Gemini

The current Gemini model family: Gemini 2.5 Pro (complex reasoning, 1M context, Deep Think mode, 84% MMMU, 54% SWE-Bench); Gemini 2.5 Flash (balanced quality/cost, 20-30% fewer output tokens, 1M context); Gemini 2.5 Flash-Lite (highest throughput, lowest cost); Gemini 3 Flash (Dec 2025, strong agentic capabilities); Gemini 3.1 Pro (Feb 2026, 1M context, preview in Vertex AI). The 2.5 Flash model is the current default across Google products. All models support thinking mode with adjustable thinking budgets.

Thinking mode allows Gemini to reason through a problem before generating a final response — similar to extended thinking in Claude or o3's reasoning. Gemini's unique feature is the adjustable thinking budget: you set a token budget for thinking (e.g., 8,192 thinking tokens for complex tasks, 0 for simple ones). This gives you direct control over the depth of reasoning and associated cost. Deep Think is the maximum thinking depth, optimized for math, competitive coding, and complex analytical tasks.

1M tokens (~750,000 words) can hold: an entire medium-sized codebase, a library of 50 research papers, 5 hours of transcribed conversation, or 20 full-length novels. In practice: upload the entire document corpus, ask cross-document questions, and Gemini reasons over the full context without fragmentation. Cost scales with tokens used — sending 1M tokens in every call is expensive; the key is identifying tasks where the 1M context quality advantage justifies the cost over chunked retrieval approaches.

Gemini API (ai.google.dev): free tier, fast setup, less enterprise governance, no VPC controls, no enterprise SLAs. Vertex AI Gemini: IAM-based access control, VPC Service Controls for network isolation, Cloud Audit Logs for compliance, enterprise SLAs, model version pinning, fine-tuning support, and integration with Google Cloud's data services. For production enterprise applications, always use Vertex AI. For prototyping and personal projects, the public API is fine.

Gemini API pricing as of 2026 (Vertex AI): Gemini 2.5 Pro (~$1.25/1M input tokens ≤200K, $2.50/1M above; $10/1M output); Gemini 2.5 Flash (~$0.15/1M input, $0.60/1M output); Flash-Lite (lowest cost tier). Thinking tokens are billed separately. Vertex AI has Google Cloud free tier credits. Share your expected token volume, model tier, and thinking budget usage and we'll provide cost projections for your integration.

Gemini Deep Research is an agentic capability where Gemini autonomously plans a research strategy, searches the web across multiple queries, reads and synthesizes sources, and generates a comprehensive research report. It's powered by Gemini 2.5 Flash and available in Google One AI Premium. For developers, Deep Research integrates with Vertex AI Agent Builder — you can trigger deep research workflows via API as part of agentic applications.

Firebase AI Logic (formerly Vertex AI in Firebase) provides a client-side SDK for Android, iOS, and web that calls Gemini directly from mobile and web apps. Authentication uses Firebase Auth tokens — no server-side API key exposure. Multimodal inputs from device camera work natively. Firestore stores conversation history and user context. App Check prevents API abuse. The SDK handles retry logic and streaming. We use Firebase AI Logic for consumer-facing mobile AI features where server-side latency is a concern.

Yes — Gemini is natively multimodal and processes video (up to 1 hour in the 1M context window), audio files, and images alongside text in unified prompts. Video understanding includes scene analysis, object identification, action recognition, and transcript generation. Audio processing handles speech recognition, speaker identification, and audio content analysis. Gemini 3.x models add native audio output — text-to-speech as part of the model response without a separate TTS API.

Gemini RAG architecture options: (1) Direct context ingestion — for document collections under 1M tokens, pass all documents directly to Gemini without a vector database (simpler, often better quality); (2) Traditional RAG — embed with text-embedding-004 (Google's model), store in Vertex AI Vector Search, retrieve top-k chunks, pass to Gemini for synthesis; (3) Vertex AI RAG Engine — managed RAG pipeline that handles chunking, embedding, retrieval, and Gemini grounding automatically. We choose based on corpus size and update frequency.

We offer Gemini managed support including model version upgrade planning (Gemini model versions deprecate on a schedule), thinking budget tuning as usage patterns evolve, Vertex AI quota and rate limit management, cost optimization reviews, multimodal pipeline maintenance, and Firebase AI Logic SDK updates for mobile clients. We also provide team training on thinking mode design, 1M context architecture patterns, and Vertex AI deployment best practices.