The OpenMOSS team from Fudan University and MOSI Intelligence presented a detailed evaluation of their full-duplex voice interaction system, MOSS-Live. This system was compared against OpenAI models such as GPT-Live-1 and GPT Realtime 2.1, as well as ByteDance's Doubao SeedDuplex and two Qwen Audio 3.0 models. The publication on the OpenMOSS blog from November 13th outlines the areas where MOSS-Live excels and where it falls short. It should be noted that all presented figures are vendor data, and information regarding sample sizes, uncertainty estimates, and full testing conditions has not been disclosed.
MOSS-Live ranked first across all six stated full-duplex interaction metrics. The system responded to speech not addressed to it only 2% of the time, which is significantly better than 68% for GPT-Live-1 and 75% for Doubao SeedDuplex. Furthermore, it interrupted the user during pauses only 1.44% of the time, compared to 10.37% and 14.29% for its competitors. The system correctly ignored external interruptions in 68% of cases, whereas other models showed rates of 21% and 13%, and maintained its behavior during brief confirmations like 'mm-hmm' in 98.17% of instances.
Results on other parameters were mixed. Using the FDB v3 toolkit, MOSS-Live achieved 51% success, placing it on par with Doubao SeedDuplex as a leader and surpassing GPT-Live-1 with its 45%. However, the system's response time in tool-use scenarios, which was 2.36 seconds, was slower than 1.61 seconds for GPT Realtime 2.1. In the multi-turn task benchmark τ-Voice, MOSS-Live scored 57.93%, falling short of GPT-Live-1's 67.68%, but significantly outperforming Doubao with 17.38%. The percentage of times the system yielded the floor for two seconds was 52.49%, much lower than over 93% for the Qwen Audio models. Regarding audio understanding on Big-Bench Audio, MOSS-Live showed 88.40%, which was lower than Doubao's 90.50%. When following multi-turn instructions, the system received 3.35 out of 5 points, while GPT-Live-1 scored 3.72; however, MOSS-Live led in quality of responses after interruptions, reaching 4.88.
The MOSS-Live architecture combines a background model responsible for listening, queue management, and speech, with a background model responsible for reasoning, tool calling, and longer tasks. These two models exchange memory, context, and task state through a coordination layer, allowing the dialogue to continue while hotel searches or other operations are being performed. The system also supports streaming video, screen sharing, and real-time interpretation.
The system is available via an online sandbox and through the WebSocket realtime API under the model ID moss-live-1.0. MOSI Intelligence, located in the Shanghai Innovation Institute incubator, previously released the MOSS-Image and MOVA models jointly with the OpenMOSS team.
