MOSS-Image vs GPT Image 2.5: A Downloadable 5B Backbone Against Two Snapshotted Models

Start with the naming, because it is the thing that trips people up first. There is no model called gpt-image-2.5. What exists is a pair — GPT Image 2.5 Sunburst, identifier gpt-image-2.5-sunburst, and GPT Image 2.5 Flare, identifier gpt-image-2.5-flare — both snapshotted 2026-09-08 and both closed. Against them stands M​OSS-Image, released by OpenMOSS on October 16, 2026 as two Apache 2.0 checkpoints, M​OSS-Image-Base and M​OSS-Image-SFT, that you download and run. One side sells you an endpoint; the other hands you 27 gigabytes and wishes you luck with the encoder.

That is the whole shape of the comparison, and almost every other difference follows from it. The comparison below treats the two O​penAI variants as one side only where O​penAI's own pricing and documentation treat them as one — which is true for the rate card and false for capability, and that distinction gets its own section. Every number attributed to O​penAI is O​penAI's published rate card; every number attributed to M​OSS-Image is OpenMOSS's own, from the release and its technical report, and unreproduced by anyone.

Two models against one download

O​penAI's 2.5 generation is a fork rather than a version bump. Earlier generations shipped one primary model ID and asked you to choose a quality tier inside it. With 2.5 there are two separately named, separately snapshotted models and no umbrella alias — code written against a bare gpt-image-2.5 will not run. Sunburst is positioned for capability, Flare for speed, and both bill identically, so the choice between them is a latency-and-precision decision rather than a budget one.

M​OSS-Image is also two models, but the split is a training split rather than a product split. Base is the pretrained checkpoint, deliberately left undistilled — the model card says explicitly that no post-training or distillation of inference parameters such as CFG was applied, to preserve training information and keep LoRA training viable. SFT is fine-tuned on curated data and is the one most people should run. The vendor's comparison example shows SFT producing tighter text grouping and a more organised layout on the same prompt, which is what a curated-data fine-tune of an undistilled base should do.

• What you call it — GPT Image 2.5: two IDs, gpt-image-2.5-sunburst and gpt-image-2.5-flare, no bare alias. M​OSS-Image: two repositories, OpenMOSS-Team/M​OSS-Image-Base and OpenMOSS-Team/M​OSS-Image-SFT, with ModelScope mirrors for both.

• What you receive — GPT Image 2.5: an O​penAI-compatible endpoint and a snapshot date. M​OSS-Image: safetensors weights, a Diffusers custom pipeline, a Gradio app, and a native ComfyUI plugin installable through ComfyUI Manager.

• What it costs — GPT Image 2.5: metered in tokens, $8.00 per million image input and $30.00 per million image output, batch at half. M​OSS-Image: nothing for the licence, everything for the GPU.

• Whose licence governs the output — GPT Image 2.5: O​penAI's terms, which are not reproduced in the developer documentation itself. M​OSS-Image: Apache 2.0 for weights and bundled inference code, no revenue threshold and no acceptable-use annex.

Layout control, and the module that is not the model's

Both systems let you place things by coordinate. They are not equivalent in what sits behind the coordinates.

M​OSS-Image's layout format takes a JSON object as the prompt: a summary with the scene caption, and an items array where each element has a label of text, character or object, a caption, a bounding box, and — for text — the literal string to render. Boxes use bbox_format: "yxyx" in bbox_coord_space: "abs_centralized", meaning pixel offsets from the canvas centre.

GPT Image 2.5's documented interface is not a layout grammar. Its control surface is the prompt itself plus reference images; the layout-style behaviour that shows up in the 2.5 generation's editing strengths is expressed through iterative edits rather than through a structured box specification. That is a real capability difference, and it is the one place where M​OSS-Image's interface is more explicit than O​penAI's.

The caveat on M​OSS-Image's side is worth stating before any benchmark: the prompt enhancement module that turns a short idea into a full layout specification is not a small model of OpenMOSS's own. The technical report states that LayoutPE uses G​PT-6 with modular task instructions at inference time, and the bundled prompt-enhancer skill is described as being designed for G​PT-6-family agents. The strongest end-to-end layout result in OpenMOSS's own evaluation is therefore produced by a two-model pipeline whose specification half runs on someone else's API. If you use M​OSS-Image without that enhancer, you are supplying the layout JSON yourself, and the pipeline you are measuring is a different one.

