Qwen: Qwen3.5 Plus 2026-02-15 vs Meta: Llama 4 Scout: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5 Plus 2026-02-15 (qwen/qwen3.5-plus-02-15) vs Meta: Llama 4 Scout (meta-llama/llama-4-scout). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5 Plus 2026-02-15 (qwen/qwen3.5-plus-02-15) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5 Plus 2026-02-15: 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.
Meta: Llama 4 Scout (meta-llama/llama-4-scout) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 4 Scout: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.09/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.5 Plus 2026-02-15 (API ID: qwen/qwen3.5-plus-02-15) vs Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout), Meta: Llama 4 Scout offers lower standard input pricing at $0.18/1M tokens ($0.59/1M output), while Meta: Llama 4 Scout leads in cached prefix economics at $0.09/1M tokens (50% discount). Qwen: Qwen3.5 Plus 2026-02-15 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 Plus 2026-02-15 | Meta: Llama 4 Scout | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-plus-02-15 | meta-llama/llama-4-scout | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.26 / $1.56 | $0.18 / $0.59 | Meta: Llama 4 Scout |
| Cache Read Price (per 1M) | $0.13/1M (50% Off) | $0.09/1M (50% Off) | Meta: Llama 4 Scout |
| Context Window / Max Output | 977K In / 64K Out | 1280K In / 115K Out | Meta: Llama 4 Scout |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5 Plus 2026-02-15 (-0ms) |
The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achievin
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimo
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 Plus 2026-02-15 to Meta: Llama 4 Scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimo Verified P90 latency of 280ms.