Seed (ByteDance)

Seed is a family and ecosystem of large language models, multimodal foundation models, reasoning models, software-engineering agents, image- and video-generation systems, speech models, world models, scientific models, and robotics systems developed by the ByteDance Seed research organization.

The Seed name is used across several technically distinct branches:

• the numbered Seed1.x and Seed2.x foundation-model families;
• specialist open-weight models including Seed-OSS, Seed-Coder, Seed-X, and BAGEL;
• Seedream image-generation and editing models;
• Seedance audio-video-generation models;
• Seed3D three-dimensional asset-generation models;
• speech and interaction systems including Seeduplex, Seed LiveInterpret, and Seed Realtime Voice;
• embodied-AI and robotics models including Seed GR-3 and Seed GR-RL;
• scientific systems such as Protenix.

Official materials do not define Seed as a technical acronym. The term functions primarily as the brand of ByteDance's foundation-model and artificial-intelligence research organization.

The ByteDance Seed team was established in 2023. It conducts research in large language models, speech, computer vision, multimodal interaction, world models, robotics, scientific artificial intelligence, model infrastructure, and next-generation user interfaces. The organization maintains laboratories and research positions in China, Singapore, and the United States and states that its models support more than 50 product scenarios, including Doubao, Coze, and Jimeng.

As of 10 July 2026, Seed2.1 is ByteDance Seed's latest publicly announced general-purpose foundation-model family. Released on 23 June 2026, it comprises two hosted models:

• Seed2.1 Pro — intended for high-complexity reasoning, professional work, software engineering, and long-horizon agent tasks;
• Seed2.1 Turbo — intended to provide a lower-latency and lower-cost option for large-scale production workloads.

Both variants support multimodal understanding, tool use, agentic execution, software engineering, and long-context processing. The dated Volcano Engine API checkpoints are documented with a 256,000-token context window. ByteDance has not publicly disclosed Seed2.1's total parameter count, active parameter count, layer configuration, expert topology, tokenizer specification, or complete pre-training corpus. Unofficial estimates should therefore not be treated as model specifications.

The latest principal generative-media systems are:

• Seedream 5.0 Pro, released on 8 July 2026, for image generation and precision editing;
• Seedance 2.0, released on 12 February 2026, for joint audio-video generation from text, images, audio, and video;
• Seed3D 2.0, released on 23 April 2026, for geometry, physically based materials, articulated objects, and simulation-ready three-dimensional assets;
• Seeduplex, introduced on 9 April 2026, for native full-duplex speech interaction in which the system can listen while speaking.

Most current numbered Seed models are proprietary hosted services available through ByteDance products, Volcano Engine ModelArk, and selected BytePlus services. ByteDance has separately released several downloadable checkpoints under Apache 2.0, MIT, OpenMDW, or other model-specific licences. An Apache-licensed cookbook or evaluation repository for a hosted Seed model does not necessarily mean that the production model weights have been published.

History and institutional background

Establishment of ByteDance Seed

ByteDance formed the Seed organization in 2023 as a centralized foundation-model and advanced-artificial-intelligence research group. The organization extends beyond the language-model teams that originally developed Doubao models. Its official research scope includes:

• large language models;
• speech understanding and generation;
• visual understanding and generation;
• multimodal interaction;
• world models;
• artificial-intelligence infrastructure;
• responsible artificial intelligence;
• robotics;
• scientific discovery.

The Seed models are closely associated with ByteDance's Doubao brand. The distinction is primarily organizational and product-oriented:

• Seed is the research and model-family name;
• Doubao is used for ByteDance's consumer assistant and many commercial model identifiers;
• Volcano Engine ModelArk is a principal enterprise API and model-development platform;
• BytePlus distributes selected ByteDance services internationally;
• Coze provides agent and application construction;
• Jimeng or Dreamina provides generative-media tools.

A model may therefore appear as “Seed2.1” on the research website and as doubao-seed-2-1-pro-260628 in a commercial API catalogue.

Seed1.5 and Doubao-1.5-pro

The first widely documented numbered Seed generation appeared in early 2025. Seed1.5, also marketed as Doubao-1.5-pro, was a sparse MoE foundation model targeting knowledge, reasoning, code generation, and instruction following.

The associated Seed1.5-Thinking model was documented in April 2025 as a 200-billion-parameter MoE system with approximately 20 billion activated parameters per token.

The model was optimized through reinforcement learning for mathematics, programming, scientific reasoning, and more general user tasks. ByteDance reported:

• 86.7 on AIME 2024;
• 55.0 on its Codeforces evaluation;
• 77.3 on GPQA-Diamond;
• an eight-percentage-point preference advantage over the compared DeepSeek-R1 configuration on selected non-reasoning tasks.

These values were developer-reported and depended on the prompts, sampling procedure, test-time computation, and comparison checkpoints used in the technical report.

Seed1.5-VL

Seed1.5-VL was introduced in May 2025 as a general-purpose vision-language foundation model. Its published architecture combines:

• a 532-million-parameter vision encoder;
• an MoE language model activating approximately 20 billion parameters per token;
• multimodal post-training for visual reasoning and agents.

ByteDance reported that Seed1.5-VL achieved the best result among the evaluated systems on 38 of 60 public benchmarks. Its target tasks included:

• general visual question answering;
• chart and document understanding;
• mathematical and scientific diagrams;
• video understanding;
• visual puzzles;
• graphical-interface control;
• gameplay and computer-use agents.

The complete total parameter count of the MoE language backbone was not stated in the paper's abstract. It should not be inferred solely from the 20-billion active-parameter value.

Seed1.6

ByteDance released Seed1.6 on 25 June 2025. It was the first Seed generation for which the company publicly described a 230-billion-total, 23-billion-active MoE base architecture.

Its main characteristics were:

• text and visual understanding;
• a 256,000-token context window;
• deep-reasoning and direct-response configurations;
• GUI interaction;
• adaptive allocation of reasoning tokens;
• parallel decoding for difficult reasoning tasks.

Seed1.6 training was divided into three broad phases:

1. text-only pre-training;
2. multimodal mixed continual training;
3. long-context continual training, extending the maximum sequence length from 32,000 to 256,000 tokens.

The principal post-trained variants were:

• Seed1.6-Thinking, which used extended reasoning;
• Seed1.6 Adaptive CoT, which attempted to decide whether a prompt required a long chain of thought.

Open-weight specialist releases in 2025

ByteDance Seed released several model families whose checkpoints could be downloaded independently of the proprietary Doubao API.

Seed-Coder was released in May 2025 as an eight-billion-parameter code-model family:

• Seed-Coder-8B-Base — 32K context;
• Seed-Coder-8B-Instruct — 32K context;
• Seed-Coder-8B-Reasoning — 64K context.

The project was released under the MIT License.

BAGEL, released in May 2025, is a unified multimodal model with approximately 14 billion total parameters and seven billion active parameters. It supports text and image understanding, image generation, image editing, multiview synthesis, and selected world-modelling tasks. The project is distributed under Apache License 2.0.

