Seedance 2.0 API: Online video generation, tutorials and integration guide

What is Seedance 2.0

A multimodal AI video generation model from ByteDance

Seedance 2.0 is an AI video generation model officially released by ByteDance's Seed team on February 12, 2026. We position it as a multimodal audio and video generation model for filmmaking, advertising production, and short-form video creation. Compared with traditional text-to-video tools, Seedance 2.0 places greater emphasis on understanding motion, camera work, sound, and reference materials together, allowing users to control the video creation process through natural language.

Synchronized audio and video generation was already an important capability in Seedance 1.5 Pro. With Seedance 2.0, ByteDance further adopts a unified multimodal joint audio-video generation architecture, bringing text, images, video, and audio into the same creative workflow.

We believe the biggest change in Seedance 2.0 is not simply better image quality, but that it lets users work more like directors. When creating a product advertisement, for example, you can provide product images, character designs, camera references, and background music together, then describe their relationships in the prompt, rather than simply leaving the model to improvise from a piece of text.

A creative model supporting mixed text, image, video, and audio references

Seedance 2.0 supports video generation methods including Text-to-Video, Image-to-Video, and Reference-to-Video. Its officially announced multimodal reference capabilities support up to 9 images, 3 videos, and 3 audio clips, and allow users to specify the purpose of different materials in the prompt.

This reference system can help us address common problems in AI video creation: how to keep characters consistent, how to make the camera move as intended, and how to bring generated results closer to an established visual style.

For example, when preparing a 15-second brand advertisement, we can use product images to establish the subject's appearance, scene images to establish the background, reference video to specify camera movement, and audio references to control the overall rhythm.

We believe this creative approach is easier to manage than repeatedly revising a very long prompt, especially for creators who already have brand assets, character designs, and storyboards.

A model supporting 4- to 15-second videos with joint native audio generation

According to the official technical report, Seedance 2.0 supports direct generation of 4- to 15-second audio-video content, with native output resolutions including 480p and 720p.

It also supports generating character dialogue, ambient sound, action sound effects, and background music alongside the video, using stereo audio to make the visuals more immersive.

We believe this is valuable for short-form video creators. In the past, producing a complete video clip might require separate steps for visual generation, speech synthesis, sound effects, and synchronization in post-production. Some simple scenes can now produce a complete audio-video clip in a single generation.

RequestBeam's Seedance 2.0 online tool and API

Through RequestBeam, we want to help users understand Seedance 2.0's capabilities in real-world scenarios more quickly, without having to build a complete video generation system just to evaluate a model.

You only need to sign in to RequestBeam, create an API Key, and add a small amount of credit to start trying the model through the online tool. There is no need to write API integration code first or purchase a fixed plan for short-term testing.

For developers comparing multiple video generation models, we believe this approach can lower initial testing costs. You can first prepare a set of real prompts and reference materials, use the online tool to determine whether Seedance 2.0 meets your needs, and then decide whether to integrate it into a production product through the API.

For continued use, we also recommend managing available models centrally through RequestBeam to reduce the work of maintaining accounts across multiple service platforms.

Seedance 2.0's core features and strengths

Multimodal references: Keeping characters, products, and visual styles consistent

We believe multimodal references are one of Seedance 2.0's most noteworthy capabilities. By combining character images, product assets, scene images, and existing videos, the model can receive clearer creative constraints than text-only prompts provide.

In some accounts shared by our customers, users noted that Seedance 2.0 has noticeably improved character consistency within individual video clips, maintaining character movements, clothing, and facial features fairly naturally. However, when the same character needs to appear across multiple independently generated clips, changes in facial structure, hairstyle, and clothing details can still occur. Some customers have also shared that reusing the same high-quality character reference image in every generation task is usually more effective than simply repeating a written character description.

We recommend that users creating short-video series, virtual-character stories, or brand advertisements first build their own asset libraries, saving fixed reference images for characters, clothing, products, and scenes, then generating different shots around those assets.

For e-commerce advertisements, we also recommend checking product colors, logos, packaging text, and object shapes.

Complex motion and physics: Improving movement, interactions, and camera stability

Seedance 2.0 shows fairly clear improvements in complex motion. ByteDance has officially demonstrated scenes such as pairs figure skating, dancing, fighting, and object interactions, emphasizing coordination between people, shifts in body balance, and the naturalness of object movement.

In feedback from some creators, Seedance 2.0's handling of continuous shots, human movement, and physical responses has received positive comments. Examples include body movements while running, the motion of clothing, reactions after objects make contact, and visual stability as the camera follows a moving subject.

