Qwen: Qwen2.5 VL 72B Instruct vs OpenAI: o1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen2.5 VL 72B Instruct (qwen/qwen2.5-vl-72b-instruct) vs OpenAI: o1 (openai/o1). 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.
OpenAI: o1 (openai/o1) Startup Credits & Batch API 50% Off Promo
OpenAI GPT-6.1 Sol ($2/$10 base rate) qualifies for immediate 75% automatic prefix caching ($0.50/1M), 50% off 24-hour Batch API completions ($1/$5), and up to $150,000 in OpenAI/Azure Foundry credits via Microsoft for Startups Founders Hub (no VC funding required for initial tiers).
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen2.5 VL 72B Instruct (API ID: qwen/qwen2.5-vl-72b-instruct) vs OpenAI: o1 (API ID: openai/o1), Qwen: Qwen2.5 VL 72B Instruct offers lower standard input pricing at $0.8/1M tokens ($1/1M output), while Qwen: Qwen2.5 VL 72B Instruct leads in cached prefix economics at $0.4/1M 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 | OpenAI: o1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen2.5-vl-72b-instruct | openai/o1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.8 / $1 | $15 / $60 | Qwen: Qwen2.5 VL 72B Instruct |
| Cache Read Price (per 1M) | $0.4/1M (50% Off) | $7.5/1M (50% Off) | Qwen: Qwen2.5 VL 72B Instruct |
| Context Window / Max Output | 125K In / 113K Out | 195K In / 98K 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
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learn
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 OpenAI: o1
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learn Verified P90 latency of 280ms.