The evidence, and how thin it is on both sides

OpenMOSS evaluated M​OSS-Image on two benchmarks that ship with the release and that the vendor also built. LongLongTextBench is 240 literary-text rendering tasks, 120 English and 120 Chinese, at a mean target length of 1,339.8 characters. RealLayoutArena is 61 design tasks — 20 posters, 20 infographics, 21 user interfaces — judged by blind pairwise comparison and fitted to a Bradley–Terry Elo scale. On the end-to-end arena, M​OSS-Image lands at 1698.70 Elo with a 95% interval of 1655.96 to 1741.43, second overall behind GPT Image 2 at 1844.35. Restricted to the backbone alone under shared enhanced prompts, M​OSS-Image comes first at 1807.34.

Read that carefully. The benchmark on which M​OSS-Image places first is the one where the comparison is restricted to the generator and the prompt enhancement is shared — which is exactly the configuration that removes the third-party-specification problem. The benchmark on which it places second is the end-to-end one. Neither is a neutral test.

O​penAI publishes no comparable layout or text-rendering benchmark for GPT Image 2.5. The independent evidence that exists for the O​penAI models is on the Artificial Analysis boards, where the 2.5 variants hold the top two places on the image-editing leaderboard. That is a blind-preference measurement with a published methodology, and it does not test layout control at all. So the two models are measured on disjoint axes by different parties, and the temptation to put 1698.70 next to a board Elo is one to resist — they are not the same scale and they are not measuring the same thing.

What each costs at volume

GPT Image 2.5 meters in tokens, on a rate card identical to GPT Image 2: $5.00 per million text-input tokens with cached text at $1.25, $8.00 per million image-input with cached image at $2.00, and $30.00 per million image-output. Batch halves each. There is no per-image price on the vendor's page and no free allowance, so the only way to a per-image figure is a conversion — and the independent boards publish one, currently near $0.21 per image for a high-quality 1024×1024. Treat that as a board derivation, not as O​penAI's price.

M​OSS-Image has no rate card because there is nothing to bill. The cost is the machine. The SFT repository is about 27.2 GB, and the number that surprises people is the split: roughly 16.8 GB across four shards is the frozen Qwen3.5-9B text encoder and about 10.2 GB across three is the transformer itself. The 5B badge describes the diffusion generator alone. Sampling defaults are flow-matching Euler at 50 steps, CFG 3.0, shift 5.0, with negative-branch skip-layer guidance 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.

At a hundred thousand images a month, the token-metered side lands in the low tens of thousands of dollars; the downloaded side lands wherever your GPU rental lands. That is not a comparison that flatters either party, which is why the decision usually turns on something else: whether you can ship a config change or need a procurement conversation.

Running the two side by side

If you want to settle this on your own prompts rather than on anyone's leaderboard, the two sides sit on opposite ends of a boundary that our own product does not cross in the middle. M​OSS-Image is not on the OrcaRouter catalogue — we serve more than 200 models and it is not one of them — so the open-weight half of the experiment is yours to host. GPT Image 2.5 Sunburst and Flare are routable through the same O​penAI-compatible endpoint as everything else on the catalogue, at the provider's list rates of $8 per million image-input tokens and $30 per million image-output tokens, with no markup added on our side.

The useful part of that arrangement for a comparison like this one is the failover behaviour. When you are running a benchmarking loop that fires several hundred prompts at a hosted model, a provider-side rate limit or a bad minute turns into dropped samples, and automatic failover across providers means the loop finishes instead of leaving you with a half-populated results table. Whether the answer that comes back from the fallback is comparable to the primary is a question you should decide before you start, not after.

Which side of the boundary you are on

Choose M​OSS-Image if you want the weights. Apache 2.0 with no revenue ceiling is a short commercial review, the Base checkpoint was explicitly left undistilled for LoRA training, the layout JSON is a real specification rather than a prompt convention, and you can point a ComfyUI install at it today. Go in knowing that the encoder is the download, that a 50-step default at two forwards per step is a genuine serving cost, and that the headline Elo figure was produced with a third-party model in the specification half of the pipeline. If you skip the enhancer and write the layout yourself, you are on your own for the numbers — which is fine, because nobody has reproduced OpenMOSS's either.

Choose GPT Image 2.5 if you need a callable endpoint this week, if your work is iterative editing rather than composed layout, or if an independent benchmark with a published methodology matters more to your decision than a structured control interface does. The two-variant fork means an extra decision — Sunburst when the edit is the deliverable, Flare as the default — and the token-metered bill is harder to predict than a per-image rate, but the model is at the top of the only neutral board that covers editing, and that is worth something a vendor's own table is not.

The gap that would most change this comparison is on the O​penAI side, not the OpenMOSS side: a published layout-control evaluation, or an independent layout benchmark that carries both models. Until one exists, the two systems are being measured on different axes by different people, and the sentence that best describes the state of play is that M​OSS-Image has the more explicit interface and GPT Image 2.5 has the more independent evidence.

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.