) | $0.31/1M (50% Off) | $0.35/1M (50% Off) | WizardLM-2 8x22B |
| Context Window / Max Output | 64K In / 8K Out | 63K In / 16K Out | DeepSeek: R1 |
| 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
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
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
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 Verified P90 latency of 280ms.