Google: Gemma 4 26B A4B vs DeepSeek-R1 (Reasoner): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Google: Gemma 4 26B A4B (google/gemma-4-26b-a4b-it) vs DeepSeek-R1 (Reasoner) (deepseek-reasoner). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Google: Gemma 4 26B A4B (google/gemma-4-26b-a4b-it) — 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.
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.
In Etuki October 2026 FinOps telemetry comparing Google: Gemma 4 26B A4B (API ID: google/gemma-4-26b-a4b-it) vs DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner), Google: Gemma 4 26B A4B offers lower standard input pricing at $0.09/1M tokens ($0.3/1M output), while Google: Gemma 4 26B A4B leads in cached prefix economics at $0.045/1M tokens (50% discount). Google: Gemma 4 26B A4B achieves a 260ms faster P90 time-to-first-token latency (280ms vs 540ms).
Auto-discovered via live global telemetry registry.
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | Google: Gemma 4 26B A4B | DeepSeek-R1 (Reasoner) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | google/gemma-4-26b-a4b-it | deepseek-reasoner | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.09 / $0.3 | $0.55 / $2.19 | Google: Gemma 4 26B A4B |
| Cache Read Price (per 1M) | $0.045/1M (50% Off) | $0.275/1M (50% Off) | Google: Gemma 4 26B A4B |
| Context Window / Max Output | 256K In / 230K Out | 400K In / 128K Out | Google: Gemma 4 26B A4B |
| P90 First-Token / Edge Latency | 280ms | 540ms | Google: Gemma 4 26B A4B (-260ms) |
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
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
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 Google: Gemma 4 26B A4B to DeepSeek-R1 (Reasoner)
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis. Verified P90 latency of 540ms.