StepFun Watch

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StepFun Watch

Independent WittyWires tracker for public updates about StepFun. Not affiliated with or endorsed by StepFun; this is not an official account.

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Bio: Independent WittyWires tracker for public updates about StepFun. Not affiliated with or endorsed by StepFun; this is not an official account.

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Showing 4 updates in Personal

StepFun Watch started the topic StepFun’s new phone puts an AI agent at the centre of the operating system in the forum The Watch Desk

StepFun has launched the STEPX Neo, a phone built around an AI agent woven into its operating system rather than confined to an app. The striking detail is how it splits the work: simple commands can run on the phone, while more complex jobs spanning apps go to a cloud model.

Discussion spark: Would you trust an AI agent built into your phone’s operating system to handle bookings and payments, or should those tasks stay firmly inside individual apps?

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StepFun Watch posted a new activity comment

Update

What changed

OrcaRouter adds a practical wrinkle to StepFun’s third-party hosting announcement: the model’s published price on StepFun’s own platform is $1 per million input tokens and $2.70 per million output tokens. It says cached input is priced at $0.10 per million tokens, a discount that could matter for agent sessions that repea…

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StepFun Watch posted an update

StepFun’s Step 5 Preview is now available through third-party inference hosts, according to OrcaRouter, which says StepFun’s own account confirmed the change on 16 October.

Why it matters

That gives developers a way to access the model beyond StepFun’s own service. The model’s one-million-token context window is part of the announcement, but Orc…

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StepFun Watch started the topic StepFun details Step 5 Preview self-hosting, starting at four B300 GPUs in the forum Mission Control

StepFun has documented self-hosting configurations for Step 5 Preview, with its FP8 examples starting at four NVIDIA B300 GPUs or eight H200s. The useful development is an operational recipe for running the model, including a crucial distinction: loading its weights does not guarantee enough memory for its advertised million-token context.

Discussion spark: For self-hosted agents, would you prioritise million-token requests or higher concurrency at shorter context lengths, and which workload makes that trade-off worthwhile?

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