XingChen: Xing4.0 29B A4B (free) vs Meta: Llama 4 Maverick: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing XingChen: Xing4.0 29B A4B (free) (xingchen-agi/xing4.0-29b-a4b:free) vs Meta: Llama 4 Maverick (meta-llama/llama-4-maverick). Simulate cache read multipliers, context cliffs, MAU bills, and P90 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 Maverick (meta-llama/llama-4-maverick) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 4 Maverick: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0938/1M) 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 Maverick (API ID: meta-llama/llama-4-maverick), 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 4 Maverick | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | xingchen-agi/xing4.0-29b-a4b:free | meta-llama/llama-4-maverick | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0 / $0 | $0.1875 / $0.6525 | XingChen: Xing4.0 29B A4B (free) |
| Cache Read Price (per 1M) | $0/1M (50% Off) | $0.0938/1M (50% Off) | XingChen: Xing4.0 29B A4B (free) |
| Context Window / Max Output | 256K In / 230K Out | 1024K In / 16K 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 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active
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 Maverick
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active Verified P90 latency of 280ms.