BytedancebalancedVisionTool use

Seed-2.0-Lite

Seed-2.0-Lite is a versatile, cost‑efficient enterprise workhorse that delivers strong multimodal and agent capabilities while offering noticeably lower latency, making it a practical default choice for most production workloads across...

262K context·~197K words·131K max output

View context details Compare with Amazon Titan Text Express

Context window262Ktokens

Max output131Ktokens

Context window

This model accepts 262K tokens in one request (~197K words of text).

Context window size262K tokens

4K32K128K1M10M

What fits in one request

Short document
About 1,500 words of text
Fits
Long document
About 37K words of text
Fits
Small codebase
About 150K words of text
Fits
Full novel
About 375K words of text
Won't fit

Specifications

Context size, pricing, and release info in one place.

Context window: 262,144 tokens (262K)
Max output tokens: 131,072 tokens (131K)
Speed tier: balanced
Provider: Bytedance
Release date: Mar 2026

Capabilities

See which features this model supports, such as vision, tools, and streaming.

Supported (5)

Vision: Accepts image inputs alongside text; Supported
Tool use: Can call external tools and APIs; Supported
Function calling: Structured function call interface; Supported
Extended thinking: Shows its chain-of-thought reasoning; Supported
Streaming: Returns tokens as they are generated; Supported

Not supported (3)

Web search: Can browse the web during a request; Not supported
Batch API: Process many requests asynchronously; Not supported
Prompt caching: Reuse repeated prompt prefixes cheaply; Not supported

Best for

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Reasoning AI agents Multimodal Long documents RAG

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Frequently asked questions

Short answers about context size and how this model behaves.

Seed-2.0-Lite has a context window of 262K tokens (262,144 tokens). This large window is well-suited for long document analysis, extensive codebases, and multi-session agent workflows.

More from Bytedance

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Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent

Full history

Repeated info

Old context

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Key memories

Current turn

80% less to send — works with any model