Nano Banana Pro 4K image model – 8% text errors, $0.13 renders

Google DeepMind has formally launched Nano Banana Pro (Gemini 3 Pro Image), a reasoning‑aware image generation and editing model now available in the Gemini app and web, AI Mode in Search, Flow, NotebookLM and more, following earlier leaked tests of 4K outputs and text rendering 4k leak. The model supports 1K/2K/4K output, lighting and camera controls, aspect‑ratio changes, multilingual text rendering, multi‑image compositing, and Search grounding for fact‑aware visuals DeepMind feature thread surfaces and getting started.

For developers, Nano Banana Pro appears in Google AI Studio as gemini-3-pro-image-preview with pricing of $2.00/M input tokens, $12.00/M output tokens, and about $0.134 per generated image, sharing the same Jan 2025 knowledge cutoff and 1M context as Gemini 3 Pro text ai studio pricing. Vertex AI’s model garden lists the same model ID (publishers/google/models/gemini-3-pro-image-preview), and emphasizes "reasoning for image generation", 4K support, and optional grounding with Search for more factual images vertex docs overview.

On the front end, Gemini web now exposes a "Show thinking (Nano Banana Pro)" toggle on image generations, hinting that chain‑of‑thought style internal reasoning is being applied even for visuals and optionally made visible to users show thinking ui. Community posts confirm successful runs in Gemini web (“cat shooting power into a wormhole”, “minion with nano banana”) and in the mobile app, aligning with AILeaks’ note that the model is rolling out broadly across Google’s AI offerings gemini app release rollout confirmation.

Early benchmark charts circulating today show Gemini 3 Pro Image (Nano Banana Pro) leading competing models like GPT‑Image 1, Seedream v4 4K and Flux Pro Kontext Max on both text‑to‑image and image‑editing Elo scores, while also drastically cutting text rendering errors image elo scores editing elo chart. In text‑to‑image, Gemini 3 Pro Image tops overall preference and visual quality, and in editing it wins across object/environment edits, style transfer, single and multi‑character consistency, and text editing, with Elo gaps of ~100 points or more over GPT‑Image 1 in several categories editing elo chart.

A separate heatmap on single‑line text rendering shows Nano Banana Pro at roughly 8% average error across languages versus GPT‑Image 1 at about 38%, with particularly stark differences in scripts like Arabic, Hindi and Hebrew text error heatmap. Sample outputs like a 4K "How solar power works" infographic, complete with clean typography and structured diagram elements, illustrate the practical impact for product teams that care about legible UI mocks and dashboards rather than just concept art solar infographic sample. DeepMind and third‑party commentary are already framing Nano Banana Pro as a "reasoning image model"—the idea being that its improved factual grounding and layout reasoning, not just its decoder quality, are what push these numbers above older models reasoning model quote.

Inference host fal.ai has Nano Banana Pro live on day zero, exposing separate text‑to‑image and image‑editing endpoints so teams can start wiring Gemini 3 Pro Image into their own apps without waiting on Google’s stacks fal launch. The text‑to‑image model page quotes per‑image pricing and supports the full creative prompt flow, while a dedicated edit endpoint lets you upload an image and drive semantic edits with natural language rather than masks fal text to image fal image editing.

Because fal handles queuing and scaling, this gives smaller shops an immediate way to prototype Nano Banana‑backed features—marketing image pipelines, design tools, product configurators—without standing up Vertex or dealing with quota friction in AI Studio fal endpoints thread. For AI engineers already using fal for other diffusion or video models, swapping in Nano Banana Pro should mostly be a matter of changing the model slug and adjusting prompts for its stronger text and layout handling.

Video‑first platform Higgsfield is pitching an unusually aggressive commercial package around Nano Banana Pro: unlimited 4K generations for a full year at up to 65% off as part of a Black Friday sale, plus a short‑term promo of 350 free credits for retweets and comments higgsfield launch promo. They emphasize Gemini‑powered reasoning, multilingual text quality and precise edit controls, positioning their offer as the way to tap the model without worrying about per‑image caps or token budgeting higgsfield feature recap.

The Higgsfield landing page reinforces this framing by advertising "Unlimited 4K Nano Banana Pro" plans and showing Hollywood‑style VFX clips and plushie conversions as example outputs, which target agencies and heavy social content shops more than hobbyists Higgsfield site. For AI leads, the main trade‑off versus going direct to Google is clear: pay a fixed platform price with Higgsfield’s workflow UI on top, or stay closer to the metal with Vertex/AI Studio and handle quotas and orchestration yourself.