Seed-X was released in July 2025 as a seven-billion-parameter multilingual translation family. It includes instruction, reinforcement-learning, and reward-model variants and uses a Mistral-style architecture. The repository uses the OpenMDW licence.

Seed-OSS was released on 20 August 2025. It is a 36-billion-parameter dense causal language model with a native 524,288-token context and Apache-2.0-licensed weights.

Seed Diffusion Preview was introduced in August 2025 as an experimental discrete diffusion language model specialized for source-code generation. ByteDance reported 2,146 generated tokens per second on H20 accelerators in the specified evaluation.

Seed1.8

Seed1.8 was released on 18 December 2025 as a generalized agentic model integrating language, vision, search, coding, tools, and GUI interaction.

The model was designed for:

• search and research;
• software engineering;
• graphical-interface control;
• document and visual understanding;
• multi-turn tool use;
• long-video analysis;
• complex personal and professional workflows.

Seed1.8 supports four inference modes in the technical model card:

• no_think;
• think-low;
• think-medium;
• think-high.

The public launch article described three user-facing thinking configurations, while the fuller model card documented four technical levels. This distinction reflects different interface abstractions rather than necessarily separate checkpoints.

The international BytePlus service documents text, image, and video input with text output, a context window of approximately 256,000 tokens, and separate limits for ordinary response and generated reasoning. The model's total and active parameter counts were not published.

Seed2.0

Seed2.0 was released in February 2026 as a family of three general-purpose agent models:

• Seed2.0 Pro — optimized for long-chain reasoning and complex workflows;
• Seed2.0 Lite — designed to balance model quality, latency, and production cost;
• Seed2.0 Mini — optimized for throughput, batch generation, and high deployment density.

The family strengthened:

• foundational reasoning;
• multimodal understanding;
• search agents;
• coding agents;
• GUI agents;
• office and research workflows;
• scientific and economically valuable tasks.

At the end of April 2026, Seed2.0 Lite received an omni-modal upgrade. ByteDance described the updated model as the first Seed foundation model to natively unify video, image, audio, and text understanding.

Parameter counts and full architectural specifications for Seed2.0 Pro, Lite, and Mini were not publicly disclosed. Public Seed2.0 GitHub repositories contain cookbooks and integration examples rather than the production weights.

Seed2.1

Seed2.1 was released on 23 June 2026 after ByteDance collected feedback from Seed2.0 deployments. It shifted evaluation emphasis toward end-to-end task completion rather than isolated benchmark answers.

The two model sizes have different operational goals:

• Pro emphasizes difficult professional tasks, advanced software engineering, research, and high-value agent workflows;
• Turbo emphasizes lower cost, lower latency, and large-scale production use.

Reported improvements include:

• cross-tool and cross-environment execution;
• professional document and spreadsheet work;
• requirement analysis and project planning;
• software architecture and implementation;
• debugging and validation;
• visual and spatial reasoning;
• long-video understanding;
• switching between GUI actions and structured tools;
• more efficient computer-use trajectories.

ByteDance reported that reinforcement learning reduced the average number of actions required in selected GUI tasks by approximately 16%.

The official model page and public API documentation do not disclose a parameter count. Claims that Seed2.1 uses a particular trillion-parameter MoE configuration are therefore unofficial unless ByteDance publishes a technical specification.

Release chronology

Date	Model or system	Disclosed scale	Modalities or role	Availability and qualification
January 2025	Seed1.5 / Doubao-1.5-pro	General model scale not fully disclosed	Knowledge, reasoning, code, and general instruction following	Proprietary Doubao and Volcano Engine service
April 2025	Seed1.5-Thinking	Approximately 200B total / 20B active	Text reasoning, mathematics, programming, and general tasks	Proprietary hosted checkpoint; technical report published
May 2025	Seed1.5-VL	532M vision encoder plus MoE LLM with 20B active parameters	Text, images, video, visual reasoning, and GUI agents	Hosted model; public technical report and evaluation resources
May 2025	Seed-Coder	8B dense	Code generation, instruction following, and reasoning	Open weights under MIT; 32K and 64K variants
May 2025	BAGEL	14B total / 7B active	Unified image understanding, generation, editing, and world modelling	Open weights under Apache 2.0
25 June 2025	Seed1.6	230B total / 23B active	Text, vision, reasoning, GUI interaction, and adaptive thinking	Proprietary API model; 256K context
July 2025	Seed-X	7B dense	Multilingual translation	Open weights under OpenMDW
20 August 2025	Seed-OSS	36B dense	General text generation, reasoning, and agents	Apache-2.0 open weights; native 512K context
August 2025	Seed Diffusion Preview	Not fully disclosed	Discrete diffusion language model for code	Experimental hosted preview and research publication
18 December 2025	Seed1.8	Not disclosed	Generalized multimodal agent for search, code, GUI, and tools	Proprietary hosted model; approximately 256K context
February 2026	Seed2.0 Pro, Lite, and Mini	Not disclosed	General agents, reasoning, code, search, multimodal understanding, and GUI tasks	Proprietary hosted family
28 April 2026	Seed2.0 Lite omni-modal update	Not disclosed	Unified text, image, video, and audio understanding	Proprietary hosted checkpoint
23 June 2026	Seed2.1 Pro and Turbo	Not disclosed	General agents, software engineering, visual reasoning, video, and productivity	Proprietary Doubao and Volcano Engine models; 256K API context
12 February 2026	Seedance 2.0	Not disclosed	Joint audio-video generation and editing	Proprietary generative-media model
9 April 2026	Seeduplex	Not disclosed	Native full-duplex speech interaction	Deployed in the Doubao application
23 April 2026	Seed3D 2.0	Not disclosed	Geometry, PBR material, articulated-object, and scene generation	Proprietary Volcano Engine API
8 July 2026	Seedream 5.0 Pro	Not disclosed	Image generation, information visualization, multilingual typography, and precision editing	Proprietary image-creation model

Naming and scope

Several distinctions are necessary when describing the Seed ecosystem.

• Seed and Doubao are overlapping but non-identical labels. Seed is the research and model-family brand; Doubao is a consumer and commercial-product brand.
• The numbered Seed generations are not fully open-weight. Public repositories for Seed1.8 or Seed2.0 mainly provide cookbooks, prompts, and integration examples.
• A repository licence does not automatically apply to a hosted checkpoint. Apache-2.0 licensing of a cookbook does not imply Apache-licensed production weights.
• Active parameters are not total parameters. Seed1.6 activates 23B of 230B parameters; this does not give it the storage footprint of a dense 23B model.
• Thinking modes are inference configurations. They may share one underlying checkpoint and differ in test-time computation, prompts, routing, or generation limits.
• Seed2.0 Lite's omni-modal update is a dated checkpoint. The February and April 2026 versions should not be assumed to have identical capabilities.
• Seedream, Seedance, and Seed3D are separate generative branches. Seed2.1's ability to understand an image or video does not mean its text endpoint generates images or video.
• Agent products are systems, not only models. Search, browser control, code execution, file access, and GUI actions are supplied through external runtimes and tools.
• No official Seed2.1 parameter count had been published by 10 July 2026.

