
Coding-tuned 256K-context model with strong front-end results and multilingual programming support for AI coding tools and agents.
Coding-tuned 256K-context model with strong front-end results and multilingual programming support for AI coding tools and agents.
Also known as Seed Code, ByteDance Seed 2.0 Code, Seed-2.0-Code, seed-2-0-code
seed-2-0-code/v1/chat/completionsPOST/v1/responsesPOST/v1/messagesPOST/v1beta/models/seed-2-0-code:generateContentbytedance/seed-2-codedola-seed-2.0-codedoubao-seed-2-codeseed-2-0-code-preview-260328seed-2-codeLive pay-as-you-go rates from the EmpirioLabs catalog. You are billed only for what you use, with no monthly minimum.
Seed 2.0 Code serves the OpenAI-compatible Chat Completions API. Point any OpenAI SDK at https://api.empiriolabs.ai/v1 with your EmpirioLabs API key and use the model id seed-2-0-code. Get an API key from the EmpirioLabs dashboard.
curl https://api.empiriolabs.ai/v1/chat/completions \
-H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "seed-2-0-code",
"messages": [
{"role": "user", "content": "Write a haiku about the ocean."}
]
}'from openai import OpenAI
client = OpenAI(
base_url="https://api.empiriolabs.ai/v1",
api_key="YOUR_EMPIRIOLABS_API_KEY",
)
response = client.chat.completions.create(
model="seed-2-0-code",
messages=[{"role": "user", "content": "Write a haiku about the ocean."}],
)
print(response.choices[0].message.content)Request parameters supported by the Seed 2.0 Code API on EmpirioLabs. Defaults apply when a field is omitted.
| Parameter | Type | Default | Range / values | Description |
|---|---|---|---|---|
| max_tokens | number | 4096 | 1 to 65536 | Max output tokens |
| frequency_penalty | number | 0 | -2 to 2 | Penalty for repeated tokens. >0 reduces repetition, <0 encourages it. |
| presence_penalty | number | 0 | -2 to 2 | Penalty for new vs. seen tokens. >0 encourages new topics, <0 encourages staying on topic. |
| stop | string | - | - | Comma-separated stop sequences |
| enable_thinking | boolean | true | - | Enable deep thinking / reasoning mode. |
| reasoning_effort | enum | medium | low, medium, high | Reasoning effort tier. Use enable_thinking=false to disable reasoning entirely. |
| enable_web_search | boolean | false | - | Enable web search: retrieves live web results and provides them to the model as additional context. |
| image_detail | enum | high | low, high, xhigh | Image visual quality tier for vision input. |
| video_fps | number | - | 0.2 to 5 | Frames per second extracted from video input. |
Pricing is 2x when input tokens >=128K. Temperature and top_p are server-fixed (temp=1, top_p=0.95) regardless of client value.
When this model invokes built-in tools (web search, code interpreter, etc.) inside a single request, the response carries a normalized usage.tool_usage map alongside the token counts. The example below shows the shape — exact field names, units, and which tools appear can vary slightly per provider:
"usage": {
"prompt_tokens": 123,
"completion_tokens": 456,
"cost_usd": 0.0042,
"tool_usage": {"web_search": 3, "code_interpreter": 1}
}The tool counts are already factored into cost_usd — they are surfaced for transparency so you can audit per-tool billing. The field is omitted when no tools were invoked.
On EmpirioLabs, Seed 2.0 Code is billed pay as you go. The live rate card on this page always matches what the API charges.
Seed 2.0 Code supports a 256K-token context window with up to 128,000 output tokens per response.
Yes. Seed 2.0 Code serves the OpenAI-compatible Chat Completions API, so existing OpenAI SDKs work by pointing base_url at https://api.empiriolabs.ai/v1 and setting the model id to seed-2-0-code.
Yes. The EmpirioLabs playground runs Seed 2.0 Code in the browser with the same parameters the API exposes, so you can test prompts before writing code.
Create an EmpirioLabs account, then generate a key under API Keys in the dashboard. Billing is pay-as-you-go credits, so you only pay for the requests you make.
Check out our pricing or reach out if you want your own model deployed on our stack.