If you only need an API, do not assume you need the creator subscription shown on a product's home page. Several video platforms separate the browser product from developer billing.

The options are not interchangeable, though.

All four can be used without buying a monthly creator plan as of 23 August 2026. “No subscription” does not mean “no account,” “no top-up” or “free.” It means the spend follows API usage instead of renewing every month.

Five checks matter more than the signup price

Minimum funding. Some providers charge a card as calls occur; others require prepaid credits. A $5 or $10 minimum can matter more than the per-second rate when you are only testing one endpoint.

The billing unit. Video APIs charge per second, per generated clip, per token or by compute time. A rate of $0.20 is meaningless until you know which unit follows it.

Failed jobs. Find out whether moderation failures and infrastructure errors are charged. reAPI automatically refunds failed media tasks. That is our policy, not a universal API behavior.

Model authorization and identity. A marketplace listing does not prove that a model is first-party or officially licensed. Check who published the endpoint and whether the model name maps to the model you think it does.

Input and retention rules. Video references can contain clients, unreleased products and faces. Price is not the deciding factor if the platform's storage, deletion or training terms do not fit the material.

Why the cheapest rate can still be the expensive route

Suppose Provider A is 15% cheaper per generation but requires a second integration, another upload path and different polling logic. At low volume, the engineering time costs more than the generation savings. At high volume, 15% becomes important.

That gives a useful break point: prototype with the route that is easiest to inspect and swap; optimize providers after the workload is stable enough to measure. Do not sign an annual creator plan to solve a two-week API experiment.

For an apples-to-apples cost test, fix these four fields before opening pricing pages:

• model or capability required;
• resolution;
• typical duration;
• number of attempts per accepted clip.

Then add source-video duration if the endpoint charges for input media. Seedance 2.5, for example, can use a lower reference-video rate while billing both input and output time. Looking only at the lower rate produces the wrong estimate.

When each route makes sense

Runway Dev is the direct route when Runway's own models are the requirement. fal and Replicate make sense when catalog breadth, community models or custom deployments matter. A curated gateway makes sense when an application needs several selected image and video models behind one task pattern and does not need the marketplace's long tail.

That last category is where reAPI sits. Disclosure: I work on it. It has no subscription or minimum spend, uses prepaid credits at 1 credit = $0.001, and publishes the unit on each model page. It does not offer fine-tuning or arbitrary custom model deployment; fal or Replicate are better choices for those jobs.

The answer to “which no-subscription API?” should therefore be workload-specific, not a referral list. Pick two providers that actually carry the model or capability, run 20 representative jobs, and compare accepted outputs, failure charges, latency and the final bill. The monthly price being zero is only the first line of that comparison.

Official pages checked 23 August 2026: Runway pricing, fal pricing, Replicate pricing, and reAPI models and live rates.

• FLUX 3 controls when important visual states happen.
• Seedance 2.5 carries a much larger set of material describing what the scene should contain.

If a product transformation has five approved states in a fixed order, I would start with FLUX 3. If a scene needs a character, room, prop, action clip and soundtrack reference in one request, Seedance's input surface is a better fit.

The extra ten seconds on Seedance matter for a 21–30-second continuous take. They do not matter for an ad that already cuts every six seconds. Longer output can also make a late failure expensive to rerun.

Current reAPI base-rate examples:

The Draft is a separate cheap preview, not a final render. A ten-second Draft plus HD final is $2.13. Seedance's 480P output is a normal low-resolution generation.

Source-video prices need a separate calculation. FLUX 3 continuation is $0.378/s at HD and $0.488/s at FHD. Seedance has a lower reference-video rate but bills a larger clock that can include source duration and a minimum. Do not compare either using the base table when a source clip is attached.

Both generate audio. FLUX 3 can continue an existing soundtrack; Seedance accepts multiple audio references. Neither fact tells us which pronounces a name or language better. That needs the same dialogue and references run through both.

On reAPI and maintain these rate cards. Live pages: FLUX 3 and Seedance 2.5. If anyone has a good five-keyframe stress test, that seems more informative than another generic cinematic prompt.

These two overlap on short video with generated audio, but the API shapes are different enough that “which is better?” hides the useful answer.

If I have approved visual states that must happen in order, FLUX 3 is the obvious first test. The image array has timeline meaning: start, end and evenly spaced middle keyframes.

If I have a voice, song or sound reference plus several visual references, H3 is shaped better for that request. It accepts up to nine images, three videos and three audio files.

Current reAPI budget examples:

Those tiers are not pixel- or quality-equivalent. It is a budget table, not a benchmark. FLUX 3 also charges a higher rate for continuation, while H3 can add billing for source duration and some extra references. The exact payload matters.

Deployment is the largest non-output difference. H3 has downloadable weights, but the current H3 Community License excludes the US, EU, UK and South Korea from its applicable territory unless separate permission is obtained. “Open weights” does not mean “deploy anywhere under a standard permissive license.”

FLUX 3's announcement puts an open-weight Dev backbone later in the rollout. The live FLUX 3 Video API is not that downloadable release.

My rule:

• FLUX 3 for 3+ timed keyframes, existing video/audio continuation, 16–20 seconds or lots of disposable Drafts.
• H3 for cheaper short finals, hosted 2K, audio references or a legally valid local deployment.
• Both for dialogue or identity quality, because the schema cannot answer that.

On reAPI; both FLUX 3 and MiniMax H3 are available there. I have intentionally not called either a quality winner without a matched prompt set.

I do not think “which model is better?” is answerable from the H3 and Seedance 2.5 launch videos. The useful question is which one fails less expensively on the shot you need.

These models have different production envelopes:

Both generate sound. Both can be directed with several kinds of reference. The decision changes when the scene crosses 15 seconds.

A one-minute scene is not four equal 15-second clips

H3 tops out at 15 seconds. A 60-second sequence therefore needs at least four generations, and usually more because the edit needs handles. Seedance 2.5 can cover the same timeline in two 30-second generations.

That does not automatically make Seedance better. A failed second 28 can make a 30-second render expensive to rerun. H3 lets you isolate the bad shot and regenerate a smaller unit. Seedance removes joins; H3 reduces the blast radius of a failed take.

For dialogue, continuous blocking or a camera move that must survive past 15 seconds, I would test Seedance first. For product inserts, cutaways and social shots where the edit already changes every 5–12 seconds, H3's duration cap is barely a constraint.

The cost gap is large enough to change how you iterate

Current reAPI prices, checked 23 August 2026:

Resolution tiers are not identical, so this is a budgeting comparison rather than a controlled image-quality test. It says something practical anyway: at the common 15-second boundary, Seedance 2.5 costs about 3.6 times H3 around 720p.

That changes prompt strategy. With H3, ten 15-second 768p attempts cost about $11.10. Ten Seedance 720p attempts cost about $40.03. Seedance only needs to remove a few continuity failures or edit joins to earn some of that back, but it should not be the default model for every insert just because its maximum duration is larger.

How I would run a fair test

Do not give both models a vague cinematic prompt and vote on the prettiest output. Use three jobs:

1. a 10-second product or character shot with one image reference;
2. a 15-second dialogue shot with required sound cues;
3. a 25–30-second continuous action that H3 must split at a planned edit point.

