Thinking Machines: Inkling Small vs Qwen: Qwen2.5 VL 72B Instruct: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Thinking Machines: Inkling Small (thinkingmachines/inkling-small) vs Qwen: Qwen2.5 VL 72B Instruct (qwen/qwen2.5-vl-72b-instruct). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Thinking Machines: Inkling Small (thinkingmachines/inkling-small) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Thinking Machines: Inkling Small: 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: 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.
In Etuki October 2026 FinOps telemetry comparing Thinking Machines: Inkling Small (API ID: thinkingmachines/inkling-small) vs Qwen: Qwen2.5 VL 72B Instruct (API ID: qwen/qwen2.5-vl-72b-instruct), Thinking Machines: Inkling Small offers lower standard input pricing at $0.45/1M tokens ($1.2/1M output), while Thinking Machines: Inkling Small leads in cached prefix economics at $0.225/1M tokens (50% discount). Thinking Machines: Inkling Small 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 | Thinking Machines: Inkling Small | Qwen: Qwen2.5 VL 72B Instruct | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | thinkingmachines/inkling-small | qwen/qwen2.5-vl-72b-instruct | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.45 / $1.2 | $0.8 / $1 | Thinking Machines: Inkling Small |
| Cache Read Price (per 1M) | $0.225/1M (50% Off) | $0.4/1M (50% Off) | Thinking Machines: Inkling Small |
| Context Window / Max Output | 512K In / 256K Out | 125K In / 113K Out | Thinking Machines: Inkling Small |
| P90 First-Token / Edge Latency | 280ms | 280ms | Thinking Machines: Inkling Small (-0ms) |
Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 12B active parameters out of 276B total. It is positioned as the smaller, more
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
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 Thinking Machines: Inkling Small to Qwen: Qwen2.5 VL 72B Instruct
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 Verified P90 latency of 280ms.