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Google just dropped Gemini 3.5 Flash — a frontier-grade multimodal model with over 1 million tokens of context, built-in Python code execution, and pricing so aggressive it’s hard to believe. In this video, I break down what it actually is, how it compares to GPT-4o and Claude Opus/Sonnet, and walk through real demos for text, code, and image understanding so you can decide if it belongs in your stack.
We cover the Mixture-of-Experts architecture, benchmark scores on Terminal-bench and agentic workflows, the four pricing tiers (Standard, Batch, Flex, Priority), the free tier, the OpenAI-compatible API migration trick, and the optimization tips Google itself recommends.
If you’re a developer, AI builder, or creator trying to figure out which model deserves a slot in your toolkit in 2026 — this is the deep-dive for you.
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⏱️ CHAPTERS
00:00 Why Gemini 3.5 Flash Changes the Game
01:05 Dzine AI
02:16 What Gemini 3.5 Flash Actually Is
03:35 The Benchmarks That Made Me a Believer
04:55 How It Stacks Up Against GPT and Claude
06:25 Pricing — Where Gemini Quietly Wins
07:56 Getting Started — From Zero to First Response
08:58 Demo 1: Text and Reasoning
10:00 Demo 2: Built-In Python Code Execution
10:50 Demo 3: Image Understanding + Multimodal
11:37 Pro Tips From My Testing
12:43 Where Gemini 3.5 Flash Belongs in Your Stack
💬 Drop a comment: What would YOU build with a million-token context window?
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#gemini #geminiflash #googleai #aitools #llm #gpt4o #claudeai #aicomparison