However, we also see some limitations. When the model needs to depict particularly precise hand movements, such as fastening buttons, manipulating small objects, or accurately operating tools, movements can still become distorted. Scenes with many people are also more prone to unnatural poses or confusion between characters.

Our recommendation is to start testing with a single subject and a clearly defined set of actions. If several people need to perform complex actions at the same time, you can split the action into several shots or provide clearer motion references, rather than expecting the model to handle every detail at once.

For content that requires accurate movements, such as sports instruction and product operation demonstrations, we still recommend manually checking the action sequence and physical logic. Do not judge correctness only by whether the visuals look natural, because incorrect demonstrations in these areas can have serious consequences.

Multi-shot storytelling and camera control: From a single image to a complete short film

Seedance 2.0 can understand some professional cinematography terms, such as push-ins, pull-outs, tracking shots, orbit shots, close-ups, and shot transitions, and can plan multi-shot content based on prompts.

We believe this makes the model better suited to short films, product promotions, and social media advertisements. Users can break a simple idea into an opening, character actions, a product presentation, and a closing shot, then have the model generate them in storyboard order.

In one account shared by our customers, a customer used Seedance 2.0 to complete a music video lasting over three minutes and consisting of roughly 30 shots. The user said character consistency was better than expected, but some camera movements did not follow the original storyboard, and individual clips needed multiple regenerations before becoming usable. This also shows that Seedance 2.0's multi-shot capabilities are already commercially usable in some business contexts.

We recommend clearly describing what each shot needs to accomplish, including subject position, camera movement, shot duration, and the ending state. For important commercial content, you can first generate a low-cost preview, then produce the final version after confirming the shot structure.

Native audio-video synchronization: Generating dialogue, sound effects, and background music together

Seedance 2.0 uses a joint audio-video generation architecture that can handle dialogue, action sound effects, ambient sounds, and music while generating visuals.

In our view, this capability is best suited to short videos with clearly defined sound events. For example, a person opens a car door on a rainy night, and the scene needs rain, footsteps, the sound of the car door closing, and a short line of dialogue from the character.

For these scenes, native audio-video generation can reduce the work of finding separate sound effects and synchronizing them in post-production.

However, in feedback from our customers, audio remains a relatively common source of problems. Some customers found that short lines of dialogue could achieve good lip synchronization, while longer dialogue could gradually drift out of sync. Others reported that when uploaded music was used as a reference, the model sometimes changed the original rhythm or lyrics.

We recommend keeping dialogue relatively short and checking the results line by line whenever a video includes a brand slogan, product name, or dialogue in a specific language that must be conveyed accurately.

For music videos, voice-over advertisements, and multilingual marketing content, we tend to prefer using the model to create the visuals, then deciding based on the actual quality whether to retain the generated audio or use a separate audio production workflow.

Video editing and extension: Modifying existing material, not just generating from scratch

Seedance 2.0 is not only a model for generating video from scratch; it also supports some editing and extension tasks through reference videos and text instructions.

For example, users can provide an existing clip and have the model reference its visuals, movements, or camera work to try changing the background or visual style, or continue generating subsequent shots based on a new story description.

We believe this capability is particularly valuable for teams with existing video assets. Much real-world commercial content is not made entirely from scratch; instead, it requires repeated changes to backgrounds, shots, pacing, or presentation around established characters and products.

However, creators' real-world feedback suggests that using reference videos does not always go smoothly. Some users have encountered moderation of character assets, rejected reference content, and extensions that do not fully connect with the previous video. For editing tasks that need to preserve the original visual structure accurately, generative editing can still change details that were not meant to be modified.

Our recommendation is to treat Seedance 2.0 as a creative editing tool, not a pixel-by-pixel video editor. If you only need to change subtitles, trim the duration, or replace an audio track, traditional video editing tools are often more reliable.

For advertising concepts, short-film extensions, and visual effects, you can prioritize testing Seedance 2.0's editing capabilities. Also, before uploading material featuring people or commercial assets, confirm that you have the appropriate usage rights and necessary permissions.

Seedance 2.0 benchmarks and performance

We refer to ByteDance's SeedVideoBench 2.0 technical report and Artificial Analysis's independent video generation leaderboards. SeedVideoBench 2.0 is an evaluation organized by the model's development team, scoring specific capability dimensions. Artificial Analysis produces Elo scores through anonymous pairwise comparisons, more closely reflecting users' overall preferences between different generated results.

Seedance 2.0 previously ranked first on both the text-to-video and image-to-video leaderboards at Arena.ai. However, as new models are released, we believe this historical ranking can no longer be used as evidence that it always remains in the lead.