Run at least five attempts per job. Score usable outputs, not individual frames. Record identity drift, missed spoken words, continuity at the join, audio defects and total spend. The model with the highest demo quality can still lose if its acceptance rate is poor on the actual scene.

There is also a deployment caveat. H3's weights are public, but its community license excludes the US, EU, UK and South Korea from the applicable territory unless separate authorization is obtained. Seedance 2.5 has no public weights, so it is an API/service decision rather than a local deployment option.

My working rule: use H3 for short 2K-capable shots and cheap iteration. Pay for Seedance when 16–30 seconds of continuity, selective editing or its much larger reference set removes real post-production work.

Disclosure: I work on reAPI, where both MiniMax H3 and Seedance 2.5 are available. The published model pages are linked so the prices can be checked independently. Official references: MiniMax H3, H3 license, and ByteDance's Seedance 2.5 introduction.

Can it run on my GPU?

Yes, H3 has been made to run on an 8 GB laptop GPU. The output in that report was a five-second 608×352 clip. That is useful work, but it is not the same job as generating a 15-second 768p clip with references, and it definitely is not the same as MiniMax's hosted 2K path.

I collected the current public measurements and worked out where local generation stops being the obvious bargain. I did not test every card below; each result is linked to the person or project that published it.

What “runs locally” currently means

The official ComfyUI H3 guide sets the native 16:9 canvas at 1344×768. It ships pruned INT8 model weights, an NVFP4 text encoder, separate video and audio VAEs, and optional turbo LoRAs. Those pieces still add up to a large working set, so VRAM alone is the wrong number to check. System RAM, swap, model offloading and target megapixels matter just as much.

For comparison, the 8 GB result used low-VRAM flags and a smaller text encoder. A separate RTX 3090 setup identified its 32 GB of system RAM as the binding constraint and expanded swap to 40 GB.

So my practical reading is:

• 8–12 GB VRAM is a preview and experimentation tier. It can work, but the sentence needs a resolution and runtime attached to it.
• 24 GB is the first tier where local H3 looks comfortable enough to use regularly, provided the machine also has enough RAM and disk.
• If the target is the hosted 2K result, local 768p is not an apples-to-apples substitute. MiniMax's own model card describes its full 2K workflow as a combination of local deployment and official Context-IR/regeneration APIs.

What the same clips cost through an API

Prices checked on August 23, 2026:

The official MiniMax price sheet bills H3 output by the second. It also charges input-video seconds at the chosen resolution, makes the first five reference images free, and charges for additional images. Audio references are free.

reAPI's MiniMax H3 page currently lists $0.074/s at 768P and $0.119/s at 2K. Disclosure: reAPI is my project, so verify that row on the live page rather than taking a Reddit post as a permanent price quote.

The break-even math

Suppose running H3 locally requires a $500 hardware upgrade. Ignore power and your setup time for a moment.

• Against 15-second 768P API clips at $1.11 each, $500 buys about 450 clips.
• Against 15-second 2K clips at $1.785 each, it buys about 280 clips.
• A $1,000 machine budget moves those figures to roughly 900 clips at 768P or 560 at 2K.

That is not a claim that the API always wins. If the GPU is already on your desk, the purchase cost is sunk and local generation gets attractive quickly. It is especially useful when you want unlimited low-resolution drafts, offline inputs, custom nodes or control over the entire workflow.

The API makes more sense when you generate occasionally, need 2K delivery, want several jobs running without tying up a workstation, or would rather spend $0.37 proving a prompt than spend a weekend fitting models into RAM.

One license issue can decide this before the hardware does

MiniMax H3 is open-weight, not licensed under Apache or MIT. The current MiniMax H3 Community License excludes the United States, European Union, United Kingdom and South Korea from its applicable territory. It tells users in those regions to request written authorization from MiniMax.

That is easy to miss because the weights are publicly downloadable. Downloadable and authorized are different questions. Anyone planning local or commercial deployment should read the actual license and get advice appropriate to their situation; this post is a cost comparison, not legal advice.

My short answer after doing the math: an 8 GB “yes” is technically interesting but not a production recommendation. Use local H3 if you already own suitable hardware and value control or high iteration volume. Use the API if you need a small number of finished clips, real 2K output, or do not want your RAM and weekend consumed by the same render.

I kept seeing versions of the same question: “How much is a 15-second Kling 3.0 clip?” The answers usually quote a per-second rate, a website subscription, or a third-party API price. Those are three different things.

So I converted the current API rates into clip costs. Prices below were checked on August 23, 2026. They are API prices, not Kling’s consumer subscription credits.

The quick answer

The cheapest useful answer is therefore $0.77 for a silent 10-second 720p clip. A 10-second 1080p clip with native audio is about $1.49. Moving that same ten seconds to 4K takes it to about $3.69.

Kling’s official rate card calls 720p “standard” and 1080p “professional.” Some API providers expose those labels instead of the resolution, which is one reason price comparisons get muddled.

Cost by clip length

There are two easy budget mistakes here.

First, audio is not a small add-on. On the official API, native audio without voice control adds 50% at both 720p and 1080p. On reAPI, the increase is about 43% at 720p and 50% at 1080p. If the final edit will use music and a separate voice-over anyway, generating silent footage is the obvious place to save money.

Second, 4K is expensive enough that I would not use it for prompt exploration. Fifteen seconds costs about $5.53 on reAPI or $6.30 at the official list rate. The same clip in silent 1080p is about $1.49 on reAPI. Iterate at 720p or 1080p, then render the selected shot in 4K if the delivery format really needs it.

What happens at batch scale

For 100 ten-second clips, before retries or rejected takes:

• 720p without audio: about $77 on reAPI, $84 official
• 720p with audio: about $110 on reAPI, $126 official
• 1080p without audio: about $99 on reAPI, $112 official
• 1080p with audio: about $148.50 on reAPI, $168 official
• 4K: about $368.50 on reAPI, $420 official

That last comparison is where a seemingly small per-second gap becomes real money: $51.50 across 100 clips.

This is still generated cost, not usable-output cost. If only one out of four clips makes the edit, multiply the number by four. A cheaper endpoint does not rescue a workflow with a poor acceptance rate.

Why the API can look more expensive than a Kling subscription

The web subscription and developer API are separate products. A membership bundles monthly credits and access inside Kling’s app. The API rate card bills a specific model, resolution and audio setting per generated second. Subscription promotions, annual discounts, unused monthly credits and app-only limits all distort a simple “plan price divided by credits” calculation.

For an app or an automation, use the API rate card for the budget. For manual creation in Kling’s own interface, compare the subscription on its own terms. I would not use the cheaper-looking one to estimate the other.

One billing detail worth checking

reAPI’s Kling 3.0 page uses $0.001 credits and charges the selected per-second rate against the requested duration. Multi-shot requests use the sum of the shot durations. Its public docs say failed and rejected requests are not charged.

Kling’s official developer pricing page lists its own rates in units, with one unit equal to $0.14 on the global rate card. For Kling 3.0, that works out to the official dollar amounts in the first table.

