Qwen: Qwen3.5-35B-A3B vs Meta: Llama 4 Scout: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b) vs Meta: Llama 4 Scout (meta-llama/llama-4-scout). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-35B-A3B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.075/1M) 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: Qwen3.5-35B-A3B (API ID: qwen/qwen3.5-35b-a3b) vs Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout), Qwen: Qwen3.5-35B-A3B offers lower standard input pricing at $0.15/1M tokens ($1/1M output), while Qwen: Qwen3.5-35B-A3B leads in cached prefix economics at $0.075/1M tokens (50% discount). Qwen: Qwen3.5-35B-A3B 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: Qwen3.5-35B-A3B | Meta: Llama 4 Scout | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-35b-a3b | meta-llama/llama-4-scout | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.15 / $1 | $0.18 / $0.59 | Qwen: Qwen3.5-35B-A3B |
| Cache Read Price (per 1M) | $0.075/1M (50% Off) | $0.09/1M (50% Off) | Qwen: Qwen3.5-35B-A3B |
| Context Window / Max Output | 256K In / 16K Out | 1280K In / 115K Out | Meta: Llama 4 Scout |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5-35B-A3B (-0ms) |
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model,
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: Qwen3.5-35B-A3B 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.