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signal量子位（公众号）2026-09-29

Domestic Open-Source Model 2-3x Faster Than Jev Arrives: Intern-Decision Achieves SOTA in Multimodal Decision-Making

Intern-Decision, an open-source multimodal decision-making model from Shanghai AI Laboratory, outperforms Jev with 2-3x faster inference, achieving a 30 decisions per second rate on 0.8B and 2B versions. After temperature calibration, its Brier score drops from 0.628 to 0.550 and ECE from 0.213 to 0.089, both surpassing Jev. The model supports visual inputs, game control, CAPTCHA solving, and browser operations, with weights and code open-sourced on Hugging Face and GitHub.

for who
AI researchers and developers building real-time decision systems, especially those exploring multimodal understanding and large-small model collaboration.
why now
New open source models Intern-Decision beat Jev by 2-3x speed, released September 28, 2026.
what changes
Deployers can now use a faster, open-source alternative to Jev, achieving near-real-time decisions on modest hardware while optionally delegating uncertain cases to a larger model for higher accuracy.
to do
Access the Intern-Decision model weights and code on Hugging Face and GitHub, test them in your own decision-making workflows, and provide feedback to the team.
key points
  • Intern-Decision is 2-3x faster than Jev, with 0.8B and 2B models hitting 30 inferences per second
  • After calibration, Brier drops from 0.628 to 0.550, ECE from 0.213 to 0.089, outperforming Jev
  • On JevBench-hard, standalone accuracy is 73.87% and 87.39% with Atria collaboration, saving 53.3% time
#multimodal decision-making#open-source model#real-time inference#large-small model collaboration
score
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