Use one GPTProto API key to run image jobs with GPT Image 2, Nano Banana, Seedream and other supported models. Compare outputs and costs on a sample set, then scale the model that fits your catalog or creative brief.
A catalog image, a game asset and a campaign visual may need different models. Test GPT Image 2, Nano Banana and Seedream with the same GPTProto key and shared balance. Keep your asset IDs and output review in one production workflow.
Same worker, same key — only the model name changes.
Give each model the same brief and inspect the result before you commit a full image run. Compare the details it follows, the time this sample took and the cost of this request.
A wine glass filled to the brim with white wine, the surface level with the rim of the glass, standing upright on a plain wooden table. Straight-on eye-level shot, glass upright and not tilted, no other objects in the frame.
Pick the look your project needs, then see how image models handle the same style brief. Compare the colors, composition and texture before choosing what to test in your own workflow.
Studio headshot of a woman in a charcoal blazer against a mid-grey seamless backdrop, lit by a large softbox slightly above and to the left with a soft fill from the right, catchlights in both eyes. An 85mm look with shallow depth of field, skin texture and flyaway hairs kept rather than smoothed.
Try another model without opening another provider account. Keep your asset pipeline visible as volumes grow: know what was submitted, where the result went and how much each model costs.
One key, multiple image models
Test available image models under one GPTProto account and shared balance. See how each one handles your actual product or creative brief.
Browse image models →Know the cost before a full run
Check the current rate for the model and settings you plan to use. Price a small sample first, then forecast a month of production.
Estimate image costs →Keep the batch in your control
Assign a product or asset ID to each request, store the returned task ID and review finished, failed and uncertain submissions separately.
Try models for product imagery, reference-based edits and campaign assets from the same GPTProto account. Compare the output and current rate, then check each model's inputs before adding it to your worker.
E-commerce shots, game assets, packaging and short-drama key visuals — batch image production where reference-image consistency decides whether the batch is usable.
Consistencia de la referenciaAsync batch jobsCost per image| Modelo | Precio: GPTProto | vs Oficial | vs OpenRouter | Contexto | Modalidades | Estabilidad | Acción |
|---|---|---|---|---|---|---|---|
 GPT Image 2OpenAI | $6.40por imagen | −20% | −24% | — | → | Probar | |
 Nano Banana 2 (Gemini 3.1 Flash Image)Google | $0.040por imagen | −40% | −43% | — | → | Probar | |
 Seedream 5.0 Pro (Build 260628)Bytedance | $0.041por imagen | −10% | −15% | — | → | Probar | |
 Flux Kontext ProFlux | $0.032por imagen | −20% | −24% | — | → | Probar | |
 Grok Imagine Image 2.0NuevoGrok | $0.040por imagen | — | −5% | — | → | Probar | |
 Qwen ImageQwen | $0.032por imagen | −10% | −15% | — | → | Probar |
Hand over a reference image plus a change request — product-in-context, background swaps and style locks that keep a whole catalogue visually coherent.
Image inputStyle lockPer-image pricing| Modelo | Precio: GPTProto | vs Oficial | vs OpenRouter | Contexto | Modalidades | Estabilidad | Acción |
|---|---|---|---|---|---|---|---|
| Qwen Image PlusQwen | $0.019por imagen | −35% | −38% | — | → | Probar | |
| Nano Banana 2 (Gemini 3.1 Flash Image)Google | $0.040por imagen | −40% | −43% | — | → | Probar | |
| GPT Image 2OpenAI | $6.40por imagen | −20% | −24% | — | → | Probar | |
| Flux Kontext ProFlux | $0.032por imagen | −20% | −24% | — | → | Probar |
Extend a finished visual into motion — short-drama clips, promo films and explainer shots, priced per clip instead of per minute.
Model availabilityDuration controlPer-clip pricing| Modelo | Precio: GPTProto | vs Oficial | vs OpenRouter | Contexto | Modalidades | Estabilidad | Acción |
|---|---|---|---|---|---|---|---|
| Seedance 2.5 (Build 260628)Bytedance | $0.45por clip de 5 s | — | — | — | → | Probar | |
| Seedance 2.0 (Build 260128)Bytedance | $0.30por clip de 5 s | — | — | — | → | Probar | |
| Seedance 2.0 Fast (Build 260128)Bytedance | $0.18por clip de 5 s | — | — | — | → | Probar | |
| Seedance 2.0 Mini (Build 260615)Bytedance | $0.095por clip de 5 s | — | — | — | → | Probar | |
 Kling v3.0 ProKling | $0.27por clip de 5 s | −20% | −24% | — | → | Probar | |
| Veo 3.1Google | $0.50por clip de 5 s | — | −5% | — | → | Probar | |
 Hailuo 2.3 ProMiniMax | $0.44por clip de 5 s | −10% | −15% | — | → | Probar | |
| Sora 2OpenAI | $0.40por clip de 5 s | — | −5% | — | → | Probar | |
| Wan 2.5Qwen | $0.027por clip de 5 s | −10% | −15% | — | → | Probar |
Use your GPTProto key to try a few models, then choose one and enter your expected monthly volume. Start with a small run to measure the cost of your actual prompts and settings.