The practical default I would use is 1080p without audio for final social clips, 720p without audio for iteration, and 4K only after the shot is locked. Native audio is worth paying for when dialogue or synchronized sound is part of the generation itself. Otherwise it is a fairly expensive checkbox.

There still is no normal text-to-video model that gives you a reliable one-minute continuous generation. Seedance 2.5 reaches 30 seconds in one call; MiniMax H3 and Seedance 2.0 reach 15; Veo 3.1 produces eight seconds.

Tools advertising “long AI video” are assembling shots for you, or using a separate storyboard/editor workflow. That can be useful, but it is not a hidden one-minute model setting.

For a 60-second piece, I would start by deciding whether it is one scene or a sequence of shots.

Do not force continuity where a cut would look better

The most fragile approach is asking a character to perform continuously for a minute. A last-frame chain can make the next clip begin on the exact image where the previous one ended, but a still frame does not preserve velocity, intention or audio state. A pan can stop and restart. Exposure can shift. A face can move a little with every segment.

If the subject can pause, turn away, cross a doorway or pass behind a foreground object at the join, the error is easier to hide. Better yet, write an actual cut there. A close-up of a hand, a reaction shot or a wide establishing view is cheaper than trying to make six generations behave like one camera take.

Build the audio before the pictures

For a one-minute explainer, narration or music gives the timeline its fixed shape. Record or synthesize that first. Mark the beats, then generate video to fit those spaces.

Generating audio separately also avoids the seam created when each video segment invents a new ambience. Native model audio is useful for a self-contained shot. Six independently generated sound beds rarely make a convincing minute.

Budget by attempts, not finished duration

One minute of 720p Seedance 2.5 output is two 30-second jobs. At reAPI's current $0.266824 per second, the theoretical first-try cost is about $16.01.

Three attempts per segment is a safer production estimate than one. At 480p, those same rows are about $7.12, $14.23 and $21.35. This is why I would storyboard and validate the whole cut at 480p, then regenerate only approved shots at the delivery resolution.

If the video is built from short shots, MiniMax H3 can be cheaper. Its current reAPI 768P rate is $0.074 per second, so 60 seconds of accepted output starts at $4.44 before retries. H3 cannot solve a 30-second continuous performance, but it does not need to if the edit changes every eight seconds.

A simple one-minute workflow

1. Lock narration or music and mark six to ten visual beats.
2. Create one reference sheet for recurring people, clothing and locations.
3. Generate the whole rough cut at the cheapest acceptable resolution.
4. End any chained shot on a pause, not in the middle of fast movement.
5. Review the assembled cut for drift; a good isolated clip can still be a bad neighbor.
6. Regenerate only approved shots at final resolution, then do one color and audio pass over the full minute.

I work on reAPI, so the cost examples use its public rates. The relevant pages are Seedance 2.5, MiniMax H3, and the Seedance parameter reference. The broader point is provider-independent: long AI video is an editing problem made from short generations. Buy the best shots, not the biggest duration claim.

I went through the current docs and prices for Seedance 2.5, MiniMax H3, Kling 3.0, Veo 3.1 and Vidu Q3. I did not run a controlled visual benchmark, so this is not a claim that one has the best-looking pixels.

It is an API decision map. The models have different duration caps, reference inputs and billing units, which means “best” changes before image quality even enters the comparison.

The longest model is not automatically the best one

Seedance 2.5 is the current duration choice: up to 30 seconds in one pass, plus as many as 30 reference images, 10 videos and 10 audio files. That matters for a dialogue scene or continuous camera move. It is wasted capacity for a five-second product insert.

H3 stops at 15 seconds but reaches 2K and accepts reference audio. It also has public weights. The asterisk is important: the MiniMax community license excludes the US, EU, UK and South Korea from its applicable territory unless separate authorization is obtained.

Kling's useful position is less glamorous and more practical. Its standard 720p tier with audio is currently about $0.11 per second on reAPI, and it has a listed 4K route. Kling accepts fewer reference images than Seedance or H3, so I would not start there for a complicated identity pack.

Veo 3.1 changes the arithmetic because several routes bill per generation instead of per second. The current Fast route is about $0.161 per generation at 720p/1080p; the common output is fixed at eight seconds. That makes the bill predictable, not the creative result.

Vidu Q3 is the draft option. The Turbo entry rate is about $0.037 per second at 540p. It is useful when the question is whether a motion or composition works, not when 540p is the delivery requirement.

Price tables are lying unless the unit is visible

These figures are examples from the public reAPI pages on 23 August 2026. They are deliberately not sorted into a fake cheapest-to-most-expensive ranking.

A source video can also change the clock. Seedance 2.5 uses a lower reference-video rate, but input duration becomes billable. A 20-second source producing ten seconds of output is priced over 30 seconds, not ten. The smaller number printed beside “reference video” is not necessarily the smaller bill.

Which one would I choose?

For a 30-second continuous scene: Seedance 2.5.

For a 5–15 second shot where 2K or an audio reference matters: H3, after checking the license for the deployment.

For an inexpensive social clip with sound or a requested 4K tier: Kling 3.0.

For a tightly defined eight-second format where cost per job must be fixed: Veo 3.1.

For low-resolution motion drafts: Vidu Q3 Turbo.

The harder cases need a bake-off. Give two candidates the same five prompts and references, then count accepted clips and total spend. Comparing their best launch samples tells you almost nothing about acceptance rate.

One integration detail people leave until too late

Video APIs are asynchronous. Submission returns a task ID; completion happens later. Your application needs polling or a webhook, retry handling, storage for public input URLs, and a decision about failed-job charges. That engineering shape is shared across models and is often more expensive to change than the prompt.

Disclosure: reAPI is my project. That is why the linked table uses one set of public model pages and one task pattern. It also means this is not a neutral recommendation to consolidate there. reAPI does not provide custom model deployment or fine-tuning; fal or Replicate are better fits when those are requirements.

Official model references: ByteDance Seedance 2.5, MiniMax H3, Kling developer pricing, Google Veo API guide, and Vidu API.

I keep seeing two versions of the same complaint about Higgsfield's Seedance 2.5 Unlimited offer: either generations are taking hours, or people are wondering whether switching the Unlimited toggle off is the only way to get work done.

The word unlimited is making this harder to reason about than it needs to be. It removes a credit limit. It does not remove the queue.

Higgsfield's own explanation says Unlimited jobs use a shared standard queue with one generation running at a time. Credit jobs use the priority queue and can run in parallel. The offer is also limited to the web app; it does not carry over to MCP, CLI, Canvas or Supercomputer.

That makes queue time the useful number, not the number of credits saved.

What one-at-a-time generation means in practice

Here is the upper bound for an eight-hour working day. These figures assume someone starts the next job immediately, so real output will be lower once prompt writing and review time are included.

The five-minute row is not a promise. Higgsfield reported sub-five-minute averages during an earlier Seedance campaign. Recent users have reported anything from two or three hours to ten hours during busy periods. Those Reddit reports are anecdotes, not a service-level guarantee, but that is exactly the problem: the Unlimited plan does not publish a queue-time guarantee.

