Gemini (Google)

Gemini is Google DeepMind's family of multimodal large language models, launched in December 2023 to consolidate Google's previously fragmented AI portfolio (PaLM, LaMDA, Bard, Imagen) under a single model family. By 2026, Gemini has become the AI engine inside Google Search (AI Overviews), Workspace (Gmail, Docs, Sheets, Slides), Android, and the standalone Gemini consumer app — putting it in front of more users than any other LLM.

Gemini was the first frontier model trained natively on multiple modalities (text, image, audio, video, code) from scratch rather than bolting modalities onto a text base. This native-multimodal approach delivers tighter cross-modal grounding — the model "thinks" across modalities rather than translating each one into text.

The Gemini model family

In 2026, Gemini ships in tiers tuned for different cost/latency/quality points:

• Gemini 2.5 Pro — Frontier model; 1M+ token context; strongest multimodal reasoning.
• Gemini 2.5 Flash — High-volume workhorse; ~10x cheaper than Pro; sub-second latency for short outputs. Used by PostKit.
• Gemini 2.5 Flash-Lite — Cheapest tier; favored for classification, extraction, and simple generation at massive scale.
• Gemini Nano — Runs on-device on Pixel phones; powers Pixel-exclusive AI features without network round-trips.

Pricing in 2026: Pro ~$1.25/$10 per M input/output tokens; Flash ~$0.075/$0.30; Flash-Lite ~$0.04/$0.15. Generous free tiers via AI Studio.

What Gemini does well

Gemini's strengths cluster around four areas:

• Native multimodal grounding — Best-in-class for tasks like "describe what's happening in this video" or "extract data from this scanned form."
• Long context with strong recall — 1M+ tokens with high recall accuracy across the full window (no severe "lost in the middle" failure).
• Google ecosystem integration — Direct access to Search results, Workspace data, YouTube transcripts, and Maps — a moat no other LLM can match.
• Cost/latency at the Flash tier — Flash hits a quality/speed/cost balance that's hard to beat for production short-form generation.

A 2026 LMSYS Chatbot Arena leaderboard placed Gemini 2.5 Pro within 30 ELO points of the top frontier models for multimodal tasks; Flash dominates the cost-quality Pareto frontier for sub-second responses.

Examples of Gemini in production

1. Google Search AI Overviews — Gemini-generated summaries appearing on 48% of Google searches in early 2026 (BrightEdge data).
2. Gmail "Help me write" — Gemini drafts replies and longer emails inside Gmail.
3. Google Docs / Slides — "Help me create" generates outlines, slides, and visuals via Gemini + Imagen 3.
4. NotebookLM — Source-grounded research assistant; Gemini does the synthesis with RAG over user-uploaded documents.
5. PostKit — Uses Gemini Flash 3 for script generation and image-prompt engineering.

How PostKit uses Gemini

PostKit uses Gemini Flash 3 for both LLM steps in its three-step pipeline:

1. Script + Image Briefs — One Gemini Flash 3 call ingests the brand profile, platform rules, and chosen marketing pipeline (PAS, AIDA, POV Hook, etc.) and emits structured JSON: a week of platform-specific posts with captions, slides, hashtags, and image briefs.
2. Image Prompt Engineering — A second Gemini Flash 3 call rewrites each image brief as a prompt-engineered Imagen 3 input with photographic style, lighting, lens, and composition language.

Why Flash and not Pro? For tightly-scoped structured generation following a known schema, Flash matches Pro's quality at 10–15x the speed and ~15x lower cost. PostKit's task is well-defined enough that Pro's extra reasoning headroom doesn't translate to better captions — but the cost difference is decisive at scale.

The Google ecosystem fit also matters: Gemini Flash + Imagen 3 live in the same Vertex AI project, share authentication, and run in the same data centers — minimizing latency and operational complexity.

Frequently asked questions

Was Gemini formerly called Bard? Yes. Google rebranded Bard to Gemini in February 2024 to align consumer product naming with the underlying model.

Is Gemini better than GPT-5 or Claude? Different strengths. Gemini leads on multimodal grounding, Google ecosystem integration, and cost at the Flash tier. GPT-5 leads on creative writing and consumer brand. Claude leads on code and long-document reasoning.

What is Gemini Live? Real-time multimodal voice + camera conversation — Gemini sees what your phone camera sees and converses naturally. Available in the Gemini app on Android and iOS in 2026.

Can Gemini generate images and video? Yes. Gemini orchestrates Imagen 3 for images and Veo 3 for video. Output is prompted via natural language inside the Gemini app.

Does Gemini support RAG? Yes. Vertex AI Search and NotebookLM provide turn-key RAG over user-supplied documents with Gemini as the synthesis model.

Is Gemini open-source? No. Gemini weights are proprietary. Google does release "Gemma" — a smaller open-weights family related to Gemini's architecture — for self-hosting.

What's Gemini's context window? 1M tokens for Pro and Flash; 32k for Nano. Pro can be requested up to 2M tokens in select preview tiers.

Related terms

• LLM (Large Language Model)
• GPT-4 / GPT-5
• Claude (Anthropic)
• Multimodal AI
• Generative AI
• Imagen 3
• AI Overviews
• Prompt engineering

Sources

• Google DeepMind — Gemini Technical Report (2023, 2025)
• LMSYS Chatbot Arena Leaderboard (2026)
• BrightEdge — AI Overviews Adoption Tracker 2026