Relace: Relace Apply 3 vs Google: Gemini 2.5 Pro (batch): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Relace: Relace Apply 3 (relace/relace-apply-3) vs Google: Gemini 2.5 Pro (batch) (google/gemini-2.5-pro:batch). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Relace: Relace Apply 3 (relace/relace-apply-3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Relace: Relace Apply 3: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.425/1M) or route via asynchronous Batch API for 50% off standard token rates.
Google: Gemini 2.5 Pro (batch) (google/gemini-2.5-pro:batch) — Google AI Startup Credits ($350K) & 50% Cache Promo
Google DeepMind is currently subsidizing Gemini 4 Argon at 50% off standard rates ($2.00/1M input & $10.00/1M output instead of $4/$20) until November 30, 2026, alongside 95% off Context Caching ($0.10/1M) and up to $350,000 in Google for Startups Cloud/Vertex AI credits.
In Etuki October 2026 FinOps telemetry comparing Relace: Relace Apply 3 (API ID: relace/relace-apply-3) vs Google: Gemini 2.5 Pro (batch) (API ID: google/gemini-2.5-pro:batch), Google: Gemini 2.5 Pro (batch) offers lower standard input pricing at $0.625/1M tokens ($5/1M output), while Google: Gemini 2.5 Pro (batch) leads in cached prefix economics at $0.3125/1M tokens (50% discount). Relace: Relace Apply 3 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 | Relace: Relace Apply 3 | Google: Gemini 2.5 Pro (batch) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | relace/relace-apply-3 | google/gemini-2.5-pro:batch | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.85 / $1.25 | $0.625 / $5 | Google: Gemini 2.5 Pro (batch) |
| Cache Read Price (per 1M) | $0.425/1M (50% Off) | $0.3125/1M (50% Off) | Google: Gemini 2.5 Pro (batch) |
| Context Window / Max Output | 250K In / 125K Out | 1024K In / 64K Out | Relace: Relace Apply 3 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Relace: Relace Apply 3 (-0ms) |
Relace Apply 3 is a specialized code-patching LLM that merges AI-suggested edits straight into your source files. It can apply updates from GPT-4o, Claude, and others into your fil
Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to
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 Relace: Relace Apply 3 to Google: Gemini 2.5 Pro (batch)
Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to Verified P90 latency of 280ms.