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Step 5 Preview vs Switchyard

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.

Stepfun

Model

Step 5 Preview

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Nvidia

Model

Switchyard

Context window

1M

1,000,000 tokens · ~750K 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.

Step 5 Preview1M
Switchyard1M

Same context window size for both models.

Step 5 Preview and Switchyard have identical context windows (1000K tokens).

Full specs

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

SpecStep 5 PreviewSwitchyard
Context window1,000,000 tokens (1000K)1,000,000 tokens (1000K)
Max output tokens64,000 tokens (64K)N/A
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateOct 2026Sep 2026

Frequently asked questions

Switchyard has a larger context window: 1000K tokens vs 1000K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

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.

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