Qwen: Qwen3.5 397B A17B vs OpenAI: GPT-4.1 Mini (batch): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5 397B A17B (qwen/qwen3.5-397b-a17b) vs OpenAI: GPT-4.1 Mini (batch) (openai/gpt-4.1-mini:batch). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5 397B A17B (qwen/qwen3.5-397b-a17b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5 397B A17B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.25/1M) or route via asynchronous Batch API for 50% off standard token rates.
OpenAI: GPT-4.1 Mini (batch) (openai/gpt-4.1-mini:batch) 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: Qwen3.5 397B A17B (API ID: qwen/qwen3.5-397b-a17b) vs OpenAI: GPT-4.1 Mini (batch) (API ID: openai/gpt-4.1-mini:batch), OpenAI: GPT-4.1 Mini (batch) offers lower standard input pricing at $0.2/1M tokens ($0.8/1M output), while OpenAI: GPT-4.1 Mini (batch) leads in cached prefix economics at $0.1/1M tokens (50% discount). Qwen: Qwen3.5 397B A17B 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 397B A17B | OpenAI: GPT-4.1 Mini (batch) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-397b-a17b | openai/gpt-4.1-mini:batch | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $3.6 | $0.2 / $0.8 | OpenAI: GPT-4.1 Mini (batch) |
| Cache Read Price (per 1M) | $0.25/1M (50% Off) | $0.1/1M (50% Off) | OpenAI: GPT-4.1 Mini (batch) |
| Context Window / Max Output | 256K In / 230K Out | 1023K In / 32K Out | Qwen: Qwen3.5 397B A17B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5 397B A17B (-0ms) |
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, ach
GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.
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 397B A17B to OpenAI: GPT-4.1 Mini (batch)
GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45. Verified P90 latency of 280ms.