Text-to-video: Motion quality and audio-video synchronization both score 3.75 on SeedVideoBench 2.0

In ByteDance's published SeedVideoBench 2.0 text-to-video evaluation, Seedance 2.0 scored 3.75 for both motion quality and audio-video synchronization, on a scale of 1 to 5.

In the same test, Kling 3.0 scored 3.10 for motion quality and 2.78 for audio-video synchronization. Seedance 2.0 also achieved high scores in prompt adherence, visual aesthetics, and audio quality.

We believe these results reflect Seedance 2.0's main strengths at launch: it does not only pursue attractive individual frames, but also attempts to handle motion continuity, camera work, and sound synchronization together.

However, this is still the development team's own evaluation, and it does not cover all the newer models released afterward, so it cannot directly represent the current market-wide ranking.

We recommend prioritizing Seedance 2.0 for testing if you need to generate human movement, short-film stories, and complete sound effects. We would focus on whether actions are complete, whether shots connect naturally, and whether sounds accurately match the visuals.

Image-to-video: Motion quality scores 3.35, prompt adherence scores 3.46

In SeedVideoBench 2.0's Image-to-Video evaluation, Seedance 2.0 scored 3.35 for motion quality, 3.46 for prompt adherence, and 3.31 for fidelity to the original image.

In the same report, Kling 3.0's corresponding scores were 2.80, 2.78, and 3.18. We can see that Seedance 2.0's advantage is more pronounced in motion and instruction following, while the gap in fidelity to the original image is relatively small.

We believe this is a useful reference for e-commerce and brand content creators. Users often already have product images they are happy with and do not need the model to redesign the product; they want the subject in the image to move naturally.

Our recommendation: If you already have fixed character, product, or brand assets, you can prioritize trying Seedance 2.0's image-to-video capability. For product packaging, text, logos, and fine structures, however, we recommend adding fidelity checks rather than judging only whether the motion looks smooth.

Multimodal references: Editing consistency scores 3.54, reference alignment scores 3.03

ByteDance also used SeedVideoBench 2.0 to test Reference-to-Video, or multimodal reference generation tasks.

In this evaluation, Seedance 2.0 scored 3.54 for editing consistency, 3.03 for reference alignment, and 3.24 for motion quality. These dimensions use a scoring scale of 1 to 5.

For comparison, Kling 3.0 scored 3.37 for editing consistency, 2.37 for reference alignment, and 2.36 for motion quality.

We believe reference alignment is very important in actual content production. For example, when a character image, a scene image, and a camera reference are provided together, what users really care about is whether the model correctly understands what each material is meant to control.

However, the more detailed official tests also show that tasks such as combining image and audio references remain challenging. Support for uploading multiple types of materials does not mean the model will necessarily execute every reference requirement in full.

Our recommendation: We do not recommend uploading every image, audio clip, and video at the outset. You can start with one subject image and a clear shot description, then gradually add scene, sound, and motion references. Each time you add a type of material, check whether it delivers a real improvement in quality.

Independent video generation leaderboards: 1116 Elo for text-to-video, 1176 Elo for image-to-video

According to the Artificial Analysis leaderboards we viewed on October 9, 2026, Dreamina Seedance 2.0 scored 1116 Elo on the AA-Video-T2V v2.0 text-to-video leaderboard with audio, ranking 7th.

On the same leaderboard, Wan 3.0 scored 1156 Elo and Seedance 2.5 scored 1143 Elo. We believe this shows that Seedance 2.0 remains competitive, but newer versions and models from some other vendors have surpassed it in overall preference scores.

On the AA-Video-I2V v1.0 image-to-video leaderboard with audio, Seedance 2.0 720p scored 1176 Elo, ranking 5th; MiniMax H3 Max scored 1195 Elo.

Note that text-to-video v2.0 and image-to-video v1.0 are two separate leaderboards. Their Elo values cannot simply be compared, and a score of 1176 certainly does not necessarily mean a model is stronger than one scoring 1116.

Our recommendation: If your product primarily offers text-to-video generation, you can test Seedance 2.0, Seedance 2.5, and Wan 3.0 together. If it mainly animates existing images, prepare a separate image-to-video test set and include models currently performing well, such as MiniMax H3 Max. We recommend comparing the proportion of usable final results with the same materials, rather than choosing solely by overall ranking.

Independent video editing: 1076 Elo, with complex editing requiring comparison against newer models

On Artificial Analysis's AA-Video-Editing v1.1 leaderboard, Seedance 2.0 scored 1076 Elo, ranking 6th. This evaluation primarily examines models' ability to modify existing video according to text instructions while preserving audio.