Genspark, the "everything in one" AI workspace, now exposes Google’s Nano Banana image model alongside its existing stack of text and video tools, so users can generate and edit images without leaving the same canvas they use for slides, docs and data analysis genspark overview. In a walkthrough, the team shows Nano Banana‑driven poster design and image editing flows happening directly inside Genspark, with no separate API setup or platform switching nano banana in genspark.

Because Genspark routes to multiple frontier models under one subscription and offers an AI Inbox, shared projects and agent flows, this integration means design work (posters, promo graphics, visual assets for decks) can sit in the same project as the copy, research, and analysis that feeds it genspark video models. Their Black Friday‑era promo also includes free compute credits for new signups, making it easy for teams to benchmark Nano Banana Pro’s text and layout handling against whatever image stack they already use, before deciding where to standardize genspark pricing promo.

Allen Institute for AI has released the Olmo 3 family: 7B and 32B base models plus Instruct, Think, and RL Zero variants, all with open weights, pretraining data mix, post‑training datasets, intermediate checkpoints, and training logs. They explicitly position Olmo 3‑Base 32B as the strongest fully open 32B base and the 7B variants as top "Western" thinking/instruct models, aiming to give teams a transparent alternative to Qwen‑scale systems. release thread The full suite is live on Hugging Face and documented in a detailed technical report, so you can inspect or reproduce every stage of the model flow from SFT through DPO and RLVR. huggingface collection paper pdf ai2 blog

Beyond the base and Think models, Ai2 is releasing four RL Zero tracks for the 7B Olmo 3—focused separately on math, code, instruction following, and a mixed blend—each with open datasets and intermediate checkpoints. release thread The team explicitly frames this as a lab bench for studying how starting RL directly from the base model (inspired by DeepSeek R1) interacts with mid‑training reasoning traces and how much benchmark lift comes from RL versus SFT or DPO, in areas where Qwen‑based RLVR runs have raised contamination questions. paper pdf

Benchmark charts shared with the release show Olmo 3‑Base 32B beating or matching strong open peers like Marin 32B, Apertus 70B, Qwen 2.5 32B, Gemma 3 27B, and even Llama 3 170B on tasks such as HumanEval (66.5), DROP (81.0), SQuAD (94.5), and CoQA (74.1). benchmark charts For AI engineers this means you get a competitive, mid‑sized base model for code, reading comprehension, and QA that can still fit on a single 80GB GPU, but without giving up the transparency and fine‑tuning control that most closed 30–70B models lack.

The Olmo 3‑Think 32B reasoning model lands within 1–2 points of Qwen3 32B and Qwen3 VL 32B Thinking on tough reasoning suites, scoring around 89.0 on IFEval, 96.1 on MATH, ~90 on BigBench‑Hard, and 89.7 on HumanEvalPlus, while also edging those peers on OMEGA. benchmark charts Ai2 credits a three‑stage post‑training pipeline—Dolci‑Think SFT, an expanded DPO setup that exploits deltas between Qwen3 32B and 0.6B responses, and large‑scale RLVR—for turning the 32B base into a high‑end open reasoning model, which is good news if you want near‑frontier math/code performance without leaving the open ecosystem.

Ai2 is doing a live technical walkthrough of Olmo 3 with Hugging Face at 9am PT, followed by an "afterparty" discussion on Ai2’s Discord. livestream invite For engineers and researchers this is a chance to hear the training and post‑training details directly from the authors, ask about design decisions like context length, data mixtures, DPO setup, and RLVR tricks, and see how they envision people extending the new base and Think checkpoints.

Several Olmo 3 authors are teasing behind‑the‑scenes stories about the infra and training stack, including "babysitting" long training runs and the specialized NCCL and code‑execution setups that kept large‑scale RL experiments stable. training babysitting One engineer says they have "very fun" training‑run anecdotes they hope to publish in the next few weeks, while another notes a forthcoming write‑up on their code execution environment for RL, and teammates call out contributors who debugged low‑level NCCL issues during post‑training. code execution note nccl comment If you care about reproducing Olmo‑class training or adapting their RL pipeline, it’s worth watching for these deep‑dive posts as companions to the main technical report.