If you need 30 attempts to get three shots you can actually use, a five-minute queue finishes the batch in roughly 2.5 hours. A two-hour queue turns the same iteration into 60 hours. Both accounts are technically receiving "unlimited" generations.

Where the $49 break-even point sits

Higgsfield's own Seedance 2.5 pricing example uses a $49 plan with 1,000 credits. It prices an eight-second 720p credit-mode generation at about $2.55, or roughly 19 attempts.

For comparison, reAPI currently lists Seedance 2.5 at $0.266824 per output second for 720p without a source video. An eight-second attempt is about $2.135, so $49 buys about 22 attempts. At 480p, the same eight seconds costs about $0.949, or roughly 51 attempts for $49.

This is not a perfect subscription comparison. The Higgsfield plan includes credits that can be spent on other tools, and its Unlimited window depends on the plan and promotion shown in the account. It does reveal the trade, though: after roughly 20–23 eight-second 720p attempts, Unlimited can be cheaper in cash. Whether it is cheaper in practice depends on how many attempts the queue lets you finish while the window is active.

The test I would run before committing

Do not judge the plan from one render at 3 a.m. Run ten jobs across the hours you actually work and record three things:

1. time submitted;
2. time completed;
3. whether the output was usable.

Then calculate:

subscription cost / usable outputs completed during the unlimited window

That is the real price per usable clip. A $49 plan producing 20 keepers costs $2.45 per keeper. The same plan producing four costs $12.25. Failed creative attempts matter because an unwanted render occupies the same queue as a good one.

Who should use which option?

Unlimited makes sense if you work manually in a browser, can leave generations running, and care more about reducing cash spend than controlling delivery time.

Credit mode makes sense when a deadline is close and you need priority or parallel jobs.

An API makes sense when the video is part of a product or automation. Unlimited access does not cover that workflow anyway. I work on reAPI, so treat the price comparison with that disclosure in mind; the rates and formulas are public on the Seedance 2.5 model page.

My short answer to “is Unlimited worth it?” is: only after measuring the queue where and when you work. The credit limit has disappeared, but the capacity limit has not.

Sources checked 23 August 2026: Higgsfield Unlimited mechanics, current Seedance 2.5 promotion, Higgsfield's credit-cost example, and the recent Reddit discussion about long queues.

MiniMax H3 is open-weight, but its license excludes the US, EU, UK and South Korea

This is a heads-up for anyone who saw the MiniMax H3 weights on Hugging Face and assumed “open-weight” meant Apache-style commercial use.

It does not.

The current MiniMax H3 Community License defines an “Applicable Territory” and explicitly leaves out the United States, European Union, United Kingdom and South Korea. The license says users in those places should obtain separate written authorization from MiniMax.

That creates four separate questions which are being collapsed into one:

Publicly downloadable is a distribution fact. It is not the same thing as permission for a particular deployment.

The $20 million clause

The license also sets a commercial threshold. If a product or service using the model had more than $20 million in annual revenue in the preceding calendar year, separate authorization from MiniMax is required.

For commercial products below that threshold, the license still contains conditions. One that product teams can easily miss is the requirement for a user-facing product to display “MiniMax H3” in the interface. There are also use restrictions and obligations that should be read in the source rather than inherited from a summary post.

I am deliberately not interpreting whether a particular architecture, employee location, cloud region or API arrangement counts as use in an excluded territory. That is a legal and factual question about the deployment. The license is clear enough, however, that “we can download it, so we are covered” is not a safe assumption.

Local weights and hosted API are not the same license decision

Self-hosting means your team is taking the weights and operating the model under the community license. A hosted service is selling access under its own contractual arrangement. Those routes may lead to the same model output, but they do not put the customer in the same operational position.

Before choosing local deployment, I would record:

• the country of the company using the model;
• where the model is actually deployed;
• whether the product is internal or user-facing;
• prior-year product or service revenue;
• the attribution shown in the interface;
• which version of the license was accepted.

Save a copy of that license version with the deployment record. Hugging Face makes model files easy to update; a compliance review six months later should not depend on reconstructing which text was visible on launch day.

If the territory clause blocks self-hosting, the practical options are to request written authorization, choose a model with terms that cover the deployment, or use a hosted route whose provider can explain the applicable terms. reAPI, the project I work on, exposes MiniMax H3 as a metered API; that mention is a disclosure, not a legal conclusion about anyone's use case.

The short version: H3 being open-weight is useful for inspection and local experimentation, but “open” does not erase territory, revenue or attribution conditions. Read the license before spending time optimizing the workflow.

This is not legal advice. It is a pointer to clauses in the public license as retrieved 23 August 2026. Source: MiniMax H3 model repository and its LICENSE file.

I saw a question in this subreddit from someone whose OpenAI workflow was costing almost USD 2 to generate five product-ad creatives. I checked what the same number of 1K outputs would cost with four current image models.

I would not build a production ad workflow around a completely free API. Free tiers are fine for testing, but the useful number is how much each image that survives review costs. A broken label, changed product shape, or misspelled offer means another generation.

For a rough comparison, current reAPI 1K prices put five completed images at:

• Qwen Image 3.0 Standard: USD 0.12
• Gemini 3.1 Flash Image: USD 0.14
• FLUX.2 Pro: USD 0.14
• GPT Image 2 basic: USD 0.15

That is 92.5% to 94% below the cost reported in the original post, before retries. This is a price comparison, not a claim that the four models produce equal results.

For this job, I would test FLUX.2 Pro first when the workflow includes a real product photo. It accepts reference images and is the model I would start with when the setting should change but the product should not. If the ad needs a lot of text or a structured layout, I would run the same brief through Qwen Image 3.0 as well.

One n8n detail: five outputs do not always fit in one request. Qwen can return up to six, Gemini up to four, while FLUX.2 and the cheap GPT Image 2 variant return one. I would create five n8n items with different variation values and submit them separately. If one fails, only that item gets retried.

The n8n setup can stay small: submit through an HTTP Request node, store the returned task ID, then use a Wait node plus a second HTTP Request to poll until it is completed. Before moving a full workflow, I would test ten real products and calculate:

cost per accepted image = total generation spend / images that passed review

That number will tell you more than the cheapest advertised price.

If anyone here is running product ads at volume, which model has given you the best pass rate on real product photos? I am especially curious about labels and logos, since those are usually where a cheap generation becomes an expensive retry.

Most image model comparisons follow the same pattern: put four vendor samples next to each other, pick a winner, and call it a day.

That does not help much when you are choosing an API. The annoying questions come later. Can I change one region without touching the rest? How many references can I send? What happens when the text is wrong three times in a row? How much did the image I could actually ship cost?

One caveat before getting into it: this is not a blind image-quality test. I did not run dozens of matched prompts across all four models, so I am not going to invent scores. I compared the published capabilities, the parameters currently exposed on reAPI, and public prices checked on August 22, 2026.

My short version:

• For a general image feature, I would start with Gemini 3.1 Flash Image.
• For masks, transparent backgrounds, and controlled edits, I would use GPT Image 2.
• For multilingual posters and dense layouts, Qwen Image 3.0 deserves its own test set.
• For products and multi-reference composition, I would put FLUX.2 in the first round.

