OpenAI: o1 vs Qwen: Qwen-Plus: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing OpenAI: o1 (openai/o1) vs Qwen: Qwen-Plus (qwen/qwen-plus). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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).
Qwen: Qwen-Plus (qwen/qwen-plus) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen-Plus: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.13/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing OpenAI: o1 (API ID: openai/o1) vs Qwen: Qwen-Plus (API ID: qwen/qwen-plus), Qwen: Qwen-Plus offers lower standard input pricing at $0.26/1M tokens ($0.78/1M output), while Qwen: Qwen-Plus leads in cached prefix economics at $0.13/1M tokens (50% discount). OpenAI: o1 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 | OpenAI: o1 | Qwen: Qwen-Plus | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | openai/o1 | qwen/qwen-plus | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $15 / $60 | $0.26 / $0.78 | Qwen: Qwen-Plus |
| Cache Read Price (per 1M) | $7.5/1M (50% Off) | $0.13/1M (50% Off) | Qwen: Qwen-Plus |
| Context Window / Max Output | 195K In / 98K Out | 977K In / 32K Out | OpenAI: o1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | OpenAI: o1 (-0ms) |
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
Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.
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 OpenAI: o1 to Qwen: Qwen-Plus
Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination. Verified P90 latency of 280ms.