Nano Banana 2 Lite & Gemini Omni Flash: Dev Guide (2026)

Google spent June 30, 2026 shipping two new generative media models to developers on the same day — one built to be as cheap and fast as possible, the other built to turn a still image into a directed, editable video clip. Neither is a minor point release. Nano Banana 2 Lite is a genuinely new model, and Gemini Omni Flash — Google's multimodal-input video model — reached the Gemini API for developers for the first time.¹

In one line: On June 30, 2026, Google released Nano Banana 2 Lite (gemini-3.1-flash-lite-image), its fastest, cheapest image-generation model yet, and brought Gemini Omni Flash (gemini-omni-flash-preview) — first shown at Google I/O on May 19, 2026 — to developers for the first time via the Gemini API and Google AI Studio.¹²

TL;DR

• What shipped: Nano Banana 2 Lite (image generation) and Gemini Omni Flash (video generation/editing), both live for developers as of June 30, 2026.¹
• Nano Banana 2 Lite pricing: $0.0336 per 1K-resolution (1024×1024) image standard, or $0.0168 per image on the Batch API — capped at 1K resolution only; 2K and 4K are not supported.³⁴
• Gemini Omni Flash pricing: $1.50 per 1M input tokens, $17.50 per 1M output tokens for video — an effective ~$0.10 per second of 720p video, matching Veo 3.1 Fast's 720p rate exactly.³
• Where they run: Google AI Studio, the Gemini API, and Gemini Enterprise Agent Platform; Nano Banana 2 Lite is also rolling into AI Mode in Search, the Gemini app, NotebookLM, Google Photos, Stitch, Google Flow, and Google Ads.¹
• The catch on Omni Flash: it currently caps out at 10-second clips, can't take audio references, and can't reliably process video references longer than 3 seconds — all documented limitations, not bugs.⁵
• Google's own latency claims for Nano Banana 2 Lite conflict slightly: the announcement post says 4 seconds for text-to-image; the model's own documentation says it targets sub-2-second end-to-end latency. Both are Google sources, published the same day.¹⁴
• They're designed to be chained: generate a still with Nano Banana 2 Lite, then pass it to Omni Flash as a reference image to animate it — Google shipped three demo apps built exactly this way.¹

What You'll Learn

• What Nano Banana 2 Lite actually is, and how its pricing and resolution limits compare to Nano Banana 2 and Nano Banana Pro
• Verified, source-checked pricing for every model in the current Nano Banana family
• What Gemini Omni Flash can and can't do today, per Google's own limitations list
• How to call both models with working Python — and why they use two different Gemini API calling conventions
• How Google intends the two models to be used together

What is Nano Banana 2 Lite?

Nano Banana 2 Lite is Google's newest image-generation model, model ID gemini-3.1-flash-lite-image, released June 30, 2026 as the entry-level tier of the "Nano Banana" image family.¹ Google's own model card describes it as "the efficiency specialist of the image generation family," aimed at "high-volume interactive developer use cases and real-time consumer applications," and explicitly positions it as the replacement for the original Nano Banana (gemini-2.5-flash-image).⁴

It supports interleaved generation and editing — text in, image out, or image-plus-text in, edited image out — across 14 aspect ratios, with fast multi-turn local edits like recoloring, sticker creation, and background swaps. Every image carries an always-on SynthID watermark plus C2PA content credentials.⁴ The trade-off for that speed is resolution: Nano Banana 2 Lite only generates 1K (1024×1024px) images. 2K and 4K are not supported — if you need higher resolution, you need Nano Banana 2 or Nano Banana Pro.⁴

It joins a family that already included Nano Banana 2 and Nano Banana Pro, on top of the original Nano Banana that launched as Google's image-editing breakout hit — and Google is explicitly positioning Lite as that original model's direct, cheaper replacement rather than a separate product.

There is one wrinkle worth flagging plainly: Google's own sources disagree on how fast this model actually is. The June 30 announcement blog post states Nano Banana 2 Lite "delivers text-to-image outputs in 4 seconds."¹ The model's own documentation page, updated the same day, describes a "sub-2 second end-to-end latency" target.⁴ Both are official Google sources published on the same date, and neither page explains the gap — it may reflect different measurement conditions (end-to-end request latency versus time-to-first-image, or different load conditions), but as published, the two numbers don't reconcile. Treat "a few seconds" as the honest summary until Google clarifies.

