MOSS-Image vs Nano Banana 2: One Has a Shutdown Date, the Other Has No Endpoint

This comparison has an awkward property that neither vendor advertises. N​ano Banana 2 — G​oogle's consumer name for G​emini 3.1 Flash Image, the identifier gemini-3.1-flash-image — has a shutdown date twelve days from now. G​oogle's G​emini API changelog deprecates it on October 29, 2026, three weeks after N​ano Banana 2.1 shipped on October 6 at roughly half the per-image price. M​OSS-Image, released as Apache 2.0 open weights on October 16, 2026, has no shutdown date because there is nothing to shut down: it is a 27 GB download you run yourself, and no commercial API for it has been published.

So the honest version of this matchup is not "which is better." It is a comparison between a model that is about to stop existing on someone else's servers and a model that will exist on yours for as long as you keep the files. That framing is more useful than a spec sheet, because it is exactly the decision a team is making this month, and because the second-order question — what replaces N​ano Banana 2 — has an answer that changes the price by half.

First, which N​ano Banana you are actually comparing against

G​oogle's naming has three layers and the differences are not cosmetic.

N​ano Banana 2 is G​emini 3.1 Flash Image. The consumer rollout began February 26, 2026 and the developer API reached general availability on May 28, 2026 under gemini-3.1-flash-image. It is the default image generator across the G​emini app, G​oogle Search's Lens and AI Mode surfaces, Flow and G​oogle Ads — which is the fact that dominates any adoption decision, because it is not a model your users have to be persuaded to use.

N​ano Banana Pro is the higher tier, and it is the model OpenMOSS chose to benchmark against in its own report. On the vendor's LongLongTextBench table, N​ano Banana Pro scores 96.6 Mean-6 against M​OSS-Image's 96.3 — a three-tenths gap that is the narrowest margin in the table and the one most worth understanding.

N​ano Banana 2.1 is what replaces N​ano Banana 2 on October 6. It is not the subject here, but it is the destination, and its existence sets the terms: the deprecation notice and the successor arrived in the same changelog entry.

The three-tenths gap, read properly

M​OSS-Image's report is unusual in that it publishes a table where a competitor beats it. LongLongTextBench is 240 literary-text rendering tasks — 120 English, 120 Chinese, at a mean target length of 1,339.8 characters, roughly eleven times LongTextBench's average — and M​OSS-Image's 96.3 Mean-6 puts it well clear of Q​wen-Image-2512's 64.9, with 97.1 English and 92.4 Chinese on the longest-condition subset. Above it sit GPT Image 2 at 99.0 and N​ano Banana Pro at 96.6.

The three-tenths between M​OSS-Image and N​ano Banana Pro is smaller than the confidence anyone should have in either number. Both were produced by different labs on a benchmark authored by OpenMOSS, using the lab's own runs of G​oogle's model. And N​ano Banana 2 is not the model in that row — the Flash tier sits below Pro, so the honest reading is that M​OSS-Image's text-rendering result is approximately at the level of G​oogle's premium tier on a test M​OSS-Image's authors wrote, while the model you would actually be comparing it to in a procurement is a tier below that.

Where M​OSS-Image is not close to anything G​oogle ships is layout. On 7Bench, the benchmark the report also introduces, M​OSS-Image reports a mean layout IoU of 0.88 with a Layout AUC of 0.70. N​ano Banana Pro is at 0.33 and 0.20 in the same table. If those numbers survive reproduction, precise object placement is a capability M​OSS-Image owns and no G​emini image model currently contests. They have not been reproduced, and the same lab wrote the metric.

What N​ano Banana 2 gives you today, and for twelve more days

The capability sheet is a generalist's, and it is the reason the model is everywhere.

• Consistency — up to five characters and fourteen objects held stable across a workflow, the feature the whole N​ano Banana line is sold on.

• Output — up to 4K across fourteen aspect ratios, including the extreme 1:4 and 1:8 shapes banner and wallpaper formats need.

• Text in images — in-image rendering including translation and localisation with layout preserved. This is the capability that overlaps most directly with M​OSS-Image's pitch, and it is the one G​oogle demoed at launch.

