Context Window

Compare

GPT-4o-mini Search Preview vs Seed 1.6

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.

Openai

Model

GPT-4o-mini Search Preview

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Bytedance

Model

Seed 1.6

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.

GPT-4o-mini Search Preview128K
Seed 1.6262K

Seed 1.6 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Seed 1.6 has 104% more context capacity (262K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Seed 1.6. Its 262K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Seed 1.6. Its 32K 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.

SpecGPT-4o-mini Search PreviewSeed 1.6
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokens16,384 tokens (16K)32,768 tokens (32K)
Speed tierFastBalanced
VisionYesYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesNo
Batch APIYesNo
Release dateMar 2025Dec 2025

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.

ProviderGPT-4o-mini Search Preview inGPT-4o-mini Search Preview outSeed 1.6 inSeed 1.6 out
Openai$0.150/M$0.600/M——

Frequently asked questions

Seed 1.6 has a larger context window: 262K tokens vs 128K. 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