Technical foundations

Autoregressive language generation

Most Seed language models use autoregressive token generation. For a sequence $x_1,\dots ,x_T$, the model probability is factorized as:

$P_{\theta }(x_1,\dots ,x_T)=\prod _{t=1}^T{P}_{\theta }(x_t\mid x_1,\dots ,x_{t-1}),$

where $\theta$ denotes the model parameters.

For a multimodal model, generation can additionally be conditioned on visual, video, audio, document, and tool representations:

$P_{\theta }(y_t\mid y_{<t},{𝐇}_{\mathrm{t}\mathrm{e}\mathrm{x}\mathrm{t}},{𝐇}_{\mathrm{v}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}},{𝐇}_{\mathrm{a}\mathrm{u}\mathrm{d}\mathrm{i}\mathrm{o}},{𝐇}_{\mathrm{t}\mathrm{o}\mathrm{o}\mathrm{l}\mathrm{s}}).$

This generic factorization does not imply that every Seed generation uses the same architecture or tokenizer.

Standard scaled dot-product attention is:

$\mathrm{Attention}(𝐐,𝐊,𝐕)=\mathrm{softmax}\left(\frac{𝐐{𝐊}^{𝖳}}{\sqrt{d_k}}\right)𝐕,$

where $𝐐$, $𝐊$, and $𝐕$ are query, key, and value matrices.

Mixture-of-Experts models

Seed1.5-Thinking and Seed1.6 are publicly documented as sparse MoE models. For token representation $𝐡$, a simplified expert transformation is:

$\mathrm{MoE}(𝐡)=\sum _{i\in 𝒮(𝐡)}{g}_i(𝐡)E_i(𝐡),$

where:

• $E_i$ is an expert feed-forward network;
• $𝒮(𝐡)$ is the selected expert subset;
• $g_i(𝐡)$ is the routing weight.

A simplified routing operation is:

$𝒮(𝐡)=\mathrm{TopK}\left(W_g𝐡\right).$

Sparse routing reduces the expert computation used for one token. It does not eliminate the need to store, load, distribute, or offload the complete expert pool.

For Seed1.6, the ratio of total to active parameters is approximately:

$\frac{230\mathrm{B}}{23\mathrm{B}}=10.$

This ratio should not be interpreted as an exact tenfold end-to-end speedup because attention, embeddings, routing, shared layers, communication, and memory movement remain necessary.

ByteDance has not stated whether Seed1.8, Seed2.0, or Seed2.1 retain the same 230B-A23B topology. Their architectures should therefore be described as undisclosed.

Grouped-Query Attention

Several open Seed models use Grouped-Query Attention (GQA). GQA provides more query heads than key–value heads, allowing several query heads to share one key and value representation.

The technique reduces KV-cache memory relative to full multi-head attention, although the attention score calculation remains dependent on sequence length.

Seed-OSS has:

• 80 query heads;
• eight key heads;
• eight value heads;
• 128-dimensional heads.

Ten query heads therefore share each key–value head.

Seed-OSS architecture

Seed-OSS is a dense causal language model rather than an MoE model.

Property	Seed-OSS-36B
Total parameters	36B
Transformer layers	64
Hidden-state dimension	5,120
Query / key / value heads	80 / 8 / 8
Head dimension	128
Vocabulary size	Approximately 155K
Attention	Grouped-Query Attention
Activation	SwiGLU
Normalization	RMSNorm
Position encoding	Rotary position embeddings
RoPE base frequency	$10^7$
Native context	524,288 tokens

The release contains:

• Seed-OSS-36B-Base;
• Seed-OSS-36B-Base-woSyn;
• Seed-OSS-36B-Instruct.

The woSyn checkpoint excludes synthetic instruction data from pre-training so that researchers can study post-training without that source of prior instruction behaviour.

Multimodal perception

Seed1.5-VL, Seed1.6, Seed1.8, Seed2.0, and Seed2.1 combine language processing with visual representations.

A generic image path can be written as:

${𝐙}_{\mathrm{v}\mathrm{i}\mathrm{s}}=E_{\mathrm{v}\mathrm{i}\mathrm{s}}(𝐈),$

${𝐇}_{\mathrm{v}\mathrm{i}\mathrm{s}}=P_{\mathrm{v}\mathrm{i}\mathrm{s}}\left({𝐙}_{\mathrm{v}\mathrm{i}\mathrm{s}}\right),$

where $E_{\mathrm{v}\mathrm{i}\mathrm{s}}$ is a vision encoder and $P_{\mathrm{v}\mathrm{i}\mathrm{s}}$ maps visual features into the language model's hidden space.

Video understanding additionally requires temporal representation. A surrounding system may:

• sample frames uniformly;
• select segments dynamically;
• use slow-motion inspection;
• extract audio or subtitles;
• call a video-cutting tool;
• reinsert selected evidence into the context.

Seed1.8's VideoCut tool was designed to select or replay useful portions of long video. The resulting performance is a property of the model-plus-tool system, not only the neural checkpoint.

Seed2.0 Lite's April update additionally introduced native audio understanding. Seed2.1's public materials emphasize documents, images, spatial structure, and long video but do not clearly document an independent raw-audio-input interface for every endpoint.

Adaptive Chain of Thought

Seed1.6 introduced Adaptive Chain-of-Thought (AdaCoT), which determines whether a prompt should receive an extended reasoning process.

The model supports three conceptual behaviours:

• FullCoT — use an explicit reasoning process for every prompt;
• NoCoT — answer directly;
• AdaCoT — select the response mode according to estimated task difficulty.

A simplified routing objective can be represented as:

$m\sim P_{\varphi }(m\mid x),m\in \{\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t},\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{o}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\},$

where $P_{\varphi }$ is a learned or post-trained mode-selection policy.

The generation objective may then include a reasoning-cost term:

$J(\theta ,\varphi )=𝔼[R(x,y)-\lambda C_{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{o}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}}(y,m)],$

where $R$ measures answer quality and $C_{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{o}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}}$ penalizes unnecessary reasoning length.

This equation is a conceptual abstraction, not ByteDance's disclosed production loss.

ByteDance reported CoT triggering rates of approximately:

• 37% on MMLU;
• 70% on MMLU-Pro;
• 90–100% on difficult AIME and BeyondAIME tasks.

This suggests that the system learned a correlation between prompt difficulty and test-time computation. It does not guarantee that the routing decision is correct for every input.

Parallel reasoning

Seed1.6-Thinking and Seed1.8 explored parallel reasoning trajectories. Instead of producing one chain, the system generates several candidate paths and selects or synthesizes a result.

