Qwen: Qwen3.5-9B vs Thinking Machines: Inkling: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5-9B (qwen/qwen3.5-9b) vs Thinking Machines: Inkling (thinkingmachines/inkling). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5-9B (qwen/qwen3.5-9b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-9B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.05/1M) or route via asynchronous Batch API for 50% off standard token rates.
Thinking Machines: Inkling (thinkingmachines/inkling) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Thinking Machines: Inkling: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.5-9B (API ID: qwen/qwen3.5-9b) vs Thinking Machines: Inkling (API ID: thinkingmachines/inkling), Qwen: Qwen3.5-9B offers lower standard input pricing at $0.1/1M tokens ($0.15/1M output), while Qwen: Qwen3.5-9B leads in cached prefix economics at $0.05/1M tokens (50% discount). Qwen: Qwen3.5-9B 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: Qwen3.5-9B | Thinking Machines: Inkling | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-9b | thinkingmachines/inkling | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1 / $0.15 | $1 / $4.05 | Qwen: Qwen3.5-9B |
| Cache Read Price (per 1M) | $0.05/1M (50% Off) | $0.5/1M (50% Off) | Qwen: Qwen3.5-9B |
| Context Window / Max Output | 256K In / 32K Out | 512K In / 461K Out | Thinking Machines: Inkling |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5-9B (-0ms) |
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architectur
Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning
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: Qwen3.5-9B to Thinking Machines: Inkling
Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning Verified P90 latency of 280ms.