Handoff does not rely on official APIs. For every task, it spins up a dedicated virtual computer — complete with its own browser, file system, and terminal — and interacts with websites the same way a human would . It interprets page structure and visual cues to decide where to click, what to type, and when to scroll. This allows it to operate on sites that offer no public automation endpoints, including Target, Walmart, OpenTable, DoorDash, United, Delta, and LinkedIn .
According to Hark, Handoff handles a wide range of multi-step online workflows :
Hark deliberately pivoted away from the standard LLM paradigm. Instead of predicting the next word in a text sequence, Handoff's model is architected to predict the next graphical user interface action — a click, a scroll, or a keyboard input at a specific screen location . The current release uses a post-trained model (fine-tuned on an existing base), with plans to transition to pre-training from scratch later in 2026 . Hark says this approach allows it to iterate more quickly on data pipelines and training methods before committing to a full pre-training run .
Hark claims Handoff achieved the top score to date on the Online-Mind2Web (OM2W) benchmark, a human-evaluated industry standard for web-browsing AI performance . The reported scores are:
Pricing is listed at $0.18 per million input tokens and $2.37 per million output tokens — less than one-tenth the cost of GPT-5.5 ($5 input / $30 output) . Hark claims Handoff is both faster and dramatically cheaper than frontier models from OpenAI, Anthropic, and Google for web-use tasks . These are company claims on a third-party benchmark; independent verification of real-world task completion rates and cost comparisons is not yet available.
As of August 2026, Handoff is in a closed research preview phase. Users can sign up for a waitlist at Hark's website. A full public release is expected by end of summer 2026 . The tech stack is still evolving: the model is post-trained for now, with pre-training planned before year-end .
Handoff does not rely on official APIs. For every task, it spins up a dedicated virtual computer — complete with its own browser, file system, and terminal — and interacts with websites the same way a human would . It interprets page structure and visual cues to decide where to click, what to type, and when to scroll. This allows it to operate on sites that offer no public automation endpoints, including Target, Walmart, OpenTable, DoorDash, United, Delta, and LinkedIn .
According to Hark, Handoff handles a wide range of multi-step online workflows :
Hark deliberately pivoted away from the standard LLM paradigm. Instead of predicting the next word in a text sequence, Handoff's model is architected to predict the next graphical user interface action — a click, a scroll, or a keyboard input at a specific screen location . The current release uses a post-trained model (fine-tuned on an existing base), with plans to transition to pre-training from scratch later in 2026 . Hark says this approach allows it to iterate more quickly on data pipelines and training methods before committing to a full pre-training run .
Hark claims Handoff achieved the top score to date on the Online-Mind2Web (OM2W) benchmark, a human-evaluated industry standard for web-browsing AI performance . The reported scores are:
Pricing is listed at $0.18 per million input tokens and $2.37 per million output tokens — less than one-tenth the cost of GPT-5.5 ($5 input / $30 output) . Hark claims Handoff is both faster and dramatically cheaper than frontier models from OpenAI, Anthropic, and Google for web-use tasks . These are company claims on a third-party benchmark; independent verification of real-world task completion rates and cost comparisons is not yet available.
As of August 2026, Handoff is in a closed research preview phase. Users can sign up for a waitlist at Hark's website. A full public release is expected by end of summer 2026 . The tech stack is still evolving: the model is post-trained for now, with pre-training planned before year-end .