If $n$ candidate trajectories are independent, success under an idealized pass-at-$n$ evaluation is:

$P(\mathrm{s}\mathrm{u}\mathrm{c}\mathrm{c}\mathrm{e}\mathrm{s}\mathrm{s})=1-(1-p{)}^n,$

where $p$ is the probability that one trajectory succeeds.

Real trajectories are correlated, and selection can fail. Parallel reasoning also increases total inference cost even when concurrency reduces wall-clock latency.

ByteDance reported that parallel decoding improved Seed1.6-Thinking by approximately eight points on BeyondAIME. Seed1.8's model card similarly reported gains on selected mathematics and coding evaluations when using parallel thinking.

Thinking modes in Seed1.8 and later models

Seed1.8's four thinking levels allow applications to trade solution quality against latency and token use.

The levels should not be interpreted as four independent foundation models. They can differ through:

• prompting;
• reasoning-token limits;
• test-time branching;
• internal routing;
• stopping criteria;
• tool budget.

Seed2.0 and Seed2.1 similarly provide deep-reasoning and production-oriented variants, but ByteDance has not published a complete mapping between consumer mode names and underlying checkpoints.

Generated reasoning text should not be treated as a guaranteed complete representation of the model's internal computation. It can omit steps, contain post-hoc explanations, or include errors.

Tool use and agent trajectories

Seed agent models can interact with:

• web search;
• code execution;
• terminals;
• file systems;
• graphical interfaces;
• Model Context Protocol servers;
• office applications;
• user-defined functions;
• knowledge bases;
• image and video tools.

An agent trajectory can be represented as:

$\tau =(o_0,a_1,o_1,a_2,o_2,\dots ,a_T,o_T),$

where $o_t$ is an observation and $a_t$ is a model-selected action.

A policy can be written as:

$a_t\sim {\pi }_{\theta }(a\mid o_{\le t},a_{<t},m_t,g),$

where:

• $m_t$ is retained state or memory;
• $g$ is the task objective.

The tool runtime, not the neural model alone, determines whether an action is executed. Permissions, credentials, sandboxes, retries, and validation are application-level components.

GUI and computer use

Seed1.5-VL, Seed1.8, Seed2.0, and Seed2.1 were trained or evaluated on graphical-interface tasks.

A computer-use loop typically performs:

1. capture a screenshot or interface state;
2. identify relevant controls;
3. select an action;
4. execute a click, keystroke, scroll, or structured tool call;
5. observe the new state;
6. repeat until completion or failure.

Seed2.1 was optimized to select among GUI and non-GUI actions. Structured tools or scripts can complete some tasks more efficiently than repeated clicking, while GUI interaction remains useful when no stable API exists.

Computer-use performance depends strongly on:

• screen resolution;
• application version;
• localization;
• network delay;
• pop-ups;
• authentication state;
• action schema;
• sandbox design.

Long-context processing

The hosted context window evolved approximately as follows:

Model family	Published context
Seed1.5 family	Endpoint-dependent; later variants reached long-context configurations
Seed1.6	256K tokens
Seed-OSS	512K tokens
Seed1.8	Approximately 256K tokens
Seed2.0	Up to approximately 256K in hosted configurations
Seed2.1	256K in the dated Volcano Engine API checkpoints

A nominal context window specifies how much input and output the service can accept. It does not establish:

• uniform recall at every position;
• perfect retrieval from a long document;
• resistance to distractors;
• preservation of exact numerical state;
• correct chronology;
• immunity to prompt injection.

Long contexts also increase tokenization time, prompt-transfer cost, prefill latency, and the opportunity for conflicting instructions.

Diffusion language modelling

Seed Diffusion uses a discrete diffusion process rather than ordinary strictly left-to-right generation.

A simplified forward corruption process is:

$q({𝐱}_t\mid {𝐱}_{t-1}),$

where progressively more tokens are masked or corrupted as $t$ increases.

A neural model learns the reverse process:

$p_{\theta }({𝐱}_{t-1}\mid {𝐱}_t,c),$

conditioned on context $c$. Because several positions can be refined during one denoising step, diffusion language models can generate tokens in parallel.

This differs from speculative decoding, in which a draft autoregressive model proposes tokens that a larger model subsequently verifies.

Seed Diffusion Preview was specialized for code and reached a reported 2,146 tokens per second on H20 hardware. The figure reflects one serving configuration and should not be generalized to every prompt length, batch size, or accelerator.

Pre-training and post-training

Seed1.5-Thinking reinforcement learning

Seed1.5-Thinking was post-trained through reinforcement learning over reasoning and non-reasoning tasks.

The programme addressed several problems common to reasoning models:

• over-optimization for competition mathematics;
• excessive chain-of-thought length;
• degradation on ordinary conversational requests;
• unstable rewards;
• capability interference between code, mathematics, and general instruction following.

The model's 200B-total and 20B-active scale allowed ByteDance to perform large reinforcement-learning runs at lower per-token cost than a dense 200B model.

Seed1.6 pre-training stages

Seed1.6's documented pre-training stages were:

Stage 1 — Text-only pre-training. The corpus contained web pages, books, research papers, source code, and other text. Data preparation used rule-based and model-based cleaning, deduplication, filtering, and sampling.

Stage 2 — Multimodal mixed continual training. The mixture increased the proportion of scientific, code, and reasoning material and introduced high-quality visual data.

Stage 3 — Long-context continual training. Sequence length increased progressively from 32K to 256K rather than moving directly to the maximum context.

This staged method allows the expensive longest-context training to be concentrated near the end of the run.

Seed1.6 post-training

Seed1.6-Thinking used repeated cycles of:

• rejection fine-tuning;
• reinforcement learning;
• multi-dimensional reward modelling;
• additional mathematics, code, puzzle, and general data.

The next reinforcement-learning stage began from the checkpoint produced by the preceding rejection-fine-tuning stage.

AdaCoT added an overthinking penalty intended to discourage unnecessarily long reasoning. The training objective therefore considered both answer quality and reasoning cost.

Seed-OSS pre-training

Seed-OSS was pre-trained on more than 12 trillion tokens.

The model card describes a mixture of:

• publicly accessible internet data;
• commercially acquired data;
• internally produced data.

The reported knowledge cutoff is July 2024. Preprocessing included:

• exact and approximate deduplication;
• removal or masking of sensitive information;
• quality filtering;
• CSAM filtering;
• toxicity filtering.

Seed-OSS-36B-Base contains synthetic instruction data during pre-training, while Seed-OSS-36B-Base-woSyn omits it.

The instruction checkpoint was subsequently trained through supervised fine-tuning and reinforcement learning, including safety-oriented data.

Seed-Coder data curation

Seed-Coder uses a model-centric data pipeline in which code models help identify, score, transform, and generate their own training material.

The family targets:

• code completion;
• code generation;
• code editing;
• code reasoning;
• software-engineering tasks.

Model-assisted curation can scale faster than manual annotation, but it risks reproducing the teacher's errors, preferred coding style, insecure patterns, or benchmark-like examples.

