MoonshotAI: Kimi K3 vs Qwen: Qwen3.5-35B-A3B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MoonshotAI: Kimi K3 (moonshotai/kimi-k3) vs Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MoonshotAI: Kimi K3 (moonshotai/kimi-k3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K3: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.225/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-35B-A3B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.075/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing MoonshotAI: Kimi K3 (API ID: moonshotai/kimi-k3) vs Qwen: Qwen3.5-35B-A3B (API ID: qwen/qwen3.5-35b-a3b), Qwen: Qwen3.5-35B-A3B offers lower standard input pricing at $0.15/1M tokens ($1/1M output), while Qwen: Qwen3.5-35B-A3B leads in cached prefix economics at $0.075/1M tokens (50% discount). MoonshotAI: Kimi K3 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 | Qwen: Qwen3.5-35B-A3B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | moonshotai/kimi-k3 | qwen/qwen3.5-35b-a3b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.45 / $15 | $0.15 / $1 | Qwen: Qwen3.5-35B-A3B |
| Cache Read Price (per 1M) | $0.225/1M (50% Off) | $0.075/1M (50% Off) | Qwen: Qwen3.5-35B-A3B |
| Context Window / Max Output | 1024K In / 922K Out | 256K In / 16K Out | MoonshotAI: Kimi K3 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MoonshotAI: Kimi K3 (-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
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model,
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 to Qwen: Qwen3.5-35B-A3B
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, Verified P90 latency of 280ms.