Two more small inconsistencies in Google's own materials, for the record: the model card's bolded product name reads "Nano Banana Lite" (dropping the "2"), while the blog post — the actual launch announcement — calls it "Nano Banana 2 Lite" consistently throughout, including in the page title.¹⁴ This piece follows the blog's naming since that's the primary launch source. Separately, Google's dedicated "Nano Banana image generation" developer guide — the main how-to page for the whole model family — still describes Nano Banana as "three distinct models" (Nano Banana 2, Nano Banana Pro, and legacy Nano Banana) and, as of this writing, has not been updated to add Nano Banana 2 Lite to that list or given it a dedicated code example.⁶ Everything about the model's existence, pricing, and specs is independently confirmed on its own model card and the pricing page — it's specifically the general how-to guide that hasn't caught up yet.

One more figure worth flagging: Google DeepMind runs a separate set of research-facing model cards for these same models, and their stated token limits don't match the developer API figures used throughout this piece. DeepMind's cards for both Nano Banana 2 Lite and Nano Banana 2 state, in identical wording, "a token context window of up to 1M" for input and a "4K token" image output — the same numbers for both tiers, despite Lite and Nano Banana 2 being priced and positioned as different-capability models.⁷ That's a different figure from the 65,536-input/4,096-output (Lite) and 131,072-input/32,768-output (Nano Banana 2) limits on the ai.google.dev developer model cards this piece cites for pricing and specs. The identical wording across two different-tier models suggests the DeepMind figure describes the underlying Gemini 3.1 Flash/Flash-Lite text model's general context capacity rather than an image-generation-specific request limit — so this piece uses the developer-API model cards' numbers throughout, since those are the figures that actually govern what you can send and receive when calling the image-generation endpoint.

Nano Banana 2 Lite pricing and specs

Per Google's official Gemini API pricing page:³

That $0.0336-per-image standard rate is actually cheaper than the original Nano Banana it replaces: legacy gemini-2.5-flash-image costs $0.039 per image at the same 1024×1024 resolution.³ Google's swap-it-in-for-free-upgrade pitch checks out on price, not just marketing copy.

Meet Gemini Omni Flash: Google's video model comes to developers

Gemini Omni Flash (gemini-omni-flash-preview) first appeared publicly at Google I/O on May 19, 2026, launching that day to Google AI Plus, Pro, and Ultra subscribers through the Gemini app and Google Flow, with YouTube Shorts and YouTube Create access following within the week — the model we covered as Google's "world model" for video back at launch.² Developer access — the Gemini API and Google AI Studio — didn't arrive until June 30, 2026, six weeks later.¹ That makes this the first time outside developers could call Omni Flash programmatically at all.

Google describes Omni as natively multimodal: it accepts text, image, audio, and video as combined input and generates video as output, reasoning about physics, real-world knowledge, and narrative continuity rather than just pattern-matching pixels.⁵ The standout feature for developers is conversational, stateful editing — you generate a video, then send a follow-up prompt like "make the violin invisible," and the model edits the existing clip while preserving everything you didn't mention, using a previous_interaction_id to track state without re-uploading the video.⁵

Gemini Omni Flash pricing

Standard tier: $1.50 per 1M input tokens (text/image/video/audio) and $9.00 per 1M output tokens for text, or $17.50 per 1M output tokens for video.³ Video is billed at 5,792 tokens per second of 720p output, which works out to roughly $0.10 per second — and Google's announcement post explicitly notes this matches Veo 3.1 Fast's pricing.¹ That claim checks out against Google's own Veo pricing table: Veo 3.1 Fast costs exactly $0.10 per second for 720p video with audio.³

What Gemini Omni Flash can't do yet

Google's technical documentation lists specific, current limitations, not vague caveats:⁵

• Generations are capped, and video generation time scales with duration, resolution, and API load — Google's June 30 announcement separately notes 10-second clips as the current ceiling, with longer durations "coming soon."¹
• You cannot upload audio references in the current API version.
• Video references up to 3 seconds are accepted by the API schema but "are not correctly processed by the model at this time" — an explicit admission that this input path doesn't fully work yet.
• No referencing or reasoning across multiple videos at once; no video extension or interpolation between a first and last frame; no voice editing.
• Editing uploaded videos (as opposed to videos the model generated) is not available in the European Economic Area, Switzerland, or the UK.
• System instructions, temperature, top_p, stop sequences, and negative prompts aren't supported parameters — negatives have to be written into the prompt text itself (e.g., "no dialogue").