Seed1.8 agentic post-training

Seed1.8's post-training incorporated multimodal and interactive tasks involving:

• search;
• software engineering;
• graphical interfaces;
• general tools;
• long video;
• professional workflows.

The model card reports evaluations at different reasoning budgets and with tools such as VideoCut. Some benchmark improvements therefore measure the complete agent configuration rather than the unaided model.

Seed2.0 and Seed2.1 disclosure

ByteDance has released model cards and extensive evaluation data for Seed2.0 and Seed2.1 but has not published the full information required to reproduce their training runs.

Undisclosed or incompletely disclosed items include:

• total and active parameter counts;
• full architecture;
• exact pre-training-token totals;
• data-source distribution;
• tokenizer details;
• optimizer and learning-rate schedules;
• every reinforcement-learning environment;
• complete reward functions;
• production system prompts;
• all safety classifiers.

The models can be studied through hosted access, but their training cannot be independently reproduced from public materials.

Main foundation models

Summary table

Model	Release	Total / active parameters	Published context	Native inputs	Principal role
Seed1.5-Thinking	April 2025	200B / 20B	Endpoint-dependent	Text	General reasoning, mathematics, and code
Seed1.5-VL	May 2025	Total not fully disclosed / 20B active; 532M vision encoder	Endpoint-dependent	Text, images, and video	Multimodal reasoning and GUI agents
Seed1.6	June 2025	230B / 23B	256K	Text and images	Adaptive reasoning, multimodal understanding, and GUI interaction
Seed1.8	December 2025	Not disclosed	Approximately 256K	Text, images, and video	General agents, search, coding, and computer use
Seed2.0 Pro	February 2026	Not disclosed	Up to approximately 256K	Text and multimodal context	High-complexity reasoning and long-horizon agents
Seed2.0 Lite	February 2026; omni update April 2026	Not disclosed	Up to approximately 256K	Text, images, video, and audio in the April checkpoint	Production-grade general-purpose model
Seed2.0 Mini	February 2026	Not disclosed	Hosted limit is checkpoint-specific	Primarily text and multimodal context	High-throughput deployment
Seed2.1 Pro	June 2026	Not disclosed	256K API context	Text, images, documents, and video	Flagship professional, coding, and agent model
Seed2.1 Turbo	June 2026	Not disclosed	256K API context	Text, images, documents, and video	Lower-latency production model

Seed1.5-Thinking

Seed1.5-Thinking established the main technical themes continued by later Seed generations:

• sparse expert activation;
• reinforcement-learning scaling;
• competition-level reasoning;
• retention of ordinary instruction-following capability;
• balancing reasoning quality against output cost.

The model's public paper is primarily a post-training report rather than a complete base-model architecture specification.

Seed1.5-VL

Seed1.5-VL expanded the family from text reasoning to multimodal agents. Its visual encoder allows the model to process images, rendered documents, charts, interfaces, and sampled video.

The model was evaluated not only on static visual question answering but also on environments requiring action selection, such as graphical interfaces and games.

Its visual abilities should not be conflated with image generation. The model produces text, coordinates, or actions rather than generated pictures.

Seed1.6

Seed1.6 is the last numbered Seed family for which ByteDance publicly stated both total and active parameter counts.

Its design combines:

• 230B total parameters;
• 23B active parameters;
• multimodal continual pre-training;
• 256K context;
• adaptive reasoning;
• parallel test-time computation.

The model served as an important predecessor to Seed1.8 and to the open Seed-OSS checkpoints.

Seed1.8

Seed1.8 reframed the numbered family around real-world agency.

Its intended work patterns include:

• gather information;
• inspect text and visual material;
• invoke search;
• write or execute code;
• operate graphical interfaces;
• maintain multi-step state;
• deliver a complete artifact or answer.

ByteDance reported 67.6 on BrowseComp-en in the public launch material and strong performance across GUI, coding, travel-planning, financial-research, and long-video evaluations.

Seed2.0

Seed2.0 extended the model family into research-grade and economically valuable tasks.

The Pro, Lite, and Mini variants are not merely different reasoning settings; they are presented as models of different sizes and serving objectives. ByteDance does not disclose their parameter counts.

The April Lite checkpoint is particularly significant because it introduced native audio understanding alongside text, image, and video.

Seed2.1

Seed2.1 is the current general-purpose family.

Its public positioning emphasizes delivery: the ability to continue from initial requirements through planning, tool use, creation, debugging, validation, and completion.

Seed2.1 Pro is generally the stronger model in difficult agent, coding, visual, and video evaluations, although Turbo equals or exceeds Pro on some reported tasks, including Workspace Bench and BeyondAIME. This illustrates that a larger or more expensive model does not necessarily lead on every benchmark.

Open-weight and specialist model families

Seed-OSS

Seed-OSS was released to support independent research and local deployment. Its principal features include:

• 36B dense parameters;
• native 512K context;
• explicit thinking-budget control;
• reasoning and agentic post-training;
• base checkpoints with and without synthetic instruction data;
• Apache License 2.0.

Thinking budgets can be set to values such as 512, 1K, 2K, 4K, 8K, or 16K tokens. During generation, the model can emit budget-reflection markers that estimate consumed and remaining reasoning tokens.

The mechanism is useful for studying inference-time compute but does not guarantee that the model spends its budget effectively.

Seed-Coder

Seed-Coder is a compact code-focused family:

Checkpoint	Context	Training role
Seed-Coder-8B-Base	32K	Model-centric code pre-training
Seed-Coder-8B-Instruct	32K	Instruction-following code model
Seed-Coder-8B-Reasoning	64K	Reinforcement-learning reasoning model

The family is designed for practical local or server deployment through Transformers and vLLM.

Seed-X

Seed-X is a seven-billion-parameter translation family with:

• an instruction model;
• a reinforcement-learning policy;
• a translation reward model.

The system is optimized for multilingual translation rather than general multi-turn chat. The repository explicitly states that it does not use a conventional chat template.

BAGEL

BAGEL is a unified multimodal understanding-and-generation model. It can:

• answer questions about images;
• generate images from text;
• edit images;
• use interleaved image and text context;
• perform multiview synthesis;
• support selected navigation or world-modelling tasks.

Its 14B-total, 7B-active architecture is substantially smaller than the proprietary numbered Seed models.

BAGEL represents a different architectural goal from Seed1.5-VL: one model supports both visual understanding and visual generation.

Seed Diffusion

Seed Diffusion studies whether discrete diffusion can provide high-quality code generation with substantially greater output throughput than conventional autoregressive decoding.

The preview is a research system rather than the latest general-purpose Seed model. It is listed separately in ByteDance's official model catalogue.

UI-TARS and related GUI agents

UI-TARS is a ByteDance Seed research line for native graphical-interface agents. Later UI-TARS work incorporates multimodal perception, reasoning, action grounding, and online reinforcement learning.