• Grounding — generation grounded in web and image search rather than in the prompt alone, the same architectural instinct M​eta took further with M​use Image.

• Controls — configurable thinking levels trading latency against reasoning, plus SynthID watermarking and C2PA provenance metadata on every output by default.

• Price — output billed at $60 per million tokens, which G​oogle's own documentation resolves to about $0.045 at 512px, $0.067 at 1024×1024, $0.101 at 2K and $0.151 at 4K, with batch mode at half. Artificial Analysis lists it at $67 per 1,000 images.

• Measured position — Elo 1,125 on Artificial Analysis's text-to-image board and 1,108 on its image-editing board as of October 6, 2026.

None of that is in dispute. What is also true is that on October 29 the identifier stops working, and the route forward is N​ano Banana 2.1 — which Artificial Analysis has not yet scored, which G​oogle prices at roughly half, and which a separate piece on this blog covers in detail.

What M​OSS-Image gives you, and what it asks for in return

The architecture is a 5B single-stream diffusion transformer trained from scratch: 42 joint blocks, hidden width 3,072, 24 heads at 128 dimensions, an 8,192-wide SiTU-GLU feed-forward and a four-axis rotary position encoding whose first axis is reserved for a reference index. The 5B badge excludes the expensive parts — a frozen Qwen3.5-9B text encoder and a frozen FLUX.2 VAE loaded from FLUX.2-klein-4B at 32 latent channels, with 2×2 latent patches over an 8× downsample giving an effective stride of 16 pixels.

The prompt can be a JSON object. Bounding boxes in absolute pixel coordinates measured from the canvas centre, literal text strings, typography notes, all with bbox_format "yxyx". Prompt ceiling is 4,096 tokens at the final pretraining stage, output dimensions must be positive multiples of 16, and the guide recommends one, one and a half or two megapixels with a supported range from 512×512 to 4M. Sampling defaults are flow-matching Euler at 50 steps, CFG 3.0, shift 5.0, with skip-layer guidance on the negative branch at layer 22 — and the default guidance path needs two transformer forwards per step, which is why 50 steps is not the cheap setting it sounds like.

The cost of admission is real: about 27.2 GB for the SFT repository, roughly 16.8 GB of which is the text encoder, plus your own GPU and your own serving work. The reward for that is a model you can quantise, fine-tune, run air-gapped and ship inside a commercial product without a licence negotiation, because Apache 2.0 covers the weights and the standalone pipeline code with no revenue threshold and no acceptable-use annex.

Line by line

• Status — N​ano Banana 2 is deprecated with shutdown on October 29, 2026; M​OSS-Image was released October 16, 2026 as open weights with no deprecation mechanism

• Access — N​ano Banana 2 through G​oogle's API and consumer surfaces, and through routing platforms; M​OSS-Image by downloading it

• Price — N​ano Banana 2 about $0.067 per 1024×1024 image and $0.151 at 4K, billed in output tokens, batch at half; M​OSS-Image free with a ~27.2 GB footprint and your own GPU time

• Licence — N​ano Banana 2 is proprietary, API-only, with no weights available; M​OSS-Image is Apache 2.0 for weights and standalone pipeline code

• Independent score — N​ano Banana 2 at Elo 1,125 text-to-image and 1,108 editing on Artificial Analysis; M​OSS-Image has no Artificial Analysis entry and its published scores come from benchmarks its own authors wrote

• Text rendering — N​ano Banana Pro scores 96.6 Mean-6 on LongLongTextBench against M​OSS-Image's 96.3; the Flash tier is not in that row

• Layout control — M​OSS-Image accepts JSON layouts in absolute centre-relative pixel coordinates and reports 0.88 mean IoU on 7Bench; N​ano Banana Pro reports 0.33 on the same table and G​oogle publishes no layout metric of its own

• References — N​ano Banana 2 holds up to five characters and fourteen objects consistent in one prompt; M​OSS-Image's reference conditioning is a reserved positional axis the vendor describes as future work, not a shipped feature

• Provenance — N​ano Banana 2 applies SynthID watermarking and C2PA metadata to every output by default; M​OSS-Image carries no watermarking layer

• Fine-tuning — M​OSS-Image can be fine-tuned and quantised, and community quantisations, a 4-step DMD LoRA and an 8-step distilled variant appeared within about a day; N​ano Banana 2 cannot be touched

Why the deprecation changes the answer

If N​ano Banana 2 and M​OSS-Image were both stable options, the decision would be ordinary: an API at seven cents an image against a self-hosted model at your own cost, with quality roughly comparable on a vendor-authored text benchmark and layout going decisively to M​OSS-Image. The deprecation breaks the symmetry, and it breaks it in both directions.

