OpenAI: o1-pro vs Qwen: Qwen3.8 2.4T A95B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing OpenAI: o1-pro (openai/o1-pro) vs Qwen: Qwen3.8 2.4T A95B (qwen/qwen3.8-2.4t-a95b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
OpenAI: o1-pro (openai/o1-pro) 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: Qwen3.8 2.4T A95B (qwen/qwen3.8-2.4t-a95b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.8 2.4T A95B: Activate ephemeral prefix caching to slash input costs by 50% (down to $1/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing OpenAI: o1-pro (API ID: openai/o1-pro) vs Qwen: Qwen3.8 2.4T A95B (API ID: qwen/qwen3.8-2.4t-a95b), Qwen: Qwen3.8 2.4T A95B offers lower standard input pricing at $2/1M tokens ($6/1M output), while Qwen: Qwen3.8 2.4T A95B leads in cached prefix economics at $1/1M tokens (50% discount). OpenAI: o1-pro 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-pro | Qwen: Qwen3.8 2.4T A95B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | openai/o1-pro | qwen/qwen3.8-2.4t-a95b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $150 / $600 | $2 / $6 | Qwen: Qwen3.8 2.4T A95B |
| Cache Read Price (per 1M) | $75/1M (50% Off) | $1/1M (50% Off) | Qwen: Qwen3.8 2.4T A95B |
| Context Window / Max Output | 195K In / 98K Out | 1024K In / 128K Out | Qwen: Qwen3.8 2.4T A95B |
| P90 First-Token / Edge Latency | 280ms | 280ms | OpenAI: o1-pro (-0ms) |
The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and pr
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out
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-pro to Qwen: Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out Verified P90 latency of 280ms.