UI-TARS is related to Seed1.5-VL, Seed1.8, and Seed2.x computer-use research but is a distinct model family rather than a numbered Seed checkpoint.

Generative-media and interaction systems

Seedream image models

Seedream is ByteDance's image-generation and editing family.

The current catalogue includes:

• Seedream 4.0;
• Seedream 4.5;
• Seedream 5.0 Lite;
• Seedream 5.0 Pro;
• SeedEdit 3.0.

Seedream 5.0 Pro focuses on professional image creation. Its documented capabilities include:

• converting dense information into infographics;
• multilingual text rendering;
• point-based editing;
• lasso selection;
• sketch-guided editing;
• color and material replacement;
• layer separation;
• multi-image fusion;
• localized cultural and typographic adaptation.

The model supports Chinese and English typography, right-to-left Arabic script, accented Latin text, and other multilingual layouts.

ByteDance acknowledged continuing limitations in very fine text rendering and pixel-level editing consistency.

The model's parameter count, architecture, weight licence, and complete training corpus were not publicly disclosed.

Seedance video models

Seedance is ByteDance's video-generation branch.

Seedance 1.0 emphasized:

• 1080p output;
• text-to-video;
• image-to-video;
• multi-shot narratives;
• cinematic camera movement.

Seedance 1.5 Pro added synchronized audio-video generation.

Seedance 2.0 uses a unified multimodal audio-video generation framework. It accepts mixed combinations of:

• natural-language instructions;
• as many as nine images;
• as many as three video clips;
• as many as three audio clips.

The system can reference:

• subject identity;
• composition;
• camera movement;
• motion rhythm;
• visual effects;
• environmental sound;
• music;
• dialogue.

It generates clips of up to approximately 15 seconds in the documented launch configuration and supports stereo audio, multi-shot output, continuation, and targeted editing.

Seedance 2.0 remains limited by occasional:

• detail instability;
• audio distortion;
• imperfect photorealism;
• multi-subject inconsistency;
• text-rendering errors;
• complex-edit failures.

ByteDance states that real-human portrait references require identity verification or prior legal authorization.

Seed3D

Seed3D generates three-dimensional assets from images or other conditions.

Seed3D 2.0 uses a two-stage coarse-to-fine Diffusion Transformer process:

Stage 1 — Coarse geometry. A larger DiT establishes topology, overall shape, and spatial organization.

Stage 2 — Detail reconstruction. A second stage refines sharp edges, thin structures, and local surfaces using:

• coarse-geometry latent initialization;
• voxelized positional encoding;
• a higher-fidelity VAE.

For material generation, Seed3D 2.0 uses a unified physically based rendering pipeline. It jointly predicts PBR maps through shared DiT layers and modality-specific projections.

A sparse MoE component increases high-resolution texture capacity while controlling inference computation. Visual-language-model descriptions of material types and physical properties provide additional conditioning.

Seed3D 2.0 can additionally produce:

• functional part decompositions;
• articulated components;
• joint types and axes;
• URDF-compatible assets;
• scene layouts;
• simulation-ready objects.

The launch evaluation used 60 experienced three-dimensional-content evaluators and approximately 200 test cases. Human preference is informative but does not independently verify geometric correctness or simulation safety.

Seeduplex

Seeduplex is a native full-duplex speech language model. Unlike a conventional turn-based voice assistant, it continues listening while generating speech.

Its main objectives are:

• distinguish the primary user from ambient speech;
• ignore irrelevant conversations;
• detect hesitation and thinking pauses;
• avoid responding before a user has finished;
• stop speaking when interrupted;
• maintain low-latency conversation.

ByteDance reported:

• approximately half the false-response and false-interruption rate of the compared half-duplex system;
• a 40% reduction in premature responses;
• an 8% improvement on its human-likeness endpoint-detection test.

These are internal deployment results. Independent reproduction would require the same acoustic environments, devices, users, and evaluation procedure.

Seeduplex was deployed in the Doubao application.

Other speech and music branches

The current Seed model catalogue also includes:

• Seed LiveInterpret 2.0 for simultaneous speech-to-speech interpretation;
• Seed Realtime Voice for end-to-end speech understanding and generation;
• Seed-Music for controllable music creation.

These systems have distinct architectures and service interfaces. Their parameter counts and production weights have not generally been disclosed.

AI for science and robotics

Protenix

Protenix is ByteDance Seed's biomolecular modelling system for protein and molecular-complex structure prediction and design.

It belongs to the wider Seed research organization but is not a language-model checkpoint. Scientific model evaluation requires domain-specific metrics distinct from ordinary LLM benchmarks.

Seed GR-3

Seed GR-3 is a vision-language-action model for generalizable, long-horizon, and dexterous robotics tasks.

It connects:

• visual perception;
• language instructions;
• action prediction;
• robot control.

A generated action policy requires external safety constraints, low-level control, and hardware validation before physical execution.

Seed GR-RL

Seed GR-RL is a reinforcement-learning framework for multi-step dexterous manipulation.

Its inclusion in the same official catalogue illustrates that Seed is an organization-wide model ecosystem rather than only a chatbot family.

Reported benchmark results

Benchmark results below are principally developer-reported. They can vary with:

• checkpoint date;
• reasoning level;
• generation length;
• number of samples;
• tool access;
• context-management strategy;
• agent harness;
• judge model;
• timeout and retry rules;
• benchmark revision;
• repository state.

Seed2.1

Selected official results are:

Benchmark	Capability	Seed2.1 Pro	Seed2.1 Turbo
KINA	Knowledge	48.3	46.6
SuperGPQA	Knowledge	70.8	67.4
BeyondAIME	Reasoning	87.0	88.0
Workspace Bench	Professional work	53.0	54.7
Agent Startup Bench	High-value agent work	68.8	54.0
xDailyBench	White-collar work	61.0	56.4
NL2Repo-Bench	End-to-end software engineering	47.0	43.7
Terminal-Bench 2.1	Terminal use	71.0	67.6
SWE-Atlas	Debugging	35.2	30.6
MathVision	Multimodal reasoning	92.6; 94.5 with tools	90.1; 92.7 with tools
MMMU-Pro	Multimodal STEM	81.6; 82.7 with tools	80.1; 82.2 with tools
WorldVQA	Visual knowledge	53.0	48.6
BabyVision	Visual perception	73.7	62.9
CharXiv-RQ	Scientific infographics	85.4; 86.4 with tools	82.5; 83.6 with tools
ERQA	Spatial reasoning	72.0	71.3
MMLongBench-128K	Multimodal long context	78.3	76.9
VideoMME	Long-video understanding	89.2	89.0
TOMATO	Video motion and perception	79.5	56.8
Minerva	Video reasoning	70.7	65.9
OVOBench	Streaming-video understanding	80.7	79.2

The table includes tool-enabled and non-tool scores for some tasks. Those configurations measure different systems and should not be compared as if they were identical one-pass model outputs.