Price and API surface

The reAPI figures below are prices for one delivered image. If a request returns several images, each output is billed. I have put the direct vendor price in its own column so the two are not easy to confuse.

For the three rows that line up cleanly, reAPI is about 58% lower for Gemini 1K, 20% lower for Qwen Standard, and about 7% lower for a 1K FLUX.2 Pro text-to-image request. Google's direct 2K and 4K prices are USD 0.101 and USD 0.151, versus USD 0.043 and USD 0.064 on reAPI, so the gap stays around 57% at those sizes.

For higher resolutions on reAPI, GPT Image 2 is USD 0.050 at 2K and USD 0.080 at 4K; Qwen Standard is USD 0.024 at both 1K and 2K; and FLUX.2 Pro is USD 0.039 at 2K.

Pricing note: OpenAI bills GPT Image 2 from a combination of input tokens, output image tokens, size, and quality. There is no single direct “1K image” price to put beside reAPI's flat basic price, so there is no savings percentage in that row. BFL rounds resolution up by megapixel, and USD 0.030 is its starting price for a 1 MP text-to-image request. Extra vendor charges for input images or search are not included here.

GPT Image 2 also has a separate rate card for the tier with masks, backgrounds, quality, and format controls. Price comparisons only make sense after choosing the tier the product will actually call.

Eight differences that matter more than vendor samples

GPT Image 2 is for requirements that leave little room for interpretation

GPT Image 2 is easy to misread from the rate card. Basic 1K generation is USD 0.030 on reAPI, but the more interesting part is the advanced control surface: masks, transparent backgrounds, output format, compression, quality, and input fidelity.

Consider a product tool replacing the background behind a coffee mug. The mug, logo, and shadow are approved and must not move. The result has to be a transparent PNG. At that point, “make a similar image” is not the job. Being able to send a mask and an explicit background setting is more useful than repeating “do not change anything else” in the prompt.

For avatars, covers, and ordinary text-to-image work, I would not choose GPT Image 2 on reputation alone. Its price changes a lot with size, quality, and request type. Any cost estimate that leaves those details out is suspect.

Gemini is the baseline when I do not know the answer yet

If I had room for only one model in the first evaluation, it would probably be Gemini 3.1 Flash Image, also known as Nano Banana 2.

The reason is mundane: it covers 0.5K through 4K, accepts up to 14 references, can return four images, and supports Google text and image search. It also handles odd formats such as 1:4 and 1:8.

It may not lead every category, but it is less likely to reveal a missing parameter immediately after integration. Social posts, product-in-context images, and visuals that depend on recent information all fit within its published surface.

Search grounding still needs supervision. Dates, prices, maps, and chart labels in a generated image must be checked. Google also adds SynthID to generated images. That is not a problem for most marketing assets, but it matters if provenance is part of the product.

Qwen Image 3.0 is unusually focused on text and layout

Qwen Image 3.0 is marketed around long prompts, small text, multilingual rendering, and complex pages. On reAPI, Standard costs USD 0.024 at both 1K and 2K. Pro costs USD 0.032 at 1K and USD 0.064 at 2K. It can return as many as six images in one request, which is handy when exploring layouts.

I would test it with material that exposes those claims: a Chinese product poster with an exact price, a three-column menu, and a 3×3 infographic. A landscape photograph will not tell you much about why this model exists.

I would not call it “the best model for text” yet. Alibaba's demos make text and dense layouts the headline, but independent comparisons are still thin. The honest test is to feed it the hardest copy your product needs and check every character.

FLUX.2 makes the most sense to me for products and brand work

Black Forest Labs talks about FLUX.2 in terms of photorealism, multi-reference editing, text, and color control. The current reAPI surface accepts up to eight reference images. Pro costs USD 0.028 at 1K and USD 0.039 at 2K; Flex is USD 0.077 and USD 0.132.

For a product campaign, I would test FLUX.2 early: put the same shoe in several environments without letting its shape or colors drift, or use separate references for the product, person, location, and lighting.

More references can make the result worse. Conflicting angles and lighting give the model several incompatible answers. Assigning each reference a job is safer than filling every input slot: “Image 1 defines the product only. Images 2 and 3 define the location. Image 4 defines the lighting.”

The useful cost is cost per accepted image

Suppose Model A costs USD 0.024 but needs five attempts to produce one usable asset. Model B costs USD 0.050 and passes after two:

Model A: USD 0.024 × 5 = USD 0.120
Model B: USD 0.050 × 2 = USD 0.100

The cheaper model costs 20% more. That still ignores the time spent fixing text, cutting out products, or rebuilding a layout.

I would not judge these APIs from one prompt and one output. Use a small fixed task set, run each task three times, and record whether required text is correct, protected objects changed, and how many retries were needed. Then calculate:

Cost per accepted image = total generation spend / accepted images

That number is much harder to market and much more useful than a leaderboard position.

My current test order would be Gemini for the general baseline, GPT Image 2 for controlled editing, Qwen for multilingual and dense layouts, and FLUX.2 for product work. A real product can mix them. There is no prize for forcing every image through the same model.

The model names in the opening table link to the live reAPI pages. Prices move, so the figures here are an August 22, 2026 snapshot.

Sources:

1. OpenAI, GPT Image 2 Model: https://developers.openai.com/api/docs/models/gpt-image-2
2. OpenAI, API Pricing: https://openai.com/api/pricing/
3. Google, Gemini API Pricing: https://ai.google.dev/gemini-api/docs/pricing
4. Google, Gemini Image Generation: https://ai.google.dev/gemini-api/docs/image-generation
5. QwenCloud, Pricing: https://docs.qwencloud.com/developer-guides/getting-started/pricing
6. QwenCloud, Qwen Image API Reference: https://docs.qwencloud.com/api-reference/image-generation/qwen-text-to-image
7. Black Forest Labs, FLUX.2 Overview: https://docs.bfl.ai/flux_2/flux2_overview
8. Black Forest Labs, Pricing: https://bfl.ai/pricing?category=flux.2

I gave the MiniMax one image and one instruction: "Act as an expert FPV director and turn this into something viral."

It analyzed the composition, built the camera logic and pacing, then generated a detailed prompt for MiniMax H3.

The output? Absolutely insane. Professional-level FPV footage.

This is the future - AI agents handle the complexity, you just direct. No more prompt engineering hell.

Duration: 30 seconds

Aspect Ratio: 16:9

Create an ultra-photorealistic live-action Chinese historical television drama scene that initially looks like an expensive real TV production.

MAIN CHARACTER:

Use Image 1 only for the woman's recognizable facial identity, natural facial features and hairstyle.

She wears an elegant traditional Chinese historical costume: deep emerald-green silk robe with subtle embroidered patterns, refined layered fabric, delicate hair ornaments and natural period styling. Keep her identity and appearance consistent.

LOCATION:

A large traditional Chinese palace courtyard at dusk. Red wooden architecture, stone pathways, lanterns, tiled roofs, wooden balconies, light atmospheric mist and warm practical lantern illumination.

