MoonshotAI: Kimi K3 (batch) vs DeepSeek: R1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MoonshotAI: Kimi K3 (batch) (moonshotai/kimi-k3:batch) vs DeepSeek: R1 (deepseek/deepseek-r1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MoonshotAI: Kimi K3 (batch) (moonshotai/kimi-k3:batch) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K3 (batch): Activate ephemeral prefix caching to slash input costs by 50% (down to $1.14/1M) 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 MoonshotAI: Kimi K3 (batch) (API ID: moonshotai/kimi-k3:batch) vs DeepSeek: R1 (API ID: deepseek/deepseek-r1), DeepSeek: R1 offers lower standard input pricing at $0.7/1M tokens ($2.5/1M output), while DeepSeek: R1 leads in cached prefix economics at $0.35/1M tokens (50% discount). MoonshotAI: Kimi K3 (batch) 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 K3 (batch) | DeepSeek: R1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | moonshotai/kimi-k3:batch | deepseek/deepseek-r1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $2.28 / $11.4 | $0.7 / $2.5 | DeepSeek: R1 |
| Cache Read Price (per 1M) | $1.14/1M (50% Off) | $0.35/1M (50% Off) | DeepSeek: R1 |
| Context Window / Max Output | 1024K In / 16K Out | 63K In / 16K Out | MoonshotAI: Kimi K3 (batch) |
| P90 First-Token / Edge Latency | 280ms | 280ms | MoonshotAI: Kimi K3 (batch) (-0ms) |
Kimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is par
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 MoonshotAI: Kimi K3 (batch) 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.