Finding Nano Banana 2.1 in Google AI Studio

Matthew Berman shows the model selector and generates a close-up portrait in a short October 2026 demonstration.

MBMatthew Berman연사 · Forward Future
채널
Matthew Berman
게시일
2026년 10월 7일
영상
0:39
읽기
1분
슬라이드
2
원문
영어
YouTube에서 열기
스크롤하면 영상이 오른쪽 아래로 작아져 계속 재생됩니다. 자막은 읽기 언어를 따릅니다.
읽기 언어2개 언어로 읽을 수 있음
이 글은 아직 한국어 번역이 없어 영어 원문으로 표시합니다.
요약

30초 요약

Matthew Berman demonstrates where to select Nano Banana 2.1 in the Google AI Studio playground and tries a portrait prompt. He says the model is paid and praises the detail in the resulting image. The interface and availability shown reflect the video’s October 2026 recording.

  1. 1Open the playgroundBerman opens Google AI Studio and locates the model selector on the right.
  2. 2Filter for imagesHe selects the Images filter and chooses Nano Banana 2.1, which he describes as paid.
  3. 3A portrait promptHe requests a hyperrealistic close-up and comments on the face, hair and jacket details.

핵심 정리

  1. In the shown interface, the model selector has an Images filter for finding Nano Banana 2.1.
  2. Berman says the model is paid in the demonstrated setup.
  3. The portrait is one example, and Berman’s assessment of its detail is subjective.
전문

챕터별로 다듬은 전문 읽기

원본 자막만 보기
01

Find and select the model

Berman opens the Google AI Studio playground, selects the Images filter and chooses Nano Banana 2.1. He notes that the model is paid in the shown setup.

To find Nano Banana 2.1 in the Google AI Studio interface shown in October 2026, I open the playground and use the model selector on the right. I choose the Images filter and select Nano Banana 2.1. I note that the model is paid in this setup; the interface, availability and pricing can change.

The Images filter in model selection
슬라이드 1 / 2The Images filter in model selectionThe 2026 interface shows the model selector and Images category used in the walkthrough.
02

Generate a portrait

He asks for a hyperrealistic close-up of a woman and examines the resulting detail. His positive assessment is based on this one example.

Generated portrait example
슬라이드 2 / 2Generated portrait exampleBerman examines a close-up image produced from his prompt.

I ask it to generate a hyperrealistic close-up of a woman. Looking closely at the result, I notice facial texture, wrinkles around the eyes, individual hairs and fibers in the jacket. I find this image impressive, though it is one example rather than a systematic comparison.

같은 채널의 다른 글

슬라이드 12장34:21EN
Matthew Berman· 2026년 10월 27일Matthew Berman Examines Anthropic’s IPO Filing, AI Growth and GovernanceMatthew Berman, Sam Altman · Forward FutureThe presenter reviews the S-1’s financial metrics, compute strategy and voting structure while questioning the tension between rapid AI development and safety.AnthropicAI Safety엔터프라이즈 AI+2
0:26EN
Matthew Berman· 2026년 10월 25일Sam Altman on Human Values and Problem-Solving in an AI WorldSam Altman · OpenAIIn a short interview excerpt, Altman considers the enjoyment of solving hard problems and the human role in interpreting results.Human-AI CollaborationFuture of WorkAI Strategy
슬라이드 2장0:36EN
Matthew Berman· 2026년 10월 25일Kimi K3.1 and a Rubik’s Cube Solver DemonstrationMatthew Berman · Forward FutureMatthew Berman shows a scrambled virtual cube being solved and gives the Kimi K3.1 test a favorable assessment.평가(Evals)코딩 에이전트Open-weight models
슬라이드 2장0:21EN
Matthew Berman· 2026년 10월 24일An AI-Generated Kimi Benchmark Chart Used Invented ScoresMatthew Berman · Forward FutureMatthew Berman asks for a graph of Kimi K3.1 Pro benchmarks, then discovers that the displayed figures are illustrative placeholders.Benchmarks평가(Evals)Generative media
슬라이드 6장25:50EN
Matthew Berman· 2026년 10월 24일Kimi K3.1 Pro Tested: Strong 3D Demos, Uneven Results and Competitive PricingMatthew Berman · Forward FutureMatthew Berman tests the open-weight model on coding, 3D rendering and visual explanations, then compares benchmark results and prices.Open-weight modelsBenchmarksAI cost & performance+1
슬라이드 2장32:20EN
Matthew Berman· 2026년 10월 23일Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1