ByteDance Seed: Seed-2.0-Code

ByteDance Seed: Seed-2.0-Code is a text generation model from ByteDance Seed, the AI research arm of ByteDance. It is positioned as a coding-focused model, described by the provider as optimized for agentic coding. Its stated strengths cover frontend development, multilingual programming tasks, and coding-agent workflows in tools such as Claude. The model was released by the provider on 2026-08-12 and is identified on api14 by the model id bytedance-seed/seed-2.0-code.

The model accepts text, image, and video input, and produces text output only. Its context window is 262,144 tokens, and the maximum output length is 131,072 tokens. That combination of a large context and a large output budget means you can feed in substantial source files, logs, or documentation alongside visual material and still leave room for a long generated response. Supported request parameters include temperature, top_p, frequency_penalty, max_tokens, stop, response_format, structured_outputs, tool_choice, and tools, along with reasoning controls: reasoning, reasoning_effort, and include_reasoning.

Because it is tuned for agentic coding, the natural fits are tasks where the model has to plan and act rather than answer a single question. Frontend work is one example, such as generating or refactoring UI components from a description or a screenshot. Multilingual programming tasks are another, since the model is explicitly built to handle code across languages rather than only one ecosystem. The tool and structured output parameters make it usable as the reasoning engine inside a coding agent that calls functions, edits files, or returns machine-readable results. The image and video input modalities extend this to cases where the input is a design mockup, a screen recording, or a diagram rather than source text.

You call it through api14's OpenAI-compatible API using the model id bytedance-seed/seed-2.0-code. Point your existing OpenAI client at the api14 base URL and pass that id in the model field; the chat completions shape stays the same. You can set temperature, top_p, frequency_penalty, max_tokens, and stop as usual, use response_format or structured_outputs when you need constrained output, and pass tools with tool_choice when the model should call functions. The reasoning, reasoning_effort, and include_reasoning parameters let you control and surface the model's reasoning behaviour.

One thing to keep in mind is the output modality: the model returns text only, so it will not generate images or video even though it can read them. If your task is pure text generation outside a coding context, or if you need a model that produces non-text output, another model may fit better. For coding agents, frontend generation, and multilingual programming work that benefits from long context and tool use, this model is aimed squarely at that space.