DeepSeek-R1 (Reasoner) vs SpaceXAI: Grok 4.7: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-R1 (Reasoner) (deepseek-reasoner) vs SpaceXAI: Grok 4.7 (x-ai/grok-4.7). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek-R1 (Reasoner) (deepseek-reasoner) — Automatic Disk KV Cache ($0.275/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
SpaceXAI: Grok 4.7 (x-ai/grok-4.7) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for SpaceXAI: Grok 4.7: Activate ephemeral prefix caching to slash input costs by 50% (down to $1/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner) vs SpaceXAI: Grok 4.7 (API ID: x-ai/grok-4.7), DeepSeek-R1 (Reasoner) offers lower standard input pricing at $0.55/1M tokens ($2.19/1M output), while DeepSeek-R1 (Reasoner) leads in cached prefix economics at $0.275/1M tokens (50% discount). SpaceXAI: Grok 4.7 achieves a 260ms faster P90 time-to-first-token latency (280ms vs 540ms).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | DeepSeek-R1 (Reasoner) | SpaceXAI: Grok 4.7 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-reasoner | x-ai/grok-4.7 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.19 | $2 / $6 | DeepSeek-R1 (Reasoner) |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $1/1M (50% Off) | DeepSeek-R1 (Reasoner) |
| Context Window / Max Output | 400K In / 128K Out | 488K In / 439K Out | SpaceXAI: Grok 4.7 |
| P90 First-Token / Edge Latency | 540ms | 280ms | SpaceXAI: Grok 4.7 (-260ms) |
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
Grok 4.7 is SpaceXAI's flagship model for coding, agentic tasks, and knowledge work, succeeding Grok 4.6. It is particularly strong at long-running software engineering tasks, veri
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 DeepSeek-R1 (Reasoner) to SpaceXAI: Grok 4.7
Grok 4.7 is SpaceXAI's flagship model for coding, agentic tasks, and knowledge work, succeeding Grok 4.6. It is particularly strong at long-running software engineering tasks, veri Verified P90 latency of 280ms.