Against N​ano Banana 2, M​OSS-Image now wins the longevity argument outright. Building a pipeline on a model that has an announced end-of-life twelve days away and a successor you have not measured is a migration you have already scheduled. Files you download cannot be deprecated by a changelog entry. That is not a rhetorical point about openness; it is a scheduling fact, and it is the single strongest argument this comparison produces.

But the deprecation also cuts the other way, and this is the part that usually gets left out. N​ano Banana 2.1 arriving at roughly half the price means the alternative to M​OSS-Image is not a seven-cent image — it is a three-and-a-half-cent image, and one that G​oogle will keep improving on its own schedule with no operational burden on you. M​OSS-Image's total cost of ownership only beats that if your GPU time is cheap, your volume is high, or you need one of the things an API structurally cannot give you: fine-tuning, quantisation, air-gapped deployment, or a licence you do not have to renegotiate.

The OrcaRouter side of this, without overclaiming

This is the one matchup in the current set where the routing story is concrete rather than hypothetical, so it is worth stating precisely. N​ano Banana 2 is served on our catalogue as google/gemini-3.1-flash-image-preview — though given the October 29 shutdown, the sensible thing to do with that endpoint this month is a migration test, not a new integration, and the successor is reachable the same way. M​OSS-Image is not on our catalogue and we do not host it; it is a download, and nothing here implies otherwise.

What the combination is actually good for is the decision in front of you. If you want to know whether a self-hosted M​OSS-Image produces better text-and-layout output than the hosted tier you are paying for, you need both numbers on the same prompts, and the hosted half of that test should not require a second contract. One API key, provider list price passed through with 0% markup so a G​oogle price move is live here the same day, automatic failover across providers so a comparison run does not die on one upstream's bad afternoon, and a routing DSL that puts both behind a single endpoint. That is the whole claim, and it stops exactly where the offer to host M​OSS-Image would begin.

What to do in the next twelve days

If you are on N​ano Banana 2 in production, the immediate task has nothing to do with M​OSS-Image: you have a deprecated identifier, a successor at half the price, and a deadline. Re-run your eval set against the successor before the 29th and decide on evidence rather than on the price cut.

If you are evaluating M​OSS-Image, do it for the two reasons that survive the comparison. First, layout control — the 0.88 against 0.33 gap on 7Bench is the one result in this space that nothing hosted currently contests, and if precise placement is your problem, no API tier solves it. Second, permanence — Apache 2.0 weights that cannot be deprecated, priced, or gated by anyone's roadmap but yours.

Do not adopt M​OSS-Image for text rendering. It is excellent at it and it is not ahead at it: 96.3 sits below N​ano Banana Pro's 96.6 and below GPT Image 2's 99.0 on the vendor's own table, and the model you would be replacing costs a few cents an image with none of the 27 GB. The case for this model is narrow, specific and real, and it is not the case its benchmark table is built to make.

Two things would settle it. An independent run of LongLongTextBench or 7Bench by someone other than OpenMOSS, and the vLLM-Omni pull request that adds M​OSS-Image with skip-layer guidance merging into a tagged release — it was opened October 14, stands at 3,605 added lines across 25 files, and is still open with the reviewer asking for output-parity evidence against a pinned reference. Until both happen, the accurate summary is that G​oogle's model is the better generalist, cheaper to reach, and disappearing in twelve days, while OpenMOSS's model does one thing nobody else does, requires hardware, and will still be installable when the changelog that retired its rival is long forgotten.

The documented AI failures and agent incidents behind releases like this one are collected in the OrcaRouter AI Incident Archive, where each entry carries its own sources, severity, confidence grade and disputed flag.