On the same leaderboard, Wan 3.0 scored 1187 Elo and Seedance 2.5 scored 1151 Elo. We believe this shows that while Seedance 2.0 already has some video editing capability, it has not maintained its initial leading position against newer models.

We focus more on the differences between actual editing scenarios. Examples include changing a daytime background to night, changing a character's clothing, adjusting camera movement, and continuing an existing story. Different tasks have different requirements for preserving the original video and generating new content.

Our recommendation: Seedance 2.0 is still worth testing for simple creative editing and extension tasks. However, if your business depends heavily on video editing, we recommend adding Wan 3.0 and Seedance 2.5 as comparisons, focusing on whether the parts of the original video that do not need modification remain consistent.

We also recommend recording each task's first-attempt success rate, number of regenerations, and final usable clip length, because these metrics often reflect actual costs better than the price of a single generation.

Comparing Seedance 2.0 with Seedance 2.5, Wan 3.0, Kling 3.0, and Veo 3.1

Compared with Seedance 2.5: The newer version is better suited to longer videos and complex reference tasks

Seedance 2.5 is a new-generation video generation model released by ByteDance on July 31, 2026. Compared with Seedance 2.0, it increases the maximum duration of a single generation from 15 seconds to 30 seconds and further strengthens multi-shot storytelling and video editing.

For multimodal references, Seedance 2.5's officially announced limits increase to 30 images, 10 videos, and 10 audio clips, alongside support for more precise editing control.

We believe the biggest difference between the two generations is no longer just visual quality, but whether they can handle more complex video production workflows. Seedance 2.0 may already be sufficient for a 5- to 10-second advertising shot. However, if you want to create a 30-second short film with multiple characters, several lines of dialogue, and a continuous story, Seedance 2.5's new capabilities deserve more attention.

Independent tests also support prioritizing evaluation of the newer version for complex tasks. On Artificial Analysis's text-to-video leaderboard, Seedance 2.5's 1143 Elo is higher than Seedance 2.0's 1116 Elo; on the video editing leaderboard, their scores are 1151 and 1076 Elo respectively.

We recommend that teams already using Seedance 2.0 avoid migrating every task immediately. You can retain existing short-video workflows and first test Seedance 2.5 on long shots, multi-character storytelling, and complex editing. If the newer version's improvement in usable output rate covers the additional cost, gradually expand its use.

Compared with Wan 3.0: Overall generation and video editing deserve close evaluation

Wan 3.0 is one of the models worth comparing closely with Seedance 2.0 on current public leaderboards.

On Artificial Analysis's text-to-video leaderboard with audio, Wan 3.0 scored 1156 Elo, higher than Seedance 2.0's 1116 Elo; on the video editing leaderboard, Wan 3.0's 1187 Elo is also higher than Seedance 2.0's 1076 Elo.

However, on the image-to-video leaderboard, their scores are 1164 and 1176 Elo respectively, which are relatively close, with overlapping confidence intervals. We believe these scores alone should not be used to declare that either model is necessarily better at every image-to-video task.

For teams preparing to generate short videos in batches, we recommend prioritizing a comparison of Wan 3.0 and Seedance 2.0's final usable output rates, paying particular attention to generation consistency with the same prompts, audio performance, and cost per usable video.

If the main work is text-to-video generation or modifying existing videos, we would make Wan 3.0 a priority candidate for testing. If you already have a mature multimodal reference workflow, you can continue evaluating whether Seedance 2.0 is more reliable in specific scenarios.

Compared with Kling 3.0: Choose according to visual requirements and generation costs

Kling 3.0 is a video generation model from Kuaishou. We believe competition between Kling and Seedance primarily centers on motion, shot quality, character consistency, and generation costs.

In ByteDance's SeedVideoBench 2.0 tests at launch, Seedance 2.0 scored 3.75 for motion quality while Kling 3.0 scored 3.10; in the image-to-video image fidelity test, they scored 3.31 and 3.18 respectively.

Independent evaluations offer another perspective. On Artificial Analysis's current image-to-video leaderboard with audio, Seedance 2.0 720p scored 1176 Elo and Kling 3.0 1080p Pro scored 1055 Elo. However, different versions and settings affect results, so this does not mean every generation task has the same gap.

We believe Kling 3.0 is still worth including as a comparison model for commercial video generation, particularly for budget-sensitive businesses with relatively simple asset structures that need to generate large numbers of short shots in batches.

