Qwen: Qwen3.8 2.4T A95B vs OpenAI: o1-pro: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.8 2.4T A95B (qwen/qwen3.8-2.4t-a95b) vs OpenAI: o1-pro (openai/o1-pro). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
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).
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.8 2.4T A95B (API ID: qwen/qwen3.8-2.4t-a95b) vs OpenAI: o1-pro (API ID: openai/o1-pro), 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). Qwen: Qwen3.8 2.4T A95B 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: Qwen3.8 2.4T A95B | OpenAI: o1-pro | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.8-2.4t-a95b | openai/o1-pro | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $2 / $6 | $150 / $600 | Qwen: Qwen3.8 2.4T A95B |
| Cache Read Price (per 1M) | $1/1M (50% Off) | $75/1M (50% Off) | Qwen: Qwen3.8 2.4T A95B |
| Context Window / Max Output | 1024K In / 128K Out | 195K In / 98K Out | Qwen: Qwen3.8 2.4T A95B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.8 2.4T A95B (-0ms) |
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
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
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: Qwen3.8 2.4T A95B to OpenAI: o1-pro
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 Verified P90 latency of 280ms.