Google releases Nano Banana 2.1, halving image-output token prices while raising input and text rates

• Model ID: gemini-nano-banana-2.1, stable (model page).
• Inputs and outputs: text, image, video and PDF in; image and text out. Limits are 131,072 input tokens and 32,768 output tokens.
• Resolution: 1K (default), 2K or 4K.
• Thinking: minimal, medium or high.
• Other features: up to 14 reference images, grounding in Google Web and Image Search, and the Batch API.
• Not supported: function calling, structured outputs, caching, code execution, Live API.
• Where to use it: Gemini app, AI Mode, AI Studio, Flow, Stitch, Google Ads and Gemini Enterprise (The Decoder).

• Base model: a natively multimodal reasoning model built on Gemini 3.6 Flash. Knowledge cutoff is March 2026, though some domains stop at January 2025 (model card). The API docs call it an update to Gemini 3.1 Flash Image instead, a lineage discrepancy that RuntimeWire points out.
• Evaluation method: side-by-side human preference tests (Elo scores) plus an automated factuality score from an internal rater. Google has released no prompt sets, rater protocols or seeds, so the results cannot be reproduced.
• Thinking matters a lot: it is the largest lever in Google's table. The model card does not quantify its effect on latency or token cost.
• Mixed context limits: the model card cites a 1M-token context window, while the API caps input at 131K tokens. Plan around the API limit.

All benchmark figures below come from Google's own testing (model card):

• No independent corroboration. Unite.AI found no third-party evaluations.
• 4K price conflict. Google's pricing page lists $0.113 at 4K, a 25% cut from $0.151. The Decoder reports $0.0756.
• Speed claim is unsourced. The video's claim of roughly twice the speed of GPT Image 2.5 is presented as an unofficial claim (video).

Nano Banana launched in August 2025, Nano Banana Pro (Gemini 3 Pro Image) in November 2025, and Nano Banana 2 in February 2026 (r/AISEOInsider recap). The video describes Flow's workflow of making an image first and then turning it into video. On that basis it argues that a better image model improves Flow's whole video pipeline (video). That argument is the creator's reasoning, not a measured result.

• Limitations Google acknowledges: hallucinations, poorly rendered small text and long paragraphs, imperfect character consistency, weak spatial reasoning, and occasional slowness or timeouts (model card). Small text is blurry at 1K, and the model sometimes confuses left and right (TNW).
• Safety: red-teaming found safety similar to or better than Gemini 3 Flash.
• Hands-on comparison contradicts the benchmarks: The Decoder found that Pro still produced noticeably better colours and proportions, and saw errors in relative scale.
• Early user reactions are mixed: the video says some users see a small gap and others think Pro still wins in places (video). The captions are automatic and may contain errors.

• Re-cost your own workloads. Prompt-only 1K generation roughly halves in cost, but editing with many reference images pays three times more for input. High thinking also adds text-output charges at $7.50 per million.
• Migrate on a deadline. gemini-3.1-flash-image is deprecated, and dates in the reviewed sources conflict. Plan for 29 October.
• Check for API gaps. Pipelines that depend on structured outputs or function calling need workarounds.
• Test on your own prompts first. The video's five-test kit covers text accuracy, instruction following, hands, character consistency and localized edits (video). Run it against Pro before switching.