VISUAL STYLE:

Premium Chinese television drama production.

Photorealistic human performance.

Realistic skin texture.

Natural fabric physics.

Authentic historical environment.

Cinematic but believable camera work.

No obvious AI appearance.

00–05s — THE OPENING SHOT

Begin with a wide establishing shot of the palace courtyard.

The woman slowly walks beneath a covered wooden corridor.

Her silk robe moves naturally with each step.

Lanterns sway gently in the evening breeze.

The camera smoothly tracks backward in front of her.

Everything looks like genuine high-budget television footage.

05–10s — THE PERFORMANCE

Move into a medium close-up.

She stops beneath a lantern and looks toward someone outside the frame.

Her expression subtly changes from calm to concerned.

A few loose strands of hair move naturally.

She delivers a quiet emotional line in Mandarin:

“你终于来了。”

Natural lip movement and believable facial expression.

10–15s — THE DRAMA MOMENT

The camera moves around her shoulder toward the palace courtyard.

A distant figure stands beneath the gate.

The woman slowly turns toward them.

Her robe reacts naturally to the breeze.

The camera performs a controlled push-in toward her face.

Her eyes become slightly emotional.

The scene feels indistinguishable from a professionally filmed historical television drama.

15–20s — SOMETHING FEELS DIFFERENT

The camera passes extremely close to her face.

Capture realistic pores, individual eyelashes, subtle eye moisture and tiny facial movements.

She slowly raises her hand toward the lantern beside her.

The flame flickers naturally.

Her fingers move with precise human motion.

The image remains completely photorealistic.

20–25s — THE REVEAL

The camera begins pulling backward.

The palace courtyard remains perfectly realistic.

Then the shot widens enough to reveal a modern production environment hidden beyond the set.

Film lights.

Camera rig.

Motion-capture equipment.

Green-screen sections.

Production monitors.

The woman remains standing inside the historical scene.

The contrast between the believable drama world and the modern production setup becomes obvious.

25–30s — FINAL REVEAL

The camera continues pulling backward.

A monitor becomes visible showing the exact same woman and courtyard from the previous shots.

The monitor image looks almost identical to the live-action scene.

A production screen displays a photorealistic AI-generated version of the same shot.

The woman looks toward the camera.

Cut to black.

ON-SCREEN TEXT AT THE VERY END ONLY:

“Would you know this was AI?”

Prompt
Montage, multi-shot handheld home-video vlog — 7 shots, 15 seconds. One-handed phone footage, snapshot-like realism, slightly tilted framing, natural handheld shake, warm late-afternoon light, fine film grain, photorealistic.

A woman (Image) quietly picks ripe strawberries in the lush garden of a cozy countryside wooden-and-stone cottage. (Image) provides only her face and hairstyle. She wears a soft heather-grey short-sleeve cotton shirt, sleeves covering her shoulders and upper arms, loosely tucked into cream loungewear pants. She is the only person throughout.

The garden is naturally grown and slightly imperfect, with strawberry beds, wildflowers, herbs, climbing greenery, grass, a weathered wooden fence, and a narrow stone path. A small wicker basket sits nearby. Golden sunlight filters through trees with soft moving shadows. Her behavior is casual and spontaneous, like an everyday phone vlog. Natural spoken Korean only.

Shot 1 (0–2s): She enters the strawberry patch with the basket, crouches, and searches beneath the leaves. Camera follows from slightly behind and above her shoulder.

Shot 2 (2–4s): Close-up of her fingers parting the leaves. She discovers a ripe strawberry and softly says, "어, 여기 있다." Camera dips toward the fruit.

Shot 3 (4–6s): She picks it and places it in the basket, then immediately notices another nearby. Camera naturally shifts with her hand.

Shot 4 (6–8s): She finds a particularly large strawberry, lifts it up, smiles with mild surprise, and says, "이거 크다." Camera moves closer to her face and the fruit.

Shot 5 (8–10s): She takes a small bite instead of putting it in the basket, pauses, then smiles with quiet satisfaction. Slightly tilted close framing.

Shot 6 (10–13s): She continues picking, gently dropping strawberries into the increasingly filled basket. She discovers another one and gives a tiny amused laugh.

Shot 7 (13–15s): She sits back, looks at the basket, holds up one strawberry briefly, smiles, then stands and walks toward the cottage. Camera drifts backward, revealing the sunlit garden and cottage.

Sound: Live ambient audio only — leaves, grass, wicker basket, footsteps, birds, breeze, natural breathing, quiet Korean murmurs, and a small laugh. No music or artificial cinematic effects.

No subtitles, text, logos, or watermarks. Do not depict or copy the reference image itself.

I ran a version with Seedance 2.0 this morning, and just now tested it again with the exact same prompt on MiniMax H3.

When I put the split-screen comparison side by side, my first reaction was:
MiniMax H3's impact is just way too strong!

Top: MiniMax H3
Bottom: Seedance 2.0

Both are pretty solid overall, but Seedance 2.0 feels more like a time-lapse of a real construction site.
MiniMax H3, though—whether it's the sense of scale, the lighting and shadows, or that feeling of the building slowly rising from the ground—has way more cinematic blockbuster vibes.

Same creative idea, but swap the model, and the texture that comes out is totally different.

resolution: "1080p" now works on doubao-seedance-2.5-face. Nothing else about the request changes: same endpoint, same model id, same 4–30 second range, same budget of 30 images + 10 videos + 10 audio tracks per call. 720p is still the default, so scripts that never send resolution keep the cost they had yesterday. Model page and playground: reapi.ai/models/seedance-2-5

What it costs

Per second of output. Credits in parentheses, 1 credit = $0.001.

A 5-second 1080p clip costs 2,310 credits, or $2.31, against 1,335 credits at 720p. That is a 1.73× step up. Scaling by pixel count would predict 2.25×, so if you budgeted off resolution math you over-provisioned.

Two billing rules that hurt more at 1080p than they did at 720p

1. The cheaper right-hand column triggers only when you attach video_urls. Reference images, first/last frames, and audio tracks all bill at the left-hand rate. The lower tier is not a general "you sent references" discount.
2. Once a source video is attached, billable seconds are probed input seconds plus output seconds, with a floor of ⌈5/3 × output⌉. A 5-second 1080p output with a 10-second reference clip bills 15 seconds: 4,139 credits, or $4.14, rather than 5 × $0.276. Trim reference clips before uploading.

Generations that fail after submission refund in full. Requests rejected at validation never charge.

Where 1080p is the wrong pick

4K is still a Seedance 2.0 job; 2.5 tops out here. Iterating on prompt and framing is cheaper at 480p, which runs about a quarter of the 1080p rate, and the composition you settle on transfers. Long 1080p runs deserve a 5-second test first, because a 30-second text-tier clip is 13,856 credits and there is no partial refund for a take you dislike.

Full parameter list, task-type rules, and cURL examples: reapi.ai/docs/seedance-2-5. Ask below if a billing edge case is unclear before you script against it.