Our recommendation is this: if your content requires complex camera movement, multiple reference types, and complete sound effects, prioritize testing Seedance 2.0. If it mainly involves simple image animation, character movement, and batch content production, you can also test Kling 3.0 alongside it.

Compared with Google Veo 3.1: Short shots, high resolution, and the Google ecosystem

Google Veo 3.1 also offers native audio-video generation, producing video from text, images, and specified starting and ending frames. Google's official API documentation supports duration options such as 4, 6, and 8 seconds, and offers 1080p and 4K output under specific conditions.

Seedance 2.0's strengths lie more in longer single-generation durations and extensive mixed image, video, and audio reference capabilities. This workflow is appealing to creators who need to combine different reference materials.

On Artificial Analysis's current text-to-video leaderboard with audio, Seedance 2.0 scored 1116 Elo and Veo 3.1 scored 962 Elo. However, we do not recommend directly extrapolating these overall preference scores to every 4K output or brand advertising scenario.

We recommend prioritizing tests of Veo 3.1 if your project already makes extensive use of the Gemini API and needs to generate short videos within the same development ecosystem.

If you want to create 10- to 15-second content with multiple shots and more reference materials, focus on evaluating Seedance 2.0.

For high-resolution commercial delivery, we recommend additionally checking actual output detail, clarity, resolution processing, and the cost of post-production upscaling.

Seedance 2.0 API frequently asked questions

How do I call the Seedance 2.0 API through RequestBeam?

We recommend first signing in to RequestBeam and creating an API Key, then reading our documentation or asking AI to help you configure the connection.

We recommend completing a minimal generation test through the online tool first, then integrating according to RequestBeam's current Seedance 2.0 API documentation. This lets you verify whether the model meets your business needs before integration and helps avoid mistakenly carrying over endpoint paths and parameters from other providers into RequestBeam.

Why does the Seedance 2.0 API report a parameter error after I upload reference materials?

Seedance 2.0 supports multimodal references, but input requirements are not identical across different API modes.

For example, text-to-video typically only requires a prompt and basic output parameters; image-to-video requires an image; multimodal reference generation may involve several input types, including images, videos, and audio.

The officially announced maximum reference scale is 9 images, 3 videos, and 3 audio clips, but specific APIs may impose additional limits on the total number of materials, file size, video length, and file formats. For example, fal's reference generation endpoint also requires the total number of materials across all modalities to be no more than 12.

We recommend first checking whether the current endpoint supports the input types you need, then confirming that the number, formats, and URLs of your materials meet the requirements. Reference materials should also be accessible to the server; do not use temporary webpage URLs that require additional login permissions.

When using reference numbers, also ensure that material names in the prompt match the actual upload order to avoid confusing character, scene, and motion references.

Why has my Seedance 2.0 API request been submitted, but no video has been returned?

Video generation differs significantly from ordinary text model calls. Generating a video typically requires more computing resources, so many providers process requests as asynchronous tasks.

In this case, successful request submission only means the task has entered processing; it does not mean the video has finished generating.

We recommend that developers divide the workflow into three stages: submitting the generation task, querying task status, and retrieving the final result. Handle specific response fields and task status names according to RequestBeam's current API documentation.

We also recommend adding task records, timeout checks, and a reasonable retry strategy in production products. Particularly for paid video generation tasks, confirm whether the original task has already succeeded before retrying to avoid additional charges from duplicate submissions.

How do I choose video duration and resolution and control costs with the Seedance 2.0 API?

Seedance 2.0 officially supports video generation from 4 to 15 seconds. According to the technical report, the model's native output resolutions are 480p and 720p; some commercial providers additionally offer 1080p or 4K output options.

We recommend first confirming the configurations actually supported by the current API, because parameter ranges can differ between providers, generation modes, and even Standard and Fast versions.

For cost control, we prefer a staged approach. For example, first use short 4- to 5-second clips to validate prompts and reference materials, then decide whether to generate longer or higher-resolution videos based on final publishing requirements.

If you are only validating product concepts, camera movement, and character actions, there is no need to use the highest output settings every time.

Also, video generation cost cannot be assessed solely by the price per API call. We recommend recording each generation's duration, settings, cost, success rate, and number of usable final clips.

For example, even if a 10-second video is inexpensive to generate once, its final cost may still exceed that of another model that succeeds on the first attempt if it needs to be generated five times before it is usable.

For teams that need to generate video at scale, we recommend building your own test dataset around actual business needs and evaluating video quality, generation success rate, time, and cost together before deciding whether to use Seedance 2.0 at scale.