Editing replaced generation as the core task. How GPT Image 2.5, Google's Nano Banana line, Midjourney V8.2 and Flux 2 differ, what open weights and LoRAs buy you, ControlNet vs instruction editing, and how licensing and C2PA provenance work now.
First of three episodes on AI media generation (this one is images and editing; then video, then the pipeline). A decision guide rather than a leaderboard: where GPT Image, Nano Banana, Midjourney and Flux each fit in late 2026, why instruction-driven editing replaced generation as the core task, what open weights buy you, and how licensing and provenance work.
The 2025 "artist vs collaborator" split is over and the collaborators won. Every frontier image model now sits behind a language model that reads the prompt with world knowledge and accepts images as input, so the unit of work became "here is an image, change this one thing and keep everything else." Text rendering, precise instruction following and identity-preserving reference images all landed at once for one reason: the image model became, or was paired with, a multimodal language model. Generation from scratch is now the special case where the input image is empty.
The lineage runs gpt-image-1 (2025), gpt-image-2, then ChatGPT Images 2.5 in September 2026, with a precision variant (Sunburst) and a fast default (Flare); the same models power ChatGPT and the Images API. As of this recording it holds the top slots on both the LMArena text-to-image and image-edit boards and on Artificial Analysis. For editing it does mask inpainting, up to four reference images, and multi-turn editing via the Responses API. Its weaknesses are latency at high quality and occasional text and consistency slips. Billing is per token (text in, image in, image out) with a cached-input discount; see the gpt-image-2.5-sunburst model page.
Nano Banana was gemini-2.5-flash-image (August 2025, now legacy). It was succeeded by Nano Banana Pro (gemini-3-pro-image) and Nano Banana 2 (gemini-3.1-flash-image, February 2026), plus a Lite tier. Per the Gemini image generation docs, Nano Banana 2 accepts up to fourteen reference images (objects plus characters), outputs up to 4K, does multi-turn sequential editing, and can ground generation in Google Search; the Pro model adds style references and identity preservation across up to five subjects. Every output carries a SynthID watermark with no opt-out. Imagen appears to be superseded for new work, though no formal retirement notice was found.
Midjourney moved from V7 (2025) to V8 alpha in March 2026 and V8.2 as the default in July 2026 (version history). V8.2's Edit Model replaces Omni Reference, Character Reference, Retexture and the Editor with one instruction-driven model taking up to four references, the same convergence OpenAI and Google made. Aesthetics and draft-mode ideation remain its strengths. The hard limits: still no official API and terms that bar automation; generations public by default below the Stealth tier; and it is the defendant in Disney Enterprises v. Midjourney (filed June 2025, joined by a separate Warner Bros. Discovery suit), currently in discovery with Midjourney demanding the studios' own AI records.
Black Forest Labs, founded by the original Stable Diffusion authors, shipped FLUX.1 Kontext (in-context editing without masks) in 2025 and FLUX.2 in November 2025: a Mistral vision-language model paired with a rectified-flow transformer, up to ten references, 4MP editing. Tiers: Pro and Flex (API only); FLUX.2 dev (32B, open weights, non-commercial license); and FLUX.2 klein (January 2026), where the 4B model is Apache 2.0 and the 9B is non-commercial. The open-weights editing sub-board puts FLUX.2 and HunyuanImage roughly 130-150 Elo behind the closed frontier. Open weights earn their place through fine-tuning, on-prem privacy and composability with ControlNets and node graphs rather than raw quality.
Change this thing is instruction editing, now standard everywhere; masked inpainting and outpainting (Photoshop Generative Fill, GPT Image masks, FLUX.1 Fill) remain the hard constraint when the instruction is not enough. Keep this subject is reference conditioning, whose open-world mechanism is IP-Adapter (decoupled image cross-attention on a frozen base) and whose closed equivalents are Google's fourteen references and Midjourney's edit-model references; single references drift on fine detail. When drift is unacceptable, train a LoRA: roughly ten to twenty images and about a thousand steps on Flux (fal guide, FLUX.2 LoRA guide, Replicate trainer), open weights only. Keep this structure is ControlNet depth/edge/pose conditioning, still required for geometry fidelity per Autodesk's testing, with first-party support in Qwen-Image-Edit and FLUX.2 ComfyUI nodes. Multi-turn is for exploration; for repeatability, reproduce the winning edit as one instruction from the original.
Ownership is a human-authorship question: the US Copyright Office's Copyrightability report requires human authorship, treats prompts as unprotectable instructions, and protects AI-assisted work to the extent of the human contribution; the Supreme Court declined Thaler v. Perlmutter in March 2026, leaving the "how much human is enough" line undrawn. Commercial use is a vendor question: OpenAI (you own outputs), Midjourney (paid plans, no indemnity), Flux (tiered), Firefly (indemnified). Training-data fair use is unresolved in the US; Andersen v. Stability AI is the image bellwether, while the UK High Court largely rejected Getty's claims against Stability in November 2025. Provenance now has two layers: C2PA 2.3 manifests plus pixel watermarks; OpenAI now embeds both C2PA and SynthID (API guide), as Google already did. Manifests are detailed but stripped on re-save, screenshot and most platform uploads; SynthID survives those but carries little information. Labeling is now law: EU AI Act Article 50 and California SB 942 both enforceable from August 2026, China's rules from September 2025.