◉GEMINI LABJP
●PRICE — Gemini 3.6 Flash consumes about 17% fewer output tokens and costs less at $1.50 per 1M input and $7.50 per 1M output, against $9 output for 3.5 Flash●LITE — Gemini 3.5 Flash-Lite targets high-throughput work at $0.3 per million input tokens●CYBER — Gemini 3.5 Flash Cyber powers vulnerability detection and patching inside Google's CodeMender agent●GEMINI4 — Google says it has already begun its most ambitious pre-training run yet, for Gemini 4, even as 3.5 Pro slips●SUNSET — The Imagen 4 and Gemini 3 Image generation models shut down on August 17, 2026, so integrations need moving to newer stable or preview endpoints●STUDIO — Gemini Omni Flash is available in Google AI Studio for the first time, putting cost-efficient video generation and conversational editing within reach●PRICE — Gemini 3.6 Flash consumes about 17% fewer output tokens and costs less at $1.50 per 1M input and $7.50 per 1M output, against $9 output for 3.5 Flash●LITE — Gemini 3.5 Flash-Lite targets high-throughput work at $0.3 per million input tokens●CYBER — Gemini 3.5 Flash Cyber powers vulnerability detection and patching inside Google's CodeMender agent●GEMINI4 — Google says it has already begun its most ambitious pre-training run yet, for Gemini 4, even as 3.5 Pro slips●SUNSET — The Imagen 4 and Gemini 3 Image generation models shut down on August 17, 2026, so integrations need moving to newer stable or preview endpoints●STUDIO — Gemini Omni Flash is available in Google AI Studio for the first time, putting cost-efficient video generation and conversational editing within reach
TAG

gemini-3-5-flash

3 articles
← Back to all tags
Related:
production2agent1long-horizon1architecture1rag1cost-optimization1caching1gemini-embedding-21gemini1gemini-api1model-migration1
⬡ Gemini Advanced/2026-06-18Advanced

Restarting a Long Agent Run From Where It Broke — A Step-Ledger Design for Gemini 3.5 Flash Long-Horizon Tasks

Gemini 3.5 Flash is good at long-horizon tasks, but when a 40-step run dies on step 29, you usually start over. An append-only step ledger gives you resume, idempotency, and audit in one place. Here is the design with working Python and measured results.

◈ Gemini API/2026-06-13Advanced

Rebuilding a Three-Layer RAG Cache After Migrating to Gemini 3.5 Flash

When Gemini 2.0 Flash was retired, I rebuilt my RAG caching stack around 3.5 Flash. Here are the working implementations for response, semantic, and embedding caches, measured hit rates from production, and how self-managed caching divides the work with the API's Context Caching.

◈ Gemini API/2026-06-13Advanced

Where to Adopt Gemini 3.5 Flash GA First — Per-Workload Evaluation and a Staged Rollout with a Model Router

How I migrated production workloads to Gemini 3.5 Flash GA in stages: a per-workload evaluation harness, measured results, an env-based model router, and rollback design.