Google Gemini 2.0 Pro Exp vs DeepSeek-R1 (Reasoner): Live Promos, Pricing, Hidden Fees & Latency
Google Gemini 2.0 Pro Exp (google/gemini-2.0-pro-exp) — Google AI Startup Credits ($350K) & 95% 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 (0.14/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.022/1M off-peak input tokens (vs $0.66 off-peak base) with zero code changes required, plus off-peak UTC scheduling discounts.
In Etuki October 2026 FinOps telemetry comparing Google Gemini 2.0 Pro Exp (API ID: google/gemini-2.0-pro-exp) vs DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner), 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 $0.14/1M tokens (75% discount). Google Gemini 2.0 Pro Exp achieves a 395ms faster P90 time-to-first-token latency (145ms vs 540ms).
Prompts exceeding 200K tokens incur 2x tier pricing unless pinned via Google AI Studio Context Caching (75% off).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | Google Gemini 2.0 Pro Exp | DeepSeek-R1 (Reasoner) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | google/gemini-2.0-pro-exp | deepseek-reasoner | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1 / $5 | $0.55 / $2.19 | DeepSeek-R1 (Reasoner) |
| Cache Read Price (per 1M) | $0.25/1M (95% Off) | $0.14/1M (75% Off) | DeepSeek-R1 (Reasoner) |
| Context Window / Max Output | 1050K In / 128K Out | 128K In / 128K Out | Google Gemini 2.0 Pro Exp |
| P90 First-Token / Edge Latency | 145ms | 540ms | Google Gemini 2.0 Pro Exp (-395ms) |
Google Gemini 2.0 Pro Exp is Google DeepMind's native multimodal frontier model supporting up to 2M context tokens and implicit context caching.
DeepSeek-R1 (deepseek-reasoner) open-weight reinforcement-learning reasoning model ($0.55/$2.19 per 1M, $0.14/1M disk KV cache hit, 128K context window).
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 Gemini 2.0 Pro Exp to DeepSeek-R1 (Reasoner)
DeepSeek-R1 (deepseek-reasoner) open-weight reinforcement-learning reasoning model ($0.55/$2.19 per 1M, $0.14/1M disk KV cache hit, 128K context window). Verified P90 latency of 540ms.