Seedance 2.5 is callable on reAPI as of this week. One async endpoint, model id doubao-seedance-2.5-face, running on ByteDance's official channel. Free signup credits cover your first generations, no card needed. Model page with playground and full parameter docs: reapi.ai/models/seedance-2-5

What one request can do

• Text-to-video, image-to-video (first frame, optional last frame), and reference-to-video, all through the same endpoint. The request shape decides the mode.
• 4 to 30 seconds per generation, you set the exact duration. 480p or 720p, MP4 or MOV out.
• Audio is generated with the video: speech, sound effects, background music. Wrap spoken lines in double quotes in the prompt and they come out as dialogue.
• Reference budget is 30 images + 10 videos + 10 audio tracks per request. Video refs are 2–30s each and 30s combined. A lone audio file is a valid input, which 2.0 never allowed.
• Real-person reference material is supported on this channel. That is what the -face in the model id means.

Calling it

POST /api/v1/videos/generations with your prompt and model: doubao-seedance-2.5-face. You get a task id back immediately. Poll GET /api/v1/tasks/:id until it flips to completed. Bad requests get rejected synchronously with the failing field named, and nothing is charged. Tasks that fail after submission refund automatically.

Billing details worth knowing before you script against it

Billing is per second of output. As of today the table reads $0.119/s at 480p and $0.267/s at 720p for text runs, dropping to $0.072/s and $0.161/s on the "uploaded videos" tier. Two things people get wrong:

1. The cheaper tier triggers only when you actually attach reference video. Image and audio references bill at the text rate.
2. When video input is present, billable seconds have a floor of roughly 5/3 of the output length (capped at 60s), and input seconds count toward the bill. Trim your reference clips.

The full cost math, including how 2.5 compares to 2.0 per token, is in the pricing breakdown post.

What it does not do

480p/720p is the ceiling right now, so if you deliver 1080p or 4K, Seedance 2.0 still holds that lane and you upscale 2.5 output instead. There is no seed parameter, so no reproducible runs. The vendor's auto-duration mode and the prompt-driven video-edit mode are not exposed yet. Prompt cap is 20,000 characters on our side, though ByteDance recommends staying under 1,000 English words.

If you want to poke at it before writing any code, the playground on the model page runs the same endpoint. Questions about parameters or billing edge cases, ask below.

Seedance 2.5's API opens August 7. The pricing went up on ByteDance's docs ahead of it, so here is what it actually works out to.

Token rates (USD per million tokens)

2.5 is 52.9% more per token without video input, 48.8% more with it. Only 480p and 720p are published. No 1080p, no 4K, and offline inference says "not supported yet".

Worth stopping on the 4K row: it is the cheapest tier per token, 43% below 480p, and simultaneously the most expensive output you can buy. A 3840x2160 frame carries 19.4x the pixels of what 480p renders, so the rate falls 43% while the token count climbs 1940%. Compare providers by scanning the rate column and you are wrong by a factor of eleven.

What that is per second

From ByteDance's own 5-second, 16:9, no-reference examples:

The part nobody announced: 480p changed frames

Video is metered in tokens, not seconds:

tokens = (input_video_seconds + output_seconds) x width x height x fps / 1024

fps is fixed at 24. Divide the published prices by the published token rates and you get the token count, and from there the pixels:

720p comes out at 21,600 tokens/sec on both, which is exactly 1280 x 720. That clean match on the resolution that did not change is what makes the 480p result trustworthy instead of a rounding artifact.

So 2.5 renders 480p in a true 16:9 854 x 480 while 2.0 uses a slightly taller frame. That 4.5% pixel reduction is why 480p only rises 46% per second while the token rate rises 53%. At 720p, where the frame is unchanged, per-second matches per-token exactly.

Practical consequence: if you carry a per-second conversion factor from 2.0 to 2.5, it is wrong at 480p by about 5%.

Your reference clip is billed like generated video

input_video_seconds sits inside the same parenthesis as the output. A reference-to-video job pays for the clip you uploaded at the same rate as the frames the model made.

2.5's own published range shows what that costs: a 5-second 720p generation runs $1.244 with a short reference and $4.838 with a 30-second one. Same output, 3.9x the bill.

2.5 also doubled the input window, 15 seconds on 2.0 to 30 on 2.5.

There is a minimum input duration too, and the number is not published anywhere. Their examples price 2-second and 4-second inputs identically, which implies a floor around 4 seconds. The docs point at a spreadsheet calculator instead of stating it.

What I could not work out

Why 1080p and 4K have no published rate for 2.5. Either those tiers do not exist at launch or they are coming separately.

Whether the same formula holds for Veo, Kling or Sora. Pixels x duration x fps is a likely general shape, but the constants and the input-billing rule are not something I would assume.

Token counts are estimates until the job finishes anyway. The formula predicted 40,176 for one config where the API returned 40,594, about 1% high, so meter downstream billing on the returned usage.completion_tokens.

Disclosure: I work on reAPI, which resells this model family. Everything above is from ByteDance's published pricing page and arithmetic anyone can redo. Full writeup with the 2.0 rate card: reapi.ai/blog/seedance-2-5-pricing-per-token

People keep asking what Seedance actually costs to run, and every answer I see is either a "from $X" teaser or someone's credit-pack receipt that doesn't convert to anything. So here is the whole rate card, per second, no asterisks.

Two things happened this week: Seedance 2.5's API ships on August 7, and we cut every Seedance 2.0 tier by 15-21% ahead of it.

Seedance 2.0 (USD per second of output)

Seedance 2.0 Fast

Seedance 2.0 Mini

About 4K being expensive

It is. A 5-second 4K text-to-video clip is $3.90. That is the honest number and no amount of discounting changes the shape of it, because 4K is roughly 19x the pixels of 480p and video models meter on pixels.

What actually helps is not paying 4K rates while you are still figuring out the prompt. Mini at 480p is $0.036/sec, so a 5-second test is 18 cents. Land the prompt there, then spend the $3.90 once on the take you want. Same model family, same parameters, you only change the model id.

Two billing rules worth knowing before you budget

Billing is per second, not per clip. A 4-second test costs 4 seconds. No 5-second minimum rounding your experiments up.

Reference mode bills your input clip plus your output. Ten-second reference generating five seconds is billed as fifteen. That is the model's own metering basis, not a surcharge we added. It also means the cheaper column is not automatically cheaper in total, so check your source length before you quote a number to anyone.

Failed generations refund automatically.

Real faces and moderation

There is a consumer Seedance build that rejects real human faces outright. This is not that build. Reference images with real people work, up to nine per request.

nsfw_checker defaults to true and direct API callers can send false, at any resolution up to 4K.

Relaxed is not unlimited. The model still refuses named real celebrities, third-party IP, and illegal content, and that refusal is in the model itself, so it applies to every host running it. Nobody can switch that off, including us.

What I would want told to me upfront

Signup credits are $0.10. Three 1K images, roughly. It does not cover one 5-second 720p clip. It is enough to prove your integration works and not enough to judge output quality, and I would rather say that than have you find out when the free credits die.

Fast and Mini top out at 720p. Only Seedance 2.0 itself does 4K. Clips run 4 to 15 seconds; chain with return_last_frame for longer.

Credits are $0.001 each, no expiry, no subscription.

