MoonshotAI: Kimi K2 0711 vs DeepSeek: DeepSeek V4 Flash 0423: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MoonshotAI: Kimi K2 0711 (moonshotai/kimi-k2) vs DeepSeek: DeepSeek V4 Flash 0423 (deepseek/deepseek-v4-flash). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MoonshotAI: Kimi K2 0711 (moonshotai/kimi-k2) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K2 0711: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.285/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: DeepSeek V4 Flash 0423 (deepseek/deepseek-v4-flash) — Automatic Disk KV Cache ($0.0455/1M) & 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 MoonshotAI: Kimi K2 0711 (API ID: moonshotai/kimi-k2) vs DeepSeek: DeepSeek V4 Flash 0423 (API ID: deepseek/deepseek-v4-flash), DeepSeek: DeepSeek V4 Flash 0423 offers lower standard input pricing at $0.091/1M tokens ($0.182/1M output), while DeepSeek: DeepSeek V4 Flash 0423 leads in cached prefix economics at $0.0455/1M tokens (50% discount). MoonshotAI: Kimi K2 0711 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 | MoonshotAI: Kimi K2 0711 | DeepSeek: DeepSeek V4 Flash 0423 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | moonshotai/kimi-k2 | deepseek/deepseek-v4-flash | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.57 / $2.3 | $0.091 / $0.182 | DeepSeek: DeepSeek V4 Flash 0423 |
| Cache Read Price (per 1M) | $0.285/1M (50% Off) | $0.0455/1M (50% Off) | DeepSeek: DeepSeek V4 Flash 0423 |
| Context Window / Max Output | 128K In / 96K Out | 1024K In / 922K Out | DeepSeek: DeepSeek V4 Flash 0423 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MoonshotAI: Kimi K2 0711 (-0ms) |
Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. I
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. I
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 MoonshotAI: Kimi K2 0711 to DeepSeek: DeepSeek V4 Flash 0423
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. I Verified P90 latency of 280ms.