XingChen: Xing4.0 29B A4B (free) vs Meta Llama 4 Scout: Live Promos, Pricing, Hidden Fees & Latency
XingChen: Xing4.0 29B A4B (free) (xingchen-agi/xing4.0-29b-a4b:free) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for XingChen: Xing4.0 29B A4B (free): Activate ephemeral prefix caching to slash input costs by 50% (down to $0/1M) or route via asynchronous Batch API for 50% off standard token rates.
Meta Llama 4 Scout (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 $0.225/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing XingChen: Xing4.0 29B A4B (free) (API ID: xingchen-agi/xing4.0-29b-a4b:free) vs Meta Llama 4 Scout (API ID: llama-4-scout), XingChen: Xing4.0 29B A4B (free) offers lower standard input pricing at $0/1M tokens ($0/1M output), while XingChen: Xing4.0 29B A4B (free) leads in cached prefix economics at $0/1M tokens (50% discount). Meta Llama 4 Scout achieves a 140ms faster P90 time-to-first-token latency (140ms vs 280ms).
Auto-discovered via live global telemetry registry.
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | XingChen: Xing4.0 29B A4B (free) | Meta Llama 4 Scout | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | xingchen-agi/xing4.0-29b-a4b:free | llama-4-scout | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0 / $0 | $0.45 / $0.65 | XingChen: Xing4.0 29B A4B (free) |
| Cache Read Price (per 1M) | $0/1M (50% Off) | $0.225/1M (50% Off) | XingChen: Xing4.0 29B A4B (free) |
| Context Window / Max Output | 256K In / 230K Out | 400K In / 128K Out | XingChen: Xing4.0 29B A4B (free) |
| P90 First-Token / Edge Latency | 280ms | 140ms | Meta Llama 4 Scout (-140ms) |
Xing4.0-29B-A4B is an open-weight mixture-of-experts language model from China Telecom's AI team (TeleAI) and the successor to the TeleChat series. It has 29B total parameters with
Meta open-weight multimodal MoE model supporting 10M+ extended context on dedicated clusters.
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 XingChen: Xing4.0 29B A4B (free) to Meta Llama 4 Scout
Meta open-weight multimodal MoE model supporting 10M+ extended context on dedicated clusters. Verified P90 latency of 140ms.