inclusionAI: Ling 3.0 Flash vs DeepSeek-R1 (Reasoner): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing inclusionAI: Ling 3.0 Flash (inclusionai/ling-3.0-flash) vs DeepSeek-R1 (Reasoner) (deepseek-reasoner). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
inclusionAI: Ling 3.0 Flash (inclusionai/ling-3.0-flash) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for inclusionAI: Ling 3.0 Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0105/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek-R1 (Reasoner) (deepseek-reasoner) — Automatic Disk KV Cache ($0.275/1M) & 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 inclusionAI: Ling 3.0 Flash (API ID: inclusionai/ling-3.0-flash) vs DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner), inclusionAI: Ling 3.0 Flash offers lower standard input pricing at $0.021/1M tokens ($0.063/1M output), while inclusionAI: Ling 3.0 Flash leads in cached prefix economics at $0.0105/1M tokens (50% discount). inclusionAI: Ling 3.0 Flash achieves a 260ms faster P90 time-to-first-token latency (280ms vs 540ms).
Auto-discovered via live global telemetry registry.
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | inclusionAI: Ling 3.0 Flash | DeepSeek-R1 (Reasoner) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | inclusionai/ling-3.0-flash | deepseek-reasoner | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.021 / $0.063 | $0.55 / $2.19 | inclusionAI: Ling 3.0 Flash |
| Cache Read Price (per 1M) | $0.0105/1M (50% Off) | $0.275/1M (50% Off) | inclusionAI: Ling 3.0 Flash |
| Context Window / Max Output | 256K In / 32K Out | 400K In / 128K Out | DeepSeek-R1 (Reasoner) |
| P90 First-Token / Edge Latency | 280ms | 540ms | inclusionAI: Ling 3.0 Flash (-260ms) |
*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and pr
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
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 inclusionAI: Ling 3.0 Flash to DeepSeek-R1 (Reasoner)
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis. Verified P90 latency of 540ms.