Seed2.0

Selected Seed2.0 results are:

Benchmark	Seed2.0 Lite 0428	Seed2.0 Lite 0215	Seed2.0 Pro 0215	Seed2.0 Mini 0215
GPQA-Diamond	88.4	85.1	88.9	88.0
SuperGPQA	69.6	67.5	68.7	63.9
HLE, no tools and text only	25.7	28.2	32.4	28.2
BeyondAIME	79.0	76.0	86.5	80.0
FrontierSci-Olympiad	72.0	70.0	74.0	70.0
SuperChem, text only	55.0	48.0	51.6	29.1
BABE	57.9	50.2	53.5	49.0
MultiChallenge	69.9	63.2	68.3	62.5
BrowseComp	64.0	72.1	77.3	61.3
ResearchRubrics	59.2	50.8	50.7	47.1
GDPval	53.1	47.3	54.4	50.6
SWE-bench Multilingual	66.6	64.4	71.7	73.6
SWE-bench Pro	46.6	46.0	46.9	54.4
Terminal-Bench 2.0	43.3	45.0	55.8	60.0

The Mini checkpoint leading some coding rows illustrates that benchmark performance is affected by post-training and serving configuration, not only model size.

Seed1.8

Selected Seed1.8 results include:

Benchmark	Seed1.8
BrowseComp-en	67.6
OSWorld	61.9
RealBench	49.1
Online-Mind2Web	85.9
AndroidWorld	70.7
VideoMME	87.8 in the published launch evaluation
CGBench with VideoCut	65.9
LVBench with VideoCut	78.9
ZeroVideo with VideoCut	18.8

Tool-assisted video results depend on the VideoCut selection system. They do not measure an identical inference configuration to direct video prompting.

Seed-OSS

Selected Seed-OSS-36B-Instruct results are:

Benchmark	Seed-OSS-36B-Instruct
MMLU-Pro	82.7
MMLU	87.4
GPQA-Diamond	71.4
SuperGPQA	55.7
AIME 2024	91.7
AIME 2025	84.7
BeyondAIME	65.0
LiveCodeBench v6	67.4
IFEval	85.8
SWE-bench Verified with OpenHands	56.0
RULER at 128K	94.6
AIR-Bench safety score	75.6

The recommended evaluation settings used temperature 1.1 and top-p 0.95 for most tasks. Scores obtained with deterministic decoding or a different reasoning budget may differ.

Products and ecosystem

Doubao

Doubao is ByteDance's principal consumer assistant. Depending on region and product version, it provides:

• text conversation;
• search;
• file and document analysis;
• image and video understanding;
• voice interaction;
• agents;
• image and video generation through specialized models.

Doubao's behaviour is not determined by one model alone. Product-layer components can include routing, search, memory, moderation, file parsing, speech recognition, speech synthesis, and tool execution.

Volcano Engine ModelArk

Volcano Engine ModelArk is the principal enterprise platform for hosted Seed and Doubao models.

Its documented functions include:

• text generation;
• multimodal understanding;
• document and file input;
• video and audio analysis;
• image generation;
• video generation;
• three-dimensional generation;
• function calling;
• Model Context Protocol;
• web search;
• private-knowledge search;
• context caching;
• structured output;
• model fine-tuning;
• evaluation;
• managed agents;
• sandbox execution;
• persistent memory.

Seed2.1 API checkpoint identifiers include:

• doubao-seed-2-1-pro-260628;
• doubao-seed-2-1-turbo-260628.

The date suffix represents a hosted model snapshot or service version, not necessarily the original announcement date.

BytePlus

BytePlus provides selected ByteDance artificial-intelligence services internationally.

The available model catalogue can differ from the mainland-China Volcano Engine catalogue in:

• checkpoint versions;
• regional availability;
• price;
• legal terms;
• supported modalities;
• rate limits;
• data handling.

Coze

Coze is ByteDance's agent and application platform. It can combine Seed models with:

• workflow logic;
• plugins;
• knowledge bases;
• tool calls;
• memory;
• user interfaces.

An agent built in Coze is a larger system than the underlying foundation model.

Jimeng and Dreamina

Jimeng and Dreamina provide image- and video-creation tools using Seedream and Seedance systems.

The product layer may add:

• templates;
• prompt rewriting;
• asset storage;
• safety filters;
• upscaling;
• editing controls;
• identity checks;
• provenance metadata.

Software engineering

Seed1.8, Seed2.0, and Seed2.1 can serve as the reasoning components of software-engineering agents.

Representative workflows include:

• repository analysis;
• feature implementation;
• bug repair;
• environment setup;
• terminal commands;
• test execution;
• frontend generation from images or video;
• debugging;
• validation;
• RTL and hardware-design tasks.

The coding harness controls filesystem access, credentials, network access, commands, and verification.

Multimodal and video work

Seed2.1 can use long video and visual material to:

• extract events;
• identify temporal changes;
• reason about motion;
• generate interactive pages from mock-ups or footage;
• transform long movies into narrated summaries;
• combine several images into spatial representations.

A long-video result may involve frame sampling, video tools, subtitles, and context management outside the neural backbone.

Licensing and distribution

Seed models use several distribution models.

Model or resource	Licence or status	Qualification
Seed1.5, Seed1.6, Seed1.8, Seed2.0, Seed2.1	Proprietary hosted models	No general downloadable production weights
Seed1.8 and Seed2.0 cookbook repositories	Apache License 2.0	Applies to notebooks, examples, and code—not automatically to model weights
Seed-OSS	Apache License 2.0	Downloadable 36B weights
Seed-Coder	MIT License	Downloadable 8B checkpoints
Seed-X	OpenMDW	Downloadable 7B translation checkpoints
BAGEL	Apache License 2.0	Downloadable unified multimodal weights
Seedream, Seedance, Seed3D, Seeduplex	Proprietary hosted services	No general production-weight release

Open-weight licensing applies to the distributed model files. It does not automatically grant rights to:

• confidential input data;
• copyrighted source material;
• private voices or likenesses;
• trademarks;
• every generated output;
• proprietary ByteDance products;
• third-party tools used by an agent.

Limitations and open problems

Architectural opacity of frontier models

ByteDance disclosed Seed1.5-Thinking's and Seed1.6's scale but has not published equivalent architecture specifications for Seed1.8, Seed2.0, or Seed2.1.

This prevents independent determination of:

• parameter efficiency;
• expert count;
• attention mechanism;
• tokenizer behaviour;
• memory requirements;
• quantization sensitivity;
• training-compute scale;
• hardware utilization.

Hosted behavioural evaluation cannot fully separate the base model from system prompts, routing, search, safety filters, or product tools.

Benchmark comparability

ByteDance's model pages compare Seed systems with other providers' models, but the evaluations can mix:

• reproduced and provider-reported scores;
• different reasoning settings;
• different tool budgets;
• different maximum output lengths;
• changing hosted checkpoints;
• internal benchmarks;
• model-based judges;
• multiple attempts.

An isolated table cell should not be treated as a permanent model ranking.

