Qwen: Qwen2.5 VL 72B Instruct vs MoonshotAI: Kimi K2 0905: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen2.5 VL 72B Instruct (qwen/qwen2.5-vl-72b-instruct) vs MoonshotAI: Kimi K2 0905 (moonshotai/kimi-k2-0905). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen2.5 VL 72B Instruct (qwen/qwen2.5-vl-72b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen2.5 VL 72B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.4/1M) or route via asynchronous Batch API for 50% off standard token rates.
MoonshotAI: Kimi K2 0905 (moonshotai/kimi-k2-0905) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K2 0905: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.3/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen2.5 VL 72B Instruct (API ID: qwen/qwen2.5-vl-72b-instruct) vs MoonshotAI: Kimi K2 0905 (API ID: moonshotai/kimi-k2-0905), MoonshotAI: Kimi K2 0905 offers lower standard input pricing at $0.6/1M tokens ($2.5/1M output), while MoonshotAI: Kimi K2 0905 leads in cached prefix economics at $0.3/1M tokens (50% discount). Qwen: Qwen2.5 VL 72B Instruct 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 | Qwen: Qwen2.5 VL 72B Instruct | MoonshotAI: Kimi K2 0905 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen2.5-vl-72b-instruct | moonshotai/kimi-k2-0905 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.8 / $1 | $0.6 / $2.5 | MoonshotAI: Kimi K2 0905 |
| Cache Read Price (per 1M) | $0.4/1M (50% Off) | $0.3/1M (50% Off) | MoonshotAI: Kimi K2 0905 |
| Context Window / Max Output | 125K In / 113K Out | 256K In / 96K Out | Qwen: Qwen2.5 VL 72B Instruct |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen2.5 VL 72B Instruct (-0ms) |
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts
Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trilli
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 Qwen: Qwen2.5 VL 72B Instruct to MoonshotAI: Kimi K2 0905
Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trilli Verified P90 latency of 280ms.