El video y los agentes gastan créditos más rápido. La recomendación cambia sola al cambiar el escenario o el nivel de uso.
One GPTProto key lets you work across supported image models. A 5,000-image run still needs to be traceable item by item: keep task IDs, check statuses and review failed or uncertain submissions before rerunning them.
Image models run on redundant upstream channels. If one drops mid-batch, traffic shifts to a backup automatically — the run finishes instead of erroring out.
Los modelos principales se sirven por canales redundantes, así que la caída de un proveedor no se lleva tu app.
Ver modelosTraffic is monitored continuously and shifted automatically — problems get routed around before your pipeline ever notices.
Pick a model, set the image options and enter your monthly volume. See how the estimated API spend compares on the same settings.
Publicaciones reales de cuentas públicas. Nada aquí está inventado.
For a team already juggling DeepSeek, Kimi, Qwen, or other providers, a shared routing layer can be reasonable if it removes duplicated work and the fallback behavior is tested. GPTProto is one implementation of this approach.
I've been testing GPTProto recently, and it's honestly made my creative workflow much simpler. Instead of paying for multiple subscriptions, I can access several leading AI models from one place.
I turned this single prompt into a cinematic fantasy video using GPTProto. "Continuous 15-second cinematic shot, 4K resolution, hyper-realistic dark fantasy photorealism…"
I challenged myself to create a cinematic AI cooking short in just 15 seconds. I used GPTProto to bring the entire workflow together, from image generation to video, all in one place.
Made with Seedance 2.0 + GPT Image 2 on GPTProto. A Pixar-style commercial with the perfect glow.
What worked for me was pointing the cloud connections at GPTProto so the frontend only sees one endpoint and I just change the model name to swap. I am not rebuilding a connection from scratch every time.
I route the calls through GPTProto so a fallback is a config switch instead of a weekend rewrite when a model disappears. It turns "my default model just got export controlled" from an incident into a config change.
I used to switch between different AI tools just to compare results. Now I just use GPTProto. GPT-5, Claude, Gemini, Kimi, and more — all in one workspace.
I route the calls through GPTProto so the model id and latency land in one place regardless of which provider is behind it. The win is having the log schema consistent across providers.
I created this 15-second cinematic product video with GPTProto using Seedance 2.0, and I was really impressed by how smooth the workflow was.
GPTProto routes the character prompt and shot list to a top model on one API key: 20 minutes … voices it and burns in captions from the same key: 20 minutes.
Text, images, and voice are configured separately. You can keep OpenRouter for text and use GPTProto for visuals.
The call layer underneath the router is GPTProto, so swapping a model does not require provisioning a new provider integration. Changing models becomes cheap enough that the question stops being "should we change."
You only pay for the images you render. Top up once, spend it on any image model — top-ups of $20+ earn bonus credits.
Get started. Enough to test the endpoint and render a first batch. Top up $20+ anytime to unlock bonus credits.
The sweet spot for a small pipeline running weekly batches.
You get $600 in balance for $500 — bonus credits stack on top of already-discounted model pricing. Built for daily batch loads.
The practical details behind image jobs, model choices and costs.
The documented GPT Image 2 bulk workflow submits image requests through its model endpoint, stores a returned task ID for each request and checks the corresponding result. Do not assume that an OpenAI-style JSONL Batch endpoint is available on GPTProto. See the step-by-step guide for the current workflow.
Yes. Keep one record per product or asset in your own queue, generate the model-specific request for each record and save the task ID with its SKU. Multiple variations of a single prompt and requests for many different products are two different workflows; choose the one your job requires.
Yes, for supported models in your account: one GPTProto key and shared balance let you try different image models. Each model can still have its own endpoint, request fields, output format and billing unit. Check the model documentation before you change a production worker.
Save the asset ID, prompt version, model, settings and returned task ID after each accepted request. Query the task status and store the finished file in your own asset storage. On restart, check known task IDs before creating new jobs.
Follow any rate-limit guidance and reduce concurrent submissions when you receive a 429. A connection timeout on a submission can leave the outcome uncertain; check your task history before resubmitting, because the first request may already have been accepted and billed. Review content or parameter errors rather than retrying them unchanged.
Reference and edit inputs depend on the model and task. They can help guide a look or preserve details, but they do not guarantee identical results across models or across every item. Test representative items and review product fidelity before a full run.
It depends on the model. Some models charge per request or output, while GPT Image 2 is listed with input and output token rates on its model page. Choose your actual size and quality, measure a sample and count retries or rejected results when forecasting production cost.
Some supported OpenAI-compatible routes may need only configuration changes, but do not assume that applies to every model. For example, the documented Midjourney route has a different path. Confirm request fields, status handling and output formats with a sample before migration.
The model-rate comparison and any bonus-credit offer should appear separately. Check the current credit and eligibility terms at checkout; the estimate on this page must show which of the two it includes.
Contact the GPTProto team or check the account billing page for the current invoice request process and supported invoice details.
Practical guides on product-image batches, model selection and image API costs.
Crea imágenes y videos en el navegador, o lleva modelos de texto, imagen y video a tu app con una clave de API. También puedes ver tarifas con descuento.
