Context Window

Compare

Mistral Mistral Large 2402 vs Seed-2.0-Lite

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Mistral

Model

Mistral Mistral Large 2402

Tool calling

Context window

32K

32,000 tokens · ~24K words

Model page
Bytedance

Model

Seed-2.0-Lite

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Mistral Mistral Large 240232K
Seed-2.0-Lite262K

Seed-2.0-Lite has about 8.2× the context window of the other in this pair.

Token calculator →Memory savings →

Seed-2.0-Lite has 719% more context capacity (262K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Seed-2.0-Lite. Its 262K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use Seed-2.0-Lite. Its 131K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecMistral Mistral Large 2402Seed-2.0-Lite
Context window32,000 tokens (32K)262,144 tokens (262K)
Max output tokens8,191 tokens (8K)131,072 tokens (131K)
Speed tierDeepBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AMar 2026

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderMistral Mistral Large 2402 inMistral Mistral Large 2402 outSeed-2.0-Lite inSeed-2.0-Lite out
Aws Bedrock$10.40/M$31.20/M——

Frequently asked questions

Seed-2.0-Lite has a larger context window: 262K tokens vs 32K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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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
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model