XingChen: Xing4.0 29B A4B (free) vs Meta: Llama 3.2 1B Instruct: 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 3.2 1B Instruct (meta-llama/llama-3.2-1b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 3.2 1B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0135/1M) 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 3.2 1B Instruct (API ID: meta-llama/llama-3.2-1b-instruct), 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). XingChen: Xing4.0 29B A4B (free) 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 | XingChen: Xing4.0 29B A4B (free) | Meta: Llama 3.2 1B Instruct | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | xingchen-agi/xing4.0-29b-a4b:free | meta-llama/llama-3.2-1b-instruct | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0 / $0 | $0.027 / $0.201 | XingChen: Xing4.0 29B A4B (free) |
| Cache Read Price (per 1M) | $0/1M (50% Off) | $0.0135/1M (50% Off) | XingChen: Xing4.0 29B A4B (free) |
| Context Window / Max Output | 256K In / 230K Out | 59K In / 53K Out | XingChen: Xing4.0 29B A4B (free) |
| P90 First-Token / Edge Latency | 280ms | 280ms | XingChen: Xing4.0 29B A4B (free) (-0ms) |
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
Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its
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 3.2 1B Instruct
Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its Verified P90 latency of 280ms.