All generated video carries SynthID watermarking, detectable programmatically even though it's invisible to viewers.⁵

The Nano Banana family, compared

Google now sells four tiers of the same underlying image technology. Pricing and specs below are all pulled directly from Google's pricing and model-card pages:³⁴⁸

⚠ Prices change frequently. The values above are for illustration only and may be out of date. Always verify current pricing directly with the provider before making cost decisions: Anthropic · OpenAI · Google Gemini · Google Vertex AI · AWS Bedrock · Azure OpenAI · Mistral · Cohere · Together AI · DeepSeek · Groq · Fireworks AI · Perplexity · xAI · Cursor · GitHub Copilot · Windsurf.

Nano Banana 2 (not new this week, but the mid-tier default) adds capabilities Lite doesn't have: 0.5K/2K/4K resolution options, Image Search Grounding that pulls in real web images and text during generation, and a larger 131,072-token input / 32,768-token output limit versus Lite's 65,536/4,096.⁸ Nano Banana Pro goes further still, with structured-output support and Google Search grounding built in, aimed at professional design work where accuracy and text rendering matter more than raw speed.³

How to call both APIs

The two models currently use different calling conventions in Google's own documentation — worth knowing before you copy-paste. Nano Banana 2 Lite follows the same pattern as every other Nano Banana model in Google's image-generation guide: the classic generate_content method, not the newer Interactions API.⁶ Swap in the Lite model ID and it works the same way:

from google import genai

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3.1-flash-lite-image",
    contents=["A minimalist product shot of a matte black mechanical keyboard on a light gray backdrop, soft studio lighting."],
)

for part in response.parts:
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        image = part.as_image()
        image.save("keyboard.png")

Gemini Omni Flash, by contrast, ships exclusively on the newer Interactions API — Google's own quickstart for it doesn't offer a generate_content path at all.⁵ Here's a text-to-video call, straight from Google's own documentation:⁵

import base64
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-omni-flash-preview",
    input="A marble rolling fast on a chain reaction style track, continuous smooth shot."
)

with open("marble.mp4", "wb") as f:
    f.write(base64.b64decode(interaction.output_video.data))

For iterative edits, pass previous_interaction_id from the first response into the next call instead of re-uploading the video — that's what lets Omni Flash preserve everything except what you explicitly change.⁵

Chaining the two models into one workflow

Google's own framing for these two launches is that they're meant to be used together, not separately: generate a still image fast and cheap with Nano Banana 2 Lite, then hand it to Gemini Omni Flash as a reference to animate.¹ Google shipped three demo apps built on exactly this pattern — "Anywhere" (turn a selfie into landmark photos, then animate them), "Space Lift" (reimagine a room, then generate a walkthrough), and "Omni product studio" (turn product stills into e-commerce video).¹ On the video side, the Interactions API's session support is what makes this practical: Google's blog says it lets you "maintain session history and context so users can stack up to three sequential edits" without re-uploading the clip each time.¹ A realistic build looks like: generate a still with Nano Banana 2 Lite, animate it with Omni Flash, then issue follow-up edit prompts against that same video session.

The Bottom Line

Neither of these launches is a headline-grabbing frontier model release — no benchmark chart, no "beats GPT-5" claim. What Google shipped on June 30 is more useful than that for anyone actually building: a genuinely cheaper, faster image model to swap into existing pipelines for free savings, and the first developer-API access to a Gemini video model built around conversational editing rather than one-shot regeneration. The pricing checks out against Google's own official tables, the Omni Flash limitations are documented rather than hidden, and the one place Google's own sources visibly disagree — Nano Banana 2 Lite's latency — is small enough not to change the calculus. If you're already on Nano Banana, the swap to Lite is close to free money; if you need video, Omni Flash is finally callable outside a demo.