OpenAI GPT-6 Luna vs Microsoft: Phi 4: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing OpenAI GPT-6 Luna (gpt-6-luna) vs Microsoft: Phi 4 (microsoft/phi-4). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
OpenAI GPT-6 Luna (gpt-6-luna) Startup Credits & Batch API 50% Off Promo
OpenAI GPT-6.1 Sol ($2/$10 base rate) qualifies for immediate 75% automatic prefix caching ($0.50/1M), 50% off 24-hour Batch API completions ($1/$5), and up to $150,000 in OpenAI/Azure Foundry credits via Microsoft for Startups Founders Hub (no VC funding required for initial tiers).
Microsoft: Phi 4 (microsoft/phi-4) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Microsoft: Phi 4: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.035/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing OpenAI GPT-6 Luna (API ID: gpt-6-luna) vs Microsoft: Phi 4 (API ID: microsoft/phi-4), Microsoft: Phi 4 offers lower standard input pricing at $0.07/1M tokens ($0.14/1M output), while OpenAI GPT-6 Luna leads in cached prefix economics at $0.025/1M tokens (75% discount). OpenAI GPT-6 Luna achieves a 135ms faster P90 time-to-first-token latency (145ms vs 280ms).
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 | OpenAI GPT-6 Luna | Microsoft: Phi 4 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | gpt-6-luna | microsoft/phi-4 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1 / $0.5 | $0.07 / $0.14 | Microsoft: Phi 4 |
| Cache Read Price (per 1M) | $0.025/1M (75% Off) | $0.035/1M (50% Off) | OpenAI GPT-6 Luna |
| Context Window / Max Output | 1000K In / 64K Out | 16K In / 14K Out | OpenAI GPT-6 Luna |
| P90 First-Token / Edge Latency | 145ms | 280ms | OpenAI GPT-6 Luna (-135ms) |
OpenAI ultra-affordable reasoning and logic model with 1M+ token context window for high-volume production pipelines.
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses
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 OpenAI GPT-6 Luna to Microsoft: Phi 4
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses Verified P90 latency of 280ms.