Qwen: Qwen3 235B A22B Instruct 2507 vs OpenAI: GPT-5.4 Nano: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3 235B A22B Instruct 2507 (qwen/qwen3-235b-a22b-2507) vs OpenAI: GPT-5.4 Nano (openai/gpt-5.4-nano). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3 235B A22B Instruct 2507 (qwen/qwen3-235b-a22b-2507) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 235B A22B Instruct 2507: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.045/1M) or route via asynchronous Batch API for 50% off standard token rates.
OpenAI: GPT-5.4 Nano (openai/gpt-5.4-nano) 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 235B A22B Instruct 2507 (API ID: qwen/qwen3-235b-a22b-2507) vs OpenAI: GPT-5.4 Nano (API ID: openai/gpt-5.4-nano), Qwen: Qwen3 235B A22B Instruct 2507 offers lower standard input pricing at $0.09/1M tokens ($0.55/1M output), while Qwen: Qwen3 235B A22B Instruct 2507 leads in cached prefix economics at $0.045/1M tokens (50% discount). Qwen: Qwen3 235B A22B Instruct 2507 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 235B A22B Instruct 2507 | OpenAI: GPT-5.4 Nano | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3-235b-a22b-2507 | openai/gpt-5.4-nano | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.09 / $0.55 | $0.2 / $1.25 | Qwen: Qwen3 235B A22B Instruct 2507 |
| Cache Read Price (per 1M) | $0.045/1M (50% Off) | $0.1/1M (50% Off) | Qwen: Qwen3 235B A22B Instruct 2507 |
| Context Window / Max Output | 256K In / 16K Out | 391K In / 125K Out | OpenAI: GPT-5.4 Nano |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3 235B A22B Instruct 2507 (-0ms) |
Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pa
GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is
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 235B A22B Instruct 2507 to OpenAI: GPT-5.4 Nano
GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is Verified P90 latency of 280ms.