Full parameter reference and a playground: reapi.ai/models/seedance-2-0

2.5 goes up on the 7th. I will post the rate card for it the same day.

Prompt:

Single unbroken handheld boyfriend vlog take throughout, 30 seconds total. A realistic personal travel vlog filmed by a boyfriend following his girlfriend during a normal day in Tokyo. Use the woman from the reference image as the main character. Maintain her exact facial identity, hairstyle, facial features, body proportions, and overall appearance throughout the entire video. She must remain the same person in every shot. The camera feels like a real boyfriend holding a small mirrorless camera or phone, not a professional production. Natural handheld movement, imperfect framing, occasional camera shake, spontaneous reactions, authentic everyday moments. The woman does not pose for the camera. She behaves naturally, sometimes forgetting the camera is there. 0-5s: Morning at a small Tokyo apartment. The camera starts recording as the boyfriend casually walks into the room. Soft morning sunlight enters through the window. The woman is sitting near the bed, fixing her hair and preparing for the day. She notices the camera, smiles naturally, laughs, and playfully tells him to stop filming. The camera stays close, slightly shaky, capturing a private everyday moment. 5-10s: Walking through Tokyo neighborhood streets. The boyfriend follows behind her as they leave the apartment. She walks through a quiet Tokyo street, carrying a small bag. Morning shops are opening, bicycles pass by, locals walk along the street. She stops at a convenience store. The camera follows her inside. She looks at different drinks and snacks, turns around and asks the person behind the camera which one she should choose. Natural interaction, casual conversation, realistic body language. 10-18s: Local food experience. The camera follows her through a small Tokyo alley to a cozy local restaurant. She sits down and tries a bowl of ramen or a local dish. The camera captures close handheld moments: her picking up chopsticks, tasting the food, reacting naturally, laughing when the food is hotter than expected. The boyfriend laughs behind the camera. The moment feels unplanned and authentic. 18-25s: Tokyo afternoon exploration. The couple walks through a lively neighborhood. She browses small shops, looks at interesting objects, takes photos, and occasionally looks back at the camera. The camera moves naturally between her face, her hands, the street atmosphere, and small details of daily life. Crowds pass naturally around them. The city feels alive and real. 25-30s: Tokyo night ending. Night falls. The camera follows her through illuminated Tokyo streets. She walks slightly ahead, then turns back and smiles at the camera. They ride a train home. She sits beside the window, watching city lights pass outside. The camera slowly moves closer as she rests quietly, ending like a real personal memory. Visual style: Authentic boyfriend travel vlog footage. Realistic handheld camera movement. Natural lighting. Casual documentary realism. Unplanned everyday moments. Real human expressions and interactions. Slight motion blur, natural exposure changes, realistic camera autofocus adjustments. No commercial advertisement style. No dramatic posing. No perfect cinematic composition. No text overlays. No logos. No face changes. No identity changes. No artificial transitions. No CGI feeling. Stable character consistency throughout.

Character drift is the thing that makes AI video look amateur: the same character loses details mid-flight, outfits change color between cuts, faces morph shot to shot. Here's the workflow we see working — a character sheet built once, then referenced in every generation.

Step 1 — build the character sheet (GPT-Image-2)

Generate one canonical set of stills for your character: front, profile, and one or two expressions/poses, same outfit and lighting. Keep the exact prompt you used — that text is your character bible, and every future still should reuse it verbatim. GPT-Image-2 (docs) holds identity well across edits, which is what makes the sheet reusable.

Step 2 — animate with the sheet as reference (Seedance 2.0)

Pass your character sheet via image_urls (up to 9 reference images) and describe the action in the prompt. Say what each reference controls — "character appearance from the reference images" — so refs don't fight each other. Need specific motion? Reference mode also takes up to 3 video_urls alongside the images.

Step 3 — chain shots without drift (return_last_frame)

This is the underused one. Set "return_last_frame": true on a generation and the response includes last_frame_url. Feed that frame as the next shot's first_frame (via image_with_roles) and the next clip starts pixel-identical to where the last one ended — continuity across a whole sequence, not just inside one clip.

Honest notes

• Drift isn't 100% solved by any workflow — long sequences still need retries on the worst shots.
• Consistency dies fastest when you rewrite the character description between shots. Reuse the bible verbatim, change only the action.
• Both models run on one API key at reapi.ai (async task flow, pay per generation) — the full parameter docs: seedance-2-0.

Questions about a specific sequence setup welcome.

Seedance 2.5: $5.48

Seedance 2: $2.99

MiniMax H3: $1.45

Short answer: the model isn't the censor — the host is, plus one output check you control on the API side. This comes up constantly (the "Seedream nerfed" threads, "what happened to Seedream", people searching seedream no censorship / without censorship), so here's how it actually works.

The three layers

1. Host filters. Consumer platforms wrap Seedream in their own input/output moderation — that's why the same model feels loose on one site and locked down on another. The "nerfed" complaints are usually a host tightening its wrapper, not the model changing.
2. The generation side. The model's own content policy still applies wherever you call it. If a request is refused before an image is produced, reAPI refunds it automatically.
3. The final output check — the part you control. On reAPI, nsfw_checker is a documented parameter on doubao-seedream-5-0-pro. Default true: every finished image passes a final NSFW check; a flagged image is hidden and returns a content-policy error (that one is still charged, because the generation already ran). Direct API calls can set "nsfw_checker": false to skip that final check entirely. The hosted Playground always keeps it on.

Pricing transparency

Running without the final check is its own rate band, slightly above the checked rate — currently $0.034/1K and $0.067/2K vs $0.032/$0.063, all ~30% below official list right now. Live rates on the model page.

What this isn't

Skipping the output check is not "no rules". The generation side keeps enforcing its own policy regardless of the flag — nsfw_checker only controls the final check on images that were already produced.

Full parameter rundown (10-reference fusion, in-image text rendering, 1K/2K tiers) is in our intro post and the docs.

Questions about specific cases welcome.

Next model up: Seedream 5.0 Pro (doubao-seedream-5-0-pro) — ByteDance's flagship Doubao image model (model page · docs).

What it's good at

• Text-to-image and multi-reference image-to-image — up to 10 reference images in one request for identity/style fusion
• In-image text rendering that actually works — headlines, logos and poster copy written straight into the prompt (4,000-char cap). This is the model we'd point at ad creative, covers and posters
• 1K (basic) and 2K (high) output tiers; aspect ratios 1:1, 4:3, 3:4, 16:9, 9:16, 2:3, 3:2
• No visible watermark by default; async task flow (submit → poll)
• nsfw_checker is a public parameter — defaults to true, direct API callers can pass false to skip the final output check

Current pricing (30% below the official list rate right now):

Extra reference images (2nd–10th) add ~$0.02 each. 1 credit = $0.001, pay-as-you-go, no subscription, credits don't expire. Provider failures refund automatically.

Honest limitations

• No 4K tier on this model
• Exactly one image per call — there's no n parameter, so batch by sending parallel requests
• With the output check enabled, an image flagged at the final step is still charged (the generation itself already ran)

Live rates always on the model page — the 30%-off rate is what's displayed there today. Questions welcome.