MoonshotAI: Kimi K2.5 vs Qwen: Qwen3.6 35B A3B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MoonshotAI: Kimi K2.5 (moonshotai/kimi-k2.5) vs Qwen: Qwen3.6 35B A3B (qwen/qwen3.6-35b-a3b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MoonshotAI: Kimi K2.5 (moonshotai/kimi-k2.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K2.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.25/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.6 35B A3B (qwen/qwen3.6-35b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.6 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 K2.5 (API ID: moonshotai/kimi-k2.5) vs Qwen: Qwen3.6 35B A3B (API ID: qwen/qwen3.6-35b-a3b), Qwen: Qwen3.6 35B A3B offers lower standard input pricing at $0.15/1M tokens ($1/1M output), while Qwen: Qwen3.6 35B A3B leads in cached prefix economics at $0.075/1M tokens (50% discount). MoonshotAI: Kimi K2.5 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 K2.5 | Qwen: Qwen3.6 35B A3B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | moonshotai/kimi-k2.5 | qwen/qwen3.6-35b-a3b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $2.5 | $0.15 / $1 | Qwen: Qwen3.6 35B A3B |
| Cache Read Price (per 1M) | $0.25/1M (50% Off) | $0.075/1M (50% Off) | Qwen: Qwen3.6 35B A3B |
| Context Window / Max Output | 256K In / 230K Out | 256K In / 230K Out | MoonshotAI: Kimi K2.5 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MoonshotAI: Kimi K2.5 (-0ms) |
Kimi K2.5 is Moonshot AI's native multimodal model, delivering state-of-the-art visual coding capability and a self-directed agent swarm paradigm. Built on Kimi K2 with continued p
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of
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 K2.5 to Qwen: Qwen3.6 35B A3B
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of Verified P90 latency of 280ms.