DeepSeek-V4 Pro vs Google: Gemma 4 26B A4B (free): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-V4 Pro (deepseek-v4-pro) vs Google: Gemma 4 26B A4B (free) (google/gemma-4-26b-a4b-it:free). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Zero-Config Disk KV Cache ($0.07/1M) & Off-Peak UTC Batch Arbitrage
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.
Google: Gemma 4 26B A4B (free) (google/gemma-4-26b-a4b-it:free) — 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 DeepSeek-V4 Pro (API ID: deepseek-v4-pro) vs Google: Gemma 4 26B A4B (free) (API ID: google/gemma-4-26b-a4b-it:free), Google: Gemma 4 26B A4B (free) offers lower standard input pricing at $0/1M tokens ($0/1M output), while Google: Gemma 4 26B A4B (free) leads in cached prefix economics at $0/1M tokens (50% discount). Google: Gemma 4 26B A4B (free) achieves a 180ms faster P90 time-to-first-token latency (280ms vs 460ms).
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-V4 Pro | Google: Gemma 4 26B A4B (free) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-v4-pro | google/gemma-4-26b-a4b-it:free | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.14 / $0.28 | $0 / $0 | Google: Gemma 4 26B A4B (free) |
| Cache Read Price (per 1M) | $0.07/1M (50% Off) | $0/1M (50% Off) | Google: Gemma 4 26B A4B (free) |
| Context Window / Max Output | 1000K In / 128K Out | 256K In / 32K Out | DeepSeek-V4 Pro |
| P90 First-Token / Edge Latency | 460ms | 280ms | Google: Gemma 4 26B A4B (free) (-180ms) |
1.6-Trillion parameter (49B active) MoE architecture with Fine-Grained Sparse Attention and $0.07/1M disk KV cache hits.
Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — deli
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-V4 Pro to Google: Gemma 4 26B A4B (free)
Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — deli Verified P90 latency of 280ms.