I've been testing Gemini 2.0 Flash since it launched in December 2024, and here's what no one's telling you: it's impressive when it works, but given Google's track record with killing products, you should build with exit strategies in mind. The only confirmed deprecation is image generation ending September 26, 2025.
The Good Parts (When They Work)
The 1-million token context window is legit. I threw a 50MB codebase at it and it actually understood the relationships between different modules. The native multimodal output is genuinely useful - it can generate diagrams while explaining code, which beats copying between ChatGPT and DALL-E.
The Live API is where things get interesting. Real-time voice conversations that don't suck, and it can actually interrupt itself when you start talking. I built a voice-controlled debugging assistant that could analyze error logs while I described the problem. Worked great until it didn't - connections would drop mid-sentence, or it'd confidently tell me my working code was broken. The WebSocket keeps alive signals randomly stop working, and you'll spend an hour debugging why your connection dies every 3 minutes before realizing it's not your code.
- Basic usage: $0.10 input, $0.40 output per 1M tokens
- Live API: costs way more - like $2-8 per 1M tokens depending on features
Pricing is competitive compared to alternatives, but the reliability issues add hidden costs through debugging time and fallback systems.
The Agent Prototypes Are Mostly Demos
Project Astra looks cool in videos but fails constantly in real environments. I tested it for identifying components in my electronics workshop - it confused resistors with capacitors about 30% of the time. The 10-minute memory thing? It forgets context randomly, especially if your connection hiccups.
Project Mariner achieved that 83.5% success rate on carefully curated benchmarks. In production? I watched it attempt to buy $500 worth of AWS credits when I asked it to check my billing. The human-in-the-loop is mandatory because this thing will absolutely wreck your accounts if you let it run free.
Jules for GitHub integration sounds promising until you realize it can't handle merge conflicts properly and tends to create more bugs than it fixes. I spent more time reviewing its PRs than just writing the code myself.
What Actually Breaks
The model randomly refuses to process images over 20MB despite claiming 100MB support - you'll get a generic "invalid input" error that tells you nothing. Context caching fails silently, so you're burning tokens at full price while thinking you're saving 75%. The Google Search grounding will confidently cite a 2019 Stack Overflow answer for a 2024 framework release. Pro tip: always verify the grounding results because it hallucinates citations like a drunk grad student.
Rate limits are aggressive - hit 15 requests per minute on the free tier and you're throttled for an hour. The error messages are about as helpful as "something went wrong, try again."
The Google Product Lifecycle Reality
Google's pattern with AI models is consistent: launch with enthusiasm, improve rapidly, then either sunset or radically change pricing. While there's no specific deprecation date for Gemini 2.0 Flash, Gemini 2.5 Flash costs around $2.50 for output - that's like 6x more expensive. The "cheaper" 2.5 Flash-Lite costs the same as 2.0 but lacks most of the useful features.
Bottom line: Gemini 2.0 is genuinely capable when it works, but building production apps on Google's AI models requires constant vigilance about product roadmaps, like betting your startup on Google Reader did. The technology is there, the execution is inconsistent, and Google's business model changes faster than you can adapt to it.
