Tencent: Hy3 vs Meta: Llama 4 Scout: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Tencent: Hy3 (tencent/hy3) vs Meta: Llama 4 Scout (meta-llama/llama-4-scout). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Tencent: Hy3 (tencent/hy3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Tencent: Hy3: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.066/1M) 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 Tencent: Hy3 (API ID: tencent/hy3) vs Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout), Tencent: Hy3 offers lower standard input pricing at $0.132/1M tokens ($0.528/1M output), while Tencent: Hy3 leads in cached prefix economics at $0.066/1M tokens (50% discount). Tencent: Hy3 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 | Tencent: Hy3 | Meta: Llama 4 Scout | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | tencent/hy3 | meta-llama/llama-4-scout | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.132 / $0.528 | $0.18 / $0.59 | Tencent: Hy3 |
| Cache Read Price (per 1M) | $0.066/1M (50% Off) | $0.09/1M (50% Off) | Tencent: Hy3 |
| Context Window / Max Output | 256K In / 125K Out | 1280K In / 115K Out | Tencent: Hy3 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Tencent: Hy3 (-0ms) |
Hy3 is a 295B-parameter Mixture-of-Experts model from Tencent (21B active, 192 experts with top-8 routing) built for reasoning, agentic workflows, and real-world production use. It
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 Tencent: Hy3 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.