Hallucinations

Seed models can produce:

• unsupported factual claims;
• fabricated citations;
• incorrect calculations;
• false summaries of documents;
• insecure code;
• incorrect tool arguments;
• mistaken interpretations of visual or audio input;
• false claims that a task completed successfully.

Tool access improves grounding only when the tool is appropriate, its result is accurate, and the model interprets it correctly.

Long-context reliability

Large context windows create several risks:

• losing a small fact among distractors;
• following obsolete instructions;
• confusing document versions;
• missing contradictions;
• chronology errors;
• prompt injection embedded in a file;
• high prompt-processing latency;
• unnecessary cost.

A context length of 256K or 512K is an input-capacity statement, not a guarantee of lossless memory.

Agentic error accumulation

Long-horizon tasks can fail because of:

• an incorrect initial plan;
• tool-selection errors;
• malformed calls;
• repeated actions;
• unsafe commands;
• wrong-file edits;
• loss of the original objective;
• incomplete validation;
• incorrect interpretation of partial success.

More autonomy increases both the opportunity to recover and the opportunity to cause damage.

GUI fragility

Graphical interfaces can change without warning. Agents can be disrupted by:

• changed layouts;
• different resolutions;
• localized text;
• pop-ups;
• advertisements;
• loading states;
• authentication prompts;
• accessibility overlays.

A benchmarked interface snapshot may not represent the current production application.

Multimodal errors

Seed multimodal models can misread:

• small text;
• low-contrast scans;
• unusual charts;
• diagrams with implicit conventions;
• occluded objects;
• fast video motion;
• overlapping speakers;
• sarcasm;
• cultural references.

Visual confidence is not a measurement of image quality or evidence certainty.

Generative-media consistency

Seedream, Seedance, and Seed3D can produce:

• malformed text;
• inconsistent identity;
• impossible motion;
• unstable geometry;
• changing objects;
• incorrect reflections;
• audio artifacts;
• texture seams;
• physically implausible materials.

Professional workflows require review and, where relevant, geometric or physical validation.

Speech and identity risk

Full-duplex voice and speech-generation systems can be used for:

• impersonation;
• fraud;
• harassment;
• deceptive calls;
• unauthorized reconstruction of a person's voice.

Identity verification, consent, provenance, account security, and abuse monitoring remain necessary.

Training-data transparency

ByteDance publishes many papers and model cards but does not provide a complete document-level inventory for its frontier proprietary models.

Open questions include:

• copyright composition;
• personal data;
• language distribution;
• benchmark contamination;
• synthetic-data share;
• licensing of visual and audio material;
• data-removal procedures.

Reproducibility

Seed-OSS, Seed-Coder, Seed-X, and BAGEL enable independent inference and fine-tuning. The largest current Seed models cannot be reproduced because their weights, data, and complete training systems are unavailable.

Even for open checkpoints, recreating the original training run would require:

• the exact corpus;
• data filters;
• distributed-training code;
• optimizer settings;
• post-training environments;
• reward models;
• substantial compute.

Resource cost

Large MoE and multimodal models require accelerator memory, high-bandwidth networking, storage, electricity, and cooling.

Sparse activation reduces per-token arithmetic but does not eliminate:

• storage of inactive experts;
• routing communication;
• optimizer state;
• reinforcement-learning rollouts;
• visual and audio encoders;
• long-context caches.

ByteDance has not published a complete independently audited lifecycle carbon assessment for Seed2.1.

Safety, privacy, and regulation

Safety training

The Seed-OSS model card describes safety measures including:

• training-data filtering;
• CSAM removal;
• personal-information filtering;
• safety supervised fine-tuning;
• reinforcement learning from human feedback;
• adversarial testing;
• review by a global safety and security team.

Hosted frontier models may use additional undisclosed product-level safeguards.

No safety process can guarantee that a model will reject every harmful request or avoid every false positive.

Agent security

Seed-powered agents should use:

• least-privilege credentials;
• filesystem and network sandboxes;
• command allowlists;
• transaction limits;
• short-lived secrets;
• audit logging;
• independent result verification;
• human approval for irreversible operations.

Model alignment is not an operating-system security boundary.

Prompt injection

Untrusted instructions can be embedded in:

• websites;
• documents;
• images;
• source-code comments;
• emails;
• tool results;
• video subtitles.

Applications should explicitly distinguish authoritative instructions from retrieved evidence and untrusted content.

Privacy

Seed systems can process:

• business documents;
• source code;
• photographs;
• voice recordings;
• video;
• location information;
• financial data;
• health information;
• user-account data.

Deployers must examine the applicable consumer, enterprise, regional, and API terms rather than assuming that all ByteDance services retain and process data identically.

Chinese generative-AI regulation

Public-facing generative-AI services in China operate under the Interim Measures for the Management of Generative Artificial Intelligence Services, effective from 15 August 2023.

The measures address lawful training data, personal information, security, content governance, and provider responsibilities.

The Measures for Labeling Artificial Intelligence-Generated Synthetic Content took effect on 1 September 2025 and require applicable visible and metadata-based labels for generated text, images, audio, video, and virtual scenes.

Other jurisdictions may impose additional requirements concerning privacy, copyright, biometric information, consumer protection, children, employment, finance, health, product safety, and artificial-intelligence governance.

Media provenance

Seedream and Seedance can create persuasive synthetic visual and audio material. Responsible deployments may require:

• disclosure that content is generated;
• metadata provenance;
• restrictions on public-figure impersonation;
• identity and consent checks;
• protections for minors;
• review of political and news-related uses;
• procedures for removal and complaint handling.

External links

• ByteDance Seed official website
• Official Seed model catalogue
• ByteDance Seed on GitHub
• ByteDance Seed on Hugging Face
• Seed2.1 official page
• Seed2.0 official page
• Seed1.8 official page
• Seed-OSS repository
• Seed-Coder repository
• BAGEL repository
• Volcano Engine model catalogue

See also

• Large language model
• Multimodal large language model
• Mixture of experts
• AI agent

Literature

• ByteDance Seed. Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning. arXiv:2504.13914, 2025.
• ByteDance Seed. Seed1.5-VL Technical Report. arXiv:2505.07062, 2025.
• ByteDance Seed. Seed1.8 Model Card: Towards Generalized Real-World Agency. arXiv:2603.20633, 2026.
• ByteDance Seed. Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity. arXiv:2607.00248, 2026.
• ByteDance Seed. Seed-Coder: Let the Code Model Curate Data for Itself. arXiv:2506.03524, 2025.
• Cheng, S. et al. Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters. arXiv:2507.13618, 2025.
• Deng, C. et al. Emerging Properties in Unified Multimodal Pretraining. arXiv:2505.14683, 2025.
• Gong, S. et al. Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference. arXiv:2508.02193, 2025.
• Vaswani, A. et al. Attention Is All You Need. Advances in Neural Information Processing Systems 30, 2017.
• Ainslie, J. et al. GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints. EMNLP, 2023.