Meta: Llama 4 Scout vs SpaceXAI: Grok Build 0.1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Llama 4 Scout (meta-llama/llama-4-scout) vs SpaceXAI: Grok Build 0.1 (x-ai/grok-build-0.1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
SpaceXAI: Grok Build 0.1 (x-ai/grok-build-0.1) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for SpaceXAI: Grok Build 0.1: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout) vs SpaceXAI: Grok Build 0.1 (API ID: x-ai/grok-build-0.1), Meta: Llama 4 Scout offers lower standard input pricing at $0.18/1M tokens ($0.59/1M output), while Meta: Llama 4 Scout leads in cached prefix economics at $0.09/1M tokens (50% discount). Meta: Llama 4 Scout 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 | Meta: Llama 4 Scout | SpaceXAI: Grok Build 0.1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta-llama/llama-4-scout | x-ai/grok-build-0.1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.18 / $0.59 | $1 / $2 | Meta: Llama 4 Scout |
| Cache Read Price (per 1M) | $0.09/1M (50% Off) | $0.5/1M (50% Off) | Meta: Llama 4 Scout |
| Context Window / Max Output | 1280K In / 115K Out | 250K In / 225K Out | SpaceXAI: Grok Build 0.1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Llama 4 Scout (-0ms) |
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
Grok Build 0.1 is SpaceXAI’s fast coding model trained specifically for agentic software engineering workflows. It supports text and image inputs with text output, and is optimized
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 Meta: Llama 4 Scout to SpaceXAI: Grok Build 0.1
Grok Build 0.1 is SpaceXAI’s fast coding model trained specifically for agentic software engineering workflows. It supports text and image inputs with text output, and is optimized Verified P90 latency of 280ms.