LG EXAONE Exaone 3.5 7.8B Instruct vs Google Gemini 3.1 Pro: Live Promos, Pricing, Hidden Fees & Latency
LG EXAONE Exaone 3.5 7.8B Instruct (lgai-exaone/exaone-3.5-7.8b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for LG EXAONE Exaone 3.5 7.8B Instruct: Activate ephemeral prefix caching to slash input costs by 90% (down to 0.004/1M) or route via asynchronous Batch API for 50% off standard token rates.
Google Gemini 3.1 Pro (gemini-3.1-pro) — Google AI Startup Credits ($350K) & 90% 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 LG EXAONE Exaone 3.5 7.8B Instruct (API ID: lgai-exaone/exaone-3.5-7.8b-instruct) vs Google Gemini 3.1 Pro (API ID: gemini-3.1-pro), LG EXAONE Exaone 3.5 7.8B Instruct offers lower standard input pricing at $0.04/1M tokens ($0.12/1M output), while LG EXAONE Exaone 3.5 7.8B Instruct leads in cached prefix economics at $0.004/1M tokens (90% discount). LG EXAONE Exaone 3.5 7.8B Instruct achieves a 95ms faster P90 time-to-first-token latency (195ms vs 290ms).
Gateway-synced rate ($0.04/$0.12 per 1M, flat 128K window). Verify upstream provider cache TTL and regional routing before high-volume batch runs.
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | LG EXAONE Exaone 3.5 7.8B Instruct | Google Gemini 3.1 Pro | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | lgai-exaone/exaone-3.5-7.8b-instruct | gemini-3.1-pro | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.04 / $0.12 | $2 / $12 | LG EXAONE Exaone 3.5 7.8B Instruct |
| Cache Read Price (per 1M) | $0.004/1M (90% Off) | $0.2/1M (90% Off) | LG EXAONE Exaone 3.5 7.8B Instruct |
| Context Window / Max Output | 128K In / 64K Out | 2000K In / 128K Out | Google Gemini 3.1 Pro |
| P90 First-Token / Edge Latency | 195ms | 290ms | LG EXAONE Exaone 3.5 7.8B Instruct (-95ms) |
LG EXAONE Exaone 3.5 7.8B Instruct (lgai-exaone/exaone-3.5-7.8b-instruct): OpenRouter gateway-synced model at $0.04/1M input, $0.12/1M output, $0.004/1M cached input (128K context window).
Google 2M context multimodal model (<=200K: $2.00/$12.00, $0.20 cache; >200K cliff: $4.00/$18.00, 180K soft-block).
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 LG EXAONE Exaone 3.5 7.8B Instruct to Google Gemini 3.1 Pro
Google 2M context multimodal model (<=200K: $2.00/$12.00, $0.20 cache; >200K cliff: $4.00/$18.00, 180K soft-